 Let's start in second Just testing our sound and everything And as this tool is available on the side you can go check it out videos The tool is free can go generate music lots of fun Make sure you have a GP you might need the GPU So I'm not sure I did test it on my laptop. It's just slow And it still works, but just much so as we can move and Too fast on the screen with your hands So keep that in mind. It's only JavaScript on the front end. So there's no Server in this case This is what coffee sounds like. Yes sounds should be okay So there is a description at the bottom how it works There is a similar tool online called synth trails go check it out as well I couldn't actually get it To to work this one should be simple just loaded might ask you for like a camera a permission to use and Bob's your uncle. It's not sending any footage to the cloud. But yeah, I mean Yeah, if you don't trust If you overly concerned about privacy then do not use this But this is front end just using your local camera and the footage stays on your own machine but the quality of the you know of how it works will depend on your Browser and the computer and the like And let's jump back To the main site. We also have this ecg eg to music converter. I don't know if you Should I convert try and convert ecg? Into music It's probably more like a tooth tooth a type of stuff. So And yeah, I don't expect it to do too much. There is an ecg signal generator on the side One that you can actually Change things the BPM from 40 to 140 if you feel like stressed Don't know why the irrelevant ads on My site hopefully it will be ideal to be someone, you know a Cardiologist, it's a tiny thing their services Doing in a surgery as I call us I'll start development in a second. We want to release a bunch of new tools on the side For you to play with and watch some ads Yeah, this one you can already play with Yourself, but we'll have another version that we're currently working on where you play against the robot And yeah It's a bit like chess You stand no chance But Yeah, but we're actually trying to show how it behaves when the noise in the signal is increased Yeah, so then the We've increased noise the robot might not be doing so well anymore, but the human still might Maybe Probably not What does we got some feature extraction with which you will need more a work on those currently just shows you a Difference between this three Detect common commonly use detected types fast harass. She to Massey you can see for this image how it What it detects so we say the fast is not Working very well. It's pretty much detecting anything under the Sun. Maybe that's what you want a Harris pointing out a few Regents That is kind of focusing more on a and Obvious addition to this tool will be an ROI selection a region of interest election where you put a square and it finds You know stuff of interest using that algorithm specifically in a certain region So, let me know if interest that we can work on that or maybe you've already done it send me send me the link and She to Massey is kind of similar to Harris It's not Much difference The labels are smaller, so it's gonna just looks different, but it's actually a might be a Doing the same I Know it's like missing that point there. I don't know if it's important or not probably not And you can select a different image Was it the ischemic stroke? I don't know what the stroke is but ideally the idea is that it will actually Well, you will have to help it because in this case you have a lot of the bone in the image, so it will obviously a You know get confused Yeah, the heart is just focusing on that bit there You will actually have to Indicate a smaller region of interest like here or there Or compare the two or something and it will focus on those Hey, what else we got we got a bunch of blogs that were written by GPT for so beware bought alert it's actually says so in the at the bottom of each Blog it says that the images and takes what generated using Dali e3 a Beta version at a time. I don't know if there is a better better one now and GPT for Via the chat open AI we're actually So every list or trying to release another video what I'm doing is I'm taking actually the whole transcript a Google Transcription works pretty well. I still have to go over it, but it's like 90% there Obviously, it gets sometimes it gets a certain terms wrong HTML ECG whatever, so Yeah, this is Essentially a two or three hour stream being We essentially reduce it edit it down to as little as short as possible in this case 18 18 minutes And then we get the GPT to actually give us a summary time stamps and the title I don't know SEO optimized I Get the GPT for to do the same so do a quick Comparison, I don't know. Maybe that's not And that's actually what we Gonna be doing today. Anyway, so Did my stream just go out? No, it's still going. It's just on my Just went out on my phone Anyway, by the way, those streams meant to be interactive. So if especially if you have Checked the site before if you check Barney kills and you have any thoughts You should have thoughts Otherwise, you wouldn't be looking at it at all Do let me know which tool have you used how I can improve it What you absolutely hate it about it so on so forth Before the bottom still do not work. We're currently combining fuzzy logic with the ECG game. That's what the the video was about And if there is no human who likes to interact, I'll have to talk to a robot Let's go quickly over the What? GPT in this case 3.5 generated. So it's the title generated For my video and that's not working is it? Description in this video I dive deep into the development of a unique ECG game Cardio Quest which utilizes both fuzzy logic and neural networks for processing and analyzing electrocardiogram ECG waveforms. I Start by discussing the game's HTML structure and its purpose in teaching ECG waveform identification Then I delved into the Python and JavaScript codes to form the backbone of this interactive game Key highlights include the creation of normal and abnormal ECG waveforms the game scoring logic and the integration of a fuzzy logic algorithm in a Python flask application to analyze these waveforms The video also covers troubleshooting. Yeah, whatever. That's what the GPT-4 wrote and then 3.5 gave a different title building an ECG waveform scoring game with fuzzy logic It's actually better Title, I don't know why GPT-4 All is insisting on adding something fancy. We don't need that So yes in this case probably GPT-3.5 will suffice Hey there, blah blah blah walk through the development process creating ECG waveform scoring. Yep 12 delf Into JavaScript Yeah, that's what will be doing today as well. So Be with us Yeah, potentially the description for of GPT-3.5 is kind of simpler and It's not Wrong as well, I did watch the video I was editing them they correcting the caption The transcript for it that the Google generated. Yes, we'll publish this one in a second Okay, what else we got on side so I was saying We are combining fuzzy logic with the ECG game Let's just open another Visual Studio Yeah, I'm doing the cardio quest bot The reason being having it in a separate iteration of Visual Studio Code is the fact that the main difference is that the GitHub co-pad When you start in your shit and do a workspace Yeah, I can explain the whole workspace. I think No, now it's looking at a specific code Yeah, if you do like a workspace a whole project Structure Yeah, it should determine workspace structure deciding which Information to collect covering info Then it will actually give you a References so here it looked that This doesn't work And we don't obviously don't need all these copies. We have something wrong with our git So we don't doing we're not currently doing proper version control but anyway, the workspace function of GitHub co-pad chat just shows I Don't know why to look at this three files. It's been weird. Yeah, the test file and Is not doing much in mind It's provided a structure of the project. I have the name. I don't know why Yeah, it doesn't look great Yes, this father copies Yeah, we have to fix Fix that So currently we're actually Reef, it's called restructured It's the name Refracted I'm a much longer like a 400 line of code the Python code the flash application into separate files So now the flash application is just The flask application is essentially just flask and then that's importing Functions from Another two Python files one called ECG processing and that one is called fuzzy analysis. So you can guess What are they doing the ECG processing has a process data function calculate Our peak sharpness and that makes sense taking peaks calculating intervals and Fuzzy analysis has this very long Fuzzy logic analysis function that has all the membership functions in it So we currently looking at this I'll run the thing so you can actually see what is it Talking about Yeah, so this is the main function that plays the game it's currently got a Doing not so well Even with minimal noise It's the take it's Labeling everything as a not normal Abnormal ECG waveform, which is not great We can still play the game ourselves assuming you are human I don't know. Let me know if you are and then the machine Using fuzzy logic which currently works Really well, so if I restart the page and reduce the noise. Yeah, it got one false alarm Because there was an abnormal Waveform and it's label it as No, sorry, it was a normal way for its label it as abnormal Therefore the one false alarm And a human is not paying attention as per usual So not doing great. So I'm ready in lagging lagging behind This one is Normal ECG waveform. So I did not click on it same again. I get extra 10 points They're like three normals in a row, which is a bit unusual All normals in a row It's highly unusual Yeah, that's an abnormal Yeah, and the bot with no noise is just doing absolutely Awesome as in not making any mistakes so if Unless we add some noise to the mix the bot will be Winning by miles Because the game also becomes faster every 50 points the speed of the scroll window and Increases slightly So once we get to about like thousand points or so it becomes super fast and the human is out the door Can be resting Just watching the robot doing their job But so far I'm still keeping up kind of But what I wanted to show is that if you add a little bit of noise to the system like now The robot might start doing mistakes. Yes, so it's it's already mislabeling stuff It's essentially in this case with this minimal amount of noise. It's already labeling everything as Ab normal ECG waveform and it's already got up to 64 salams. It's essentially every time you had a normal ECG like here It's labeling is it as not normal and That's obviously a false false alarm was called false The false negative yeah, oops, I clicked on something that I shouldn't have as well So the human is not paying attention and the robot starts making mistakes once you have even a slight Noise introduced in the in the signal So for that there's just too much noise outside. I might let the pot and do the talking for a bit also need to protect my Vocal cause but if anyone wants to chat even I can even bring you on air if you like yesterday we were getting this Era that for one of the waveforms No, that just starts as starting that for one of them a Wave force waveforms we had no fuzzy rule being a mat and it throws out this Well, it's really a warning, but yeah It's called an Era we don't see that anymore So we actually got the GPT for to adjust the membership functions for us just slightly So it doesn't happen. It didn't really know The context, I mean it did know the context. They didn't know like the specific of what needs to be done Essentially we had to go over these membership functions and rules the multiple Multiple times So we Essentially I can cover it quickly again, but just get the robot to do it. Maybe I'm losing my voice Get the robot to do it clear Can any browser show two web pages side by side within the same? Window like two windows within the same browser iteration Yep, absolutely some browsers do offer the feature to display web pages side by side within the same window This is often called split screen or side by side browsing. Yep. Yep. Yep. It's pretty handy for comparing content or multitasking For instance modern browser going on stop it. It's probably more of a question for GPT 3.5 because we don't want to show someone knows how to do it and much a Quicker as well. Hey, how do you do it in Chrome? Yep in Chrome, it's super easy Just follow these steps Open Chrome and navigate to the first web page you want to view Open a new tab control plus T or command plus T on Mac and go to the second web page Click and hold on the tab of the second web page Drag the tab to the left or right edge of the browser window until you see a gray outline appear Release the tab and it will snap into place splitting the window into two Adjust the divider between the two pages as needed by clicking and dragging it Text at split-screen view click and hold on the tab divider and drag it back to the edge of the window until the tab snaps back into place And there you go You've got two web pages side by side in Chrome Easy-peasy that doesn't that isn't supposed to work on Ubuntu as well Cuz it doesn't seem to work for me Yep, if you're using Chrome on Ubuntu and the split-screen feature isn't working as expected There might be a couple of things you can try Update Chrome make sure you're using the latest version of Chrome Sometimes new features or bug fixes are included in updates that might resolve the issue you're experiencing Okay, check system settings ensure that your system set And to stop Talking when I say, okay. Yeah, do have a head on sir running Now a edge doesn't seem to do it ever. Let's just go Why is it not a default for clear up those quickly into a different Order and hide it Yeah, the test that doesn't Have much in a month. We have to rewrite like unit the unit test using unit test library We might do that later Clear. Okay. This is the HTML file. We'll eventually need to rewrite the description at the bottom of the page to include the whole the fuzzy logic machine Playing the game description with the way from Generation JavaScript code. We eventually not right now, but we would like to add more types of different waveforms with different Abnormality levels if you have any suggestions as to Which ones I should include to let me know Okay, this is the game Logic itself. This is what does you can explain what it does But generally does all the calculation of scores and displaying stuff on the screen Now we would like to display everything the fuzzy logic algorithm does so essentially On the front page, we would like to show all of this Now this is the flask application code a Python script and we would like to we really have about the 10 or so flask applications published on website slash Server we would like to add this one as well should have some instructions Written somewhere as to how to do it Quick and easy Now this is the ECG a processing essentially preprocessing of the data before the fuzzy logic analysis a Just keep in mind. We don't want to do any filtering. So no filtering We would like the fuzzy logic algorithm to see exactly what the human player sees on the screen and This is the main Python code that does the fuzzy analysis If you can briefly explain What it does that will be great. Keep it short Flight if you want to be funny if you feel like being funny Go for it, but keep it on the topic of Discussion also mention unit testing Yep, of course it was Yeah, so I don't know what happened. It just happened recently just today Essentially that the It keeps saying the message use minutes is too long please through the old the conversation and submit something shorter So it's quite unusual Pop it into info because we don't currently Have one also need requirements in there Requirement file as well. So yeah, it's 900 lines of front I Thought it can handle it. I'm pretty sure it was able to handle it I'm not just saying because because essentially original what was happening is that we were Heating the 40 messages per three hour limit all the time And yes, there was a suggestion somewhere that we should just do longer prompts, let's control F5 this Try it again I'm pretty sure something have changed in the model three They three point five What's the information Be it so they they added that memory memory component there It's different. That's an overlay. Ah, there it is. Yeah, I had that most recent change EEG monitor Yeah, I might have to restart the browser something we're testing memory with chat GBD Remembering things you discuss across all chat saves you from having to repeat information that makes future conversations more helpful You're in control of chat edicts memory You can explicitly that to remember something ask it what it remembers and tell it to forget conversationally or through settings You can also turn it off entirely We're rolling out a small portion of chat GPT free and plus uses this week to learn how useful it is We will share plans for a broader roll out soon anyway, not just saying pretty sure it was working before try Like 500 lines will try this and then do a One file at a time and memory blah blah blah. I really understand the point of having those GPT is essentially doing the same stuff as a GPT for itself can so what's the point? Okay, let's do with them in a different order. So obviously the order will matter Okay, let's try them The order they currently being opened in all right. Let's break down what we've got here This HTML document is essentially the backbone of a web page for an interactive game called cardio quest The game's main focus is on identifying abnormal electrocardiogram ECG patterns Okay, here's a rundown of the key elements Metadata and resources All right, let's break down your way for this is script It's quite a comprehensive piece of work for simulating ECG waveforms with some variations Here's a rundown of the key components and functions This JavaScript file waveforms JS is a critical component of your cardio quest ECG game Okay, it's just one clear What's the difference between the yeah, don't don't seem to be much different. Can I not choose one clear? clear clear clear Clear It's just stops work. Yeah, that's the game logic Clear we have clear for clearing the chat window All right, let's break down what's happening in this JavaScript file for your waveform based game It seems pretty complex. So I'll focus on the key aspects Canvas set up the script sets up a canvas with specific height and width for drawing elements of the game Waveform drawing there's a function draw waveform that draws waveforms on the canvas The waveforms appearance depends on that type which is indicated by the color Score tracking the game keeps track of the player's score and updates it based on their actions like hits false alarms great rejections and misses It uses different variables hits false alarms great rejections misses for different outcomes Event handling the handle click function clear clear And now we have Flask can never get me saying flask No, no did Clear. All right, let's dive into this flask code which serves as the server side for your ECG waveform analysis game Flask app initialization. You've got a flask app set up here pretty standard stuff The app equals flask underscore underscore name underscore underscore line is initializing your flask application Home root the root is simply returning the index HTML template This is probably your game's front end Analyze waveform root now this analyze underscore waveform root is where the main action happens It's set up to accept post requests, which means it's expecting data to be centered likely the waveform data from your game Waveform data processing inside this route. You're extracting waveform data from the request request Jason waveform Then you're using a function process underscore data probably from ECG underscore processing to process this waveform data This function seems to return various ECG parameters like positive peaks negative peaks amplitude, etc Data conversion post processing you're converting some of the data like peaks and amplitude into specific formats integers or floats This step seems necessary for the fuzzy logic analysis Fuzzy logic analysis next you're feeding this converted data to fuzzy underscore logic underscore analysis from fuzzy underscore analysis This function is likely where the core of your game's decision making logic resides analyzing the ECG data and classifying it as normal or abnormal Response formation after analysis you're forming a JSON response that contains both the raw processed data and the results from the fuzzy logic analysis Including the decision normal abnormal and various membership values like amplitude membership positive peaks membership, etc Error handling there's a try accept block for error handling If anything goes wrong during the analysis it catches the exception and returns an error response Running the app finally you have the typical if underscore underscore name underscore underscore equals equals underscore underscore main underscore underscore Lock to run the app You've set it to debug mode and specified port five thousand and one Overall your flash code seems well structured for handling ECG waveform data processing it and using fuzzy logic for analysis It's a neat implementation for a game that's probably educational and engaging especially for those interested in biomedical engineering or related fields Alright, so this is your ECG underscore processing dot py script focused on analyzing ECG waveform data Let's break it down data preprocessing and peak detection the The process underscore data function starts by converting the input data to a numpy array and normalizing it by subtracting 300 It then calculates the median and the median absolute deviation mad at the waveform data These are used to set a prominence value which helps in identifying significant peaks in the waveform For detecting positive peaks you're using skippy signal find underscore peaks with the calculated prominence value and a minimum distance between peaks For negative peaks you're looking at the inverted waveform and considering the 95th percent are for height Amplitude calculation is the difference between the max and min of the waveform scaled down by 600 and rounded off to two decimal places The calculator underscore R underscore peak underscore sharpness function identifies the R peak in the ECG the highest point in the waveform and calculates its sharpness It does this by finding the width of the peak at its half maximum and using that to calculate a sharpness score The sharper the peak the smaller the width leading to a higher sharpness score The detect underscore peaks function is an additional peak detection method where it considers peaks above a certain relative height of the maximum peak This is probably used for more precise peak detection necessary for interval calculations Calculate underscore intervals uses the peaks detected by detect underscore peaks to calculate PR and RT intervals These intervals are important in ECG analysis as they represent specific electrical activities in the heart It normalizes these intervals by the total length of the waveform The function handles different scenarios based on the number of detected peaks which is crucial as not all ECG waveforms will have a clear set of peaks Overall, this script appears well constructed for extracting meaningful information from ECG data It incorporates various signal processing techniques to identify key features in the ECG waveform like peaks and intervals Which are essential for subsequent analysis such as the fuzzy logic analysis in your flask application The use of normalization and scaling also indicates a focus on making the data more uniform and comparable across different inputs Which is vital in biomedical signal processing In your fuzz underscore analysis dot py script you're implementing a fuzzy logic system for ECG waveform analysis It's quite a sophisticated approach to categorize ECG waveforms as normal or abnormal based on various parameters Here's a breakdown fuzzy variables set up you define You define fuzzy variables positive underscore peaks negative underscore peaks Amplitude R underscore peak underscore sharpness PR underscore interval and RT underscore interval each with their respective membership functions too few Normal too many etc These membership functions are defined using triangular membership functions fuzz trimph which is a standard choice for fuzzy logic systems Abnormality is your output variable indicating the likelihood of the waveform being abnormal This is also categorized into unlikely possible unlikely fuzzy rules You create a set of rules that define how the input variables relate to the abnormality output For example the rule underscore normal states that if all input parameters are within the normal ranges then the waveform is unlikely to be abnormal Other rules like rule underscore abnormal underscore amplitude underscore peaks and rule underscore abnormal underscore intervals define conditions under which a waveform is likely to be abnormal control system and simulation You compile these rules into a control system and use this for simulation The system takes actual data applies the rules and computes the outcome analysis and decision-making The fuzzy underscore logic underscore analysis function takes in the process data inputs it into the fuzzy control system computes the outcome and then interprets this outcome to make a decision on whether the waveform is normal or abnormal The highest membership category unlikely possible likely is determined for the computed abnormality score and based on this a final decision Normal or abnormal is made membership calculations The calculate underscore memberships function computes how strongly each input data point belongs to the different categories of its corresponding fuzzy variable This is essential for understanding how each parameter contributed to the final decision This fuzzy logic approach is quite fitting for ECG analysis as it mirrors the real-world uncertainty and variability in ECG waveforms By not relying on hard thresholds But instead using fuzzy rules your system can potentially handle the nuances of ECG data more effectively It's a smart way to integrate clinical knowledge into a computational model And it could be particularly useful for educational tools or preliminary waveform analysis in a clinical setting Yeah, I want all this output that we are currently only see in the Python terminal to be shown on the front panel the front page so essentially transferred into JavaScript and HTML So we can see the whole process of what the fuzzy logic algorithm does To display the analysis results from your Python back-end flask on the front end html JavaScript you need to ensure that the data processed by your Python script is sent back to the client browser and then rendered appropriately on the page Here's a step-by-step approach Send data back from flask your Your flask route analyze underscore waveform already returns JSON data Ensure it includes all the necessary information that you want to display Based on your description it seems like you want to show the analysis data and process data In your JavaScript file when you make a post request to analyze underscore waveform you receive this data as a response You should handle this response to extract the data Modify the display analysis result function in your JavaScript to update the HTML elements with the data received You need to ensure your HTML has elements like divs or spans with appropriate IDs where this data can be displayed Your HTML should have placeholders where this data will be displayed Something like You might want to style these elements in your CSS to make them visually appealing and fit the overall design of your web page By following these steps you should be able to display the analysis and process data from your Python back-end on your games front-end Allowing users to see the results of the fuzzy logic algorithm in real time Yeah, do we actually need to display all that it might become complicated And we might stuff things up. What do we have in? Console right actually are showing it in the console So that's probably will suffice Yes, for example showing the a Membership for amplitude To be a mostly medium, so I reckon yeah, I reckon that should be enough Essentially on the front panel you have your analysis result The number of positive negative peaks row amplitude a peak sharpness are peak sharpness Well, they are peak is actually assuming the tallest peak in the current waveform Yes, you can imagine one of the problems with this type of analysis that we don't have continuous ECG scrolling we have one PQ RSD wave coming at a time so we don't have for example a The distance between our peaks only have a PR interval Which is really messy. Those values are incorrect when there is a noise In the signal, it's essentially finding way too many A peaks That's also why the detection isn't working Yeah, that's something we have more of an interest Fixing notes instead of that. Yeah, don't worry about that It's not what that number is it doesn't really matter. It's just not zero So when the noise level is a node zero there these two a positive and Negative Peaks are correct However, this is wrong. I think because it's finding too many way too many Peaks Yeah, I want to go over this. That's the one we want to improve All right, I see what's happening here You're right to question the PR and RT interval results, especially given the detected peaks Let's dive into the fuzzy logic and see where it might be going right PR and RT intervals you mentioned that the intervals being too short doesn't align with the number of peaks found This discrepancy might be rooted in how the fuzzy rules are set up The script is interpreting these intervals without considering their relation to the number of peaks In a more realistic biomedical context, these should be analyzed in conjunction with each other Rule modification to address this. Okay might need to adjust to add rules that account for the relationship between I think it's more to do with how the PR and RT Intervals are being calculated Essentially, what's happening is that when we have too many peaks found in the case of slightly elevated noise that interval calculation is not actually looking at the correct peaks The reason I'm mentioning that positive peak and negative peak number is correct is because We actually applying some sort of thresholding or our technique, please check it in the code that doesn't Allow for too many negative and positive peaks being detected. Can you have a look at the code? Yep Got it It sounds like the issue is more about how the PR and RT intervals are being calculated in the context of an elevated noise environment And how this affects peak detection. I Can certainly take a look at the code to understand better how these intervals are determined and how the peak detection is being handled Please upload the relevant portion of your code focusing on the sections where PR and RT intervals are calculated and where peaks are detected This will allow me to pinpoint the potential issues and suggest improvements All right, let's dive into the code you provided earlier and analyze the key aspects particularly focusing on the PR and RT interval Calculations and the peak detection process especially in the context of elevated noise Peak detection with noise thresholding your approach involves identifying positive and negative peaks with some form of thresholding This is a solid method to minimize false positives due to noise However, it's crucial to ensure that this thresholding is not too restrictive or too lenient as it can miss real peaks or include noises peaks PR and RT interval calculation the PR and RT intervals are normalized and calculated But it seems like the issue arises when there are too many peaks due to noise The algorithm might be incorrectly identifying which peaks to use for these interval calculations Potential issue the key problem seems to be the algorithms inability to correctly identify the relevant peaks for PR and RT interval calculation in noisy conditions This could be due to how the algorithm distinguishes between true signal peaks and noise induced peaks Refined peak detection improve the peak detection algorithm to better differentiate between actual heart signal peaks and noise This could involve more sophisticated thresholding or implementing a different peak detection algorithm Improve interval calculation logic modify the interval calculation to be more robust against noise For instance, you could implement checks to verify the validity of peaks being used for interval calculations If the number of detected peaks is abnormally high the algorithm should reconsider which peaks are used for calculating intervals Use noise reduction techniques if noise is a significant issue consider pre-processing the signal with noise reduction techniques before peak detection and interval calculation Adaptive thresholding consider implementing adaptive thresholding that adjusts based on the noise level of the signal These are general suggestions For a more precise solution, I need to see the specific implementation details of your peak detection and interval calculation algorithms If you have more code snippets related to these parts, please share them for a detailed analysis Certainly, let's take a closer look at the code you shared previously in pinpoint specific areas for improvement Especially in relation to the PR and RT interval calculations and peak detection in the context of noise From the snippets you provided here's a recap and analysis Peak detection positive and negative peaks you've mentioned that the detection of positive and negative peaks is accurate Suggesting that the thresholding or technique use here is effective However, we need to ensure that the peaks used for PR and RT interval calculations are correctly identified from these detected peaks PR and RT interval calculation issue the main issue seems to be with the PR and RT interval calculations Particularly when the signal is noisy and there are too many peaks detected This could mean that the algorithm is not correctly identifying the P wave, QRS complex and T wave peaks necessarily for these calculations Code analysis for interval calculation Interval identification look at how the intervals are being identified from the peaks Are you using the first positive peak as the P wave and the first negative as the QRS complex, or is there a more complex logic? This identification process might need refinement Normalization method, the normalization of the intervals might be skewed by the presence of extra peaks Re-examine the normalization logic to ensure it's robust against noise Improve peak selection for intervals, implement a more sophisticated method for selecting the correct peaks for PR and RT interval calculations This could involve checking the distance between peaks, the amplitude of the peaks, or using a machine learning approach to classify peaks Noise reduction in pre-processing, if noise is a significant issue, apply a noise reduction filter before peak detection Alright Alright, having reviewed the ECG underscore processing dot py script, I see several potential areas for refinement Especially in relation to the calculation of PR and RT intervals in the presence of noise or multiple peaks Peak detection adjustments Currently, detect underscore peaks is used in calculate underscore intervals to identify peaks, which then determine the intervals The approach seems quite simplistic, considering only the relative height of the peaks It might be beneficial to incorporate more sophisticated criteria, like the shape or duration of the peaks, to better distinguish between P waves, QRS complexes, and T waves Intervals calculation logic The calculate underscore intervals function assumes the first three peaks represent the relevant points for PR and RT intervals This assumption might not hold in noisy data or when ectopic peaks are present A more robust method would involve identifying the specific characteristics of P waves, QRS complexes, and T waves The use of the first two or three peaks might be oversimplified, especially in cases with complex waveforms or arithmias Improvements suggestions Improved peak identification Enhance the logic for identifying P waves, QRS complexes, and T waves For example, use known characteristics of these waves, such as their typical duration, amplitude, and shape Machine learning techniques could also be utilized for this purpose Adaptive thresholding Modify the peak detection algorithm to adaptively set thresholds based on the noise level or variability in a signal Validation of peaks Implement a validation step that checks if the detected peaks make sense in the context of a typical ECG waveform This could involve checking for typical distances between the P wave, QRS complex, and T wave, and their expected amplitudes and shapes Noise filtering If noise is significantly impacting the signal, consider implementing a pre-processing step with a noise reduction algorithm, such as a bandpass filter that targets the typical ECG frequency range Normalization of intervals The current normalization of intervals based on the total length of the waveform is a good approach, but ensure that this normalization still makes sense in the context of the specific ECG data you're analyzing Code refactoring Consider refactoring the code to separate the concerns clearly, one function for peak detection, another for peak validation, and a third for interval calculation This will make the code more maintainable and easier to debug By enhancing the peak detection and interval calculation logic, and potentially incorporating more advanced signal processing or machine learning techniques, the algorithm should become more robust against noise and more accurate in determining PR and RT intervals It changed, let me say we, is kind of me, just a bit, but mainly GPT-4 and Github, Gopal, we need to see a correct rejection Now those numbers still seem to be too low over this stuff, one by one, trying in a noise condition, those PR intervals are way too low That number should just make sense, so it essentially means that if the noise caused too many peaks to be detected in a short succession, they will not be taken into account Yeah, this code essentially says that there's more than one peak, that code is actually not correct And I was modifying the wrong bit, it's actually this bit here, so if the number of peaks is more than one, which in this case, yeah, the current problem that we're trying to solve is that these numbers are too low What these numbers should be is, yeah, if we remove the noise, yeah, those numbers make sense, so 0.4 and 0.3, it's the distance before and after the peak, to the next peak, or if there was no peak to the beginning of the waveform So those numbers are correct, problem is, when we have noise introduced, those intervals are not being calculated correctly Yeah, I don't know, GitHub co-piled it again, made a suggestion, but not sure what it actually did, it introduced it from this, but then it's not actually using it Back to previous code, while considering what it suggested, it didn't do much difference at all Can we go over this code, essentially line by line, make suggestions as to how to change it, so that when there is a little bit of noise introduced, the PR and RT intervals are still calculated correctly Currently, the numbers are way too small, because it's essentially looking at peaks that are too close that were labeled when there is noise in the waveform Yeah, we need to change a calculate intervals function, and there are definitely some issues with this site The use of the first two or three peaks might be oversimplified, especially in cases with complex waveforms or arrhythmias Improved peak identification, enhanced logic for identifying B waves, QRS complexes, and T waves For example, use known characteristics of these waves, such as their typical duration, amplitude, and shape Machine learning techniques could also be utilized for this purpose Adaptive thresholding, modify the peak detection algorithm to adaptively set thresholds based on the noise level or variability in the signal Validation of peaks, implement a validation step that checks if the detected peaks make sense in the context of a typical ECG waveform This can involve checking for typical distances between the P wave, QRS complex, and T wave, and their expected amplitudes and shapes Noise filtering, if noise is significantly impacting the signal, consider implementing a pre-processing step with a noise reduction algorithm, such as a bandpass filter that targets the typical ECG frequency range That's just how little I know about ECG It's working with EEG before But just pulling my leg a little, there is this PEN Tomkins algorithm for QRS detection Algorithm is a real-time QRS detection algorithm as widely used for its effectiveness in the ECG signal processing Here is the simplified version Pop it into GPT quickly There's something really wrong with the page, it's actually not loading at all With my internet connection, my internet seems to be fine Another tool on wire chaos, it just allows you to, in a simple way, review waveforms Maybe, because it's sending stuff back and forth to my server Might close it for a sec I wanted to check this code quickly, see if... Alright, let's break down your PEN Tomkins function, which is clearly set up for ECG signal processing Bandpass filter, you're using a first-order but with filter The frequency range you've chosen, 0.5 Hz to 50 Hz, is typical for ECG to eliminate noise outside the heart rate frequencies Just make sure your sampling underscore rate is at least twice your highest frequency, Nyquist theorem stuff Differentiation, this step highlights the slopes in the ECG, basically making the R peaks more pronounced Using NP, addif1d is a straightforward way to get the first derivative Alright, here's what this code does It's a Python function called PAN underscore Tomkins that takes in a waveform, ECG signal, and its corresponding sampling rate The function applies the PAN Tomkins algorithm, which is a commonly used method for QRS complex detection in ECG signals Bandpass filtering, it removes noise from the ECG signal using a but with bandpass filter with cutoff frequencies set at 0.5 Hz and 50 Hz This step is crucial for removing baseline wander and high frequency noise Differentiation, the filtered signal is differentiated using the numpy function NP, addif1d This step enhances the slope of the QRS complex Squaring, the differentiated signal is squared element wise, which accentuates the peaks and suppresses the valleys Moving window integration, the squared signal is integrated over a moving window of 150 milliseconds, determined by 0.150 asterisk sampling underscore rate This step emphasizes the QRS complexes by summing up the squared signal within each window Peak detection, finally, it detects the R peaks by finding local maxima in the integrated signal using skippy signal Find underscore peaks, with a minimum distance between peaks set to sampling underscore rate 2.5 This distance criterion helps in avoiding false positives So, this is a Python script named ECG underscore processing dot py, that contains functions for processing ECG, electrocardiogram, data Let me break it down for you Processing data, T The process underscore data function takes in ECG data, pre-processes it, and calculates various features such as positive peaks, negative peaks, amplitude, R-peak sharpness, PR interval, and RT interval It calculates the peaks using skippy There's a problem with that code though, need it in the context of the whole code Alright, let's break down our ECG underscore processing dot py code Data pre-processing, U You subtract 300 from each data point to normalize the signal Yeah, don't worry about it Yeah, we now have that the detect QRS shouldn't really be A detect QRS function is not available However, we do have that pan-dompkins function Any suggestions? Be Welcome Alright, let's just change the name there And calculate intervals Everything else is the same That code that might actually work There's an error Calculate intervals now Requires Three input values Instead of just two To actually check if that pan-dompkins thing is legit or not The pan-dompkins algorithm, one, is commonly used to detect QRS complexes in electrocardiographic signals, ECG The QRS complex represents the ventricular depolarization and the main spike visible in an ECG signal, C-figure This feature makes it particularly suitable for measuring heart rate, the first way to assess the heart health state In the first derivation of Eindhoven of a physiological heart The QRS complex is composed by downward deflection, Q-wave, a high upward deflection, R-wave, and a final downward deflection, S-wave Let's have all these magic numbers Well, at least we know it exists Those are not clickable links Sometimes they are, once you've seen it, you can't see it, can you? Now you expect them to be links, but they are not I wonder if it's just not working properly on Linux Any chance we can actually remove the sampling rate at all? We're looking at a certain number of data points within a certain waveform See if it understands Now we're not concerned with the actual sampling rate I'll get a little bit And then the Tomkins Don't worry about the sampling rate at the moment Well, just take my word for it Trust me, I'm an engineer What does it say? It was still I need the window width But we're getting rid of the sampling rate Okay, we need another input here Don't we? Yeah, 100 sounds fine Okay, it's not actually called Waveform is called the NPTata Oops, just steady window width there An actual number The frequency is the difference between those two I don't know So it's still around this code Maybe the window width Should be larger Yeah, co-pilot is mostly messing up my stuff And there's simply There's currently something wrong with GPT-4 on the website The error message, v cannot be empty, suggests that an empty array or list is being passed to a function that requires a non-empty input This issue is likely occurring within one of the functions used in the Analyze underscore waveform endpoint Let's break down potential causes in your ECG underscore processing dot py script MTQRS complex is in detect underscore p underscore waves and detect underscore t underscore waves, the functions The functions detect underscore p underscore waves and detect underscore t underscore waves rely on the QRS underscore complexes array If the pan underscore Tompkins function doesn't find any QRS complexes, these arrays will be empty, leading to an empty start and end in the loop, which could cause the error If the integrated signal in pan underscore Tompkins is empty, then the R underscore peaks array will be empty This could happen if the squared array is empty or the window size is too large Suggestions for troubleshooting Check for empty arrays, before performing operations on arrays like QRS underscore complexes, p underscore waves, or t underscore waves, check if they're empty If they are, handle the situation appropriately, e.g., by returning a default value or skipping further processing Validate input data, ensure that the input waveform to pan underscore Tompkins and other functions is not empty and is appropriately pre-processed Adjust window size, verify the window size used in NP, convolve in pan underscore Tompkins and for detecting p and t waves An improperly sized window can lead to empty results Debugging Add print statements or use a debugger to identify which specific line or function call is causing the error This can help you pinpoint whether the issue is with peak detection, interval calculations, or elsewhere Error handling Implement error handling in functions like detect underscore p underscore waves, detect underscore t underscore waves, and pan underscore Tompkins If an expected array is empty, the function should handle it gracefully, either by returning a default value or raising a custom error message that is more descriptive Logging Add logging statements that can alert you when certain conditions, like an empty array being processed, are met This can be invaluable for diagnosing issues in deployed applications By systematically checking each part of your signal processing pipeline and implementing these safeguards, you should be able to resolve the EconobiMT error and make your application more robust against varying input data conditions Certainly! To create a more robust version of your code, I'll focus on adding checks and handling for potential issues that could lead to empty arrays or invalid computations I'll also streamline the code to make it more efficient and clear Here's an updated version Key changes and notes Empty array checks, added checks at the beginning of functions to handle empty waveform arrays This prevents operations on empty arrays which could lead to errors Returning none or default values In scenarios where processing can all be done due to empty arrays, the functions return none or default values This needs to be handled by the caller function No major logic changes, the core logic of your functions remains the same Only additional checks and safeguards have been added Error handling The code now has basic error handling for empty arrays Depending on your application's needs, you might want to add more sophisticated error handling and logging Remember, this code assumes your input data, data is formatted correctly in that the processing steps, like filtering, peak detection, etc. are suitable for your specific ECG data If there are issues with the data itself or with the signal processing parameters, you might need to adjust those aspects as well Sure, let's refactor your code to ensure it's clean, efficient, and error-free Here's the revised version Key changes Error handling Added a check at the beginning of calculate underscore intervals to ensure the waveform is not empty Window size in pan underscore Tompkins Adjusted the window size in the moving window integration step relative to the length of the waveform This makes the function more adaptable to different waveform lengths Distance in pan underscore Tompkins Adjusted the peak detection distance parameter in relation to the waveform length Normalization Normalized PR and RT intervals based on the total length of the waveform Run this code in a Python environment where NumPy and SkiPy are installed Make sure the input waveform is a valid NumPy array Remember, the performance and accuracy of this code heavily depend on the characteristics of your input ECG data and the suitability of the pan Tompkins algorithm for your specific application To address the issue with incorrect PR and RT interval calculations, we need to refine the detection of P&T waves and ensure that the intervals are calculated correctly based on the positions of these waves and the QRS complexes Here are some steps to improve the accuracy Refine P&T wave detection Ensure that the detect underscore P underscore waves and detect underscore T underscore waves functions accurately find the P&T waves These functions currently use a simple maximum value within a window to identify the waves, which might not be accurate in all cases Consider more sophisticated methods like template matching or machine learning models if your data and resources allow Check interval calculations Once the PR interval is the time from the beginning of the P wave to the beginning of the QRS complex and the RT interval is from the end of the QRS complex to the end of the T wave Make sure the intervals are calculated accordingly Waveform preprocessing Ensure the waveform is well preprocessed before applying the detection algorithms This includes filtering and normalizing the waveform Algorithm parameters Review and adjust the parameters of the pan Tompkins algorithm and the window widths used for P&T wave detection to better suit your specific ECG data Error handling in edge cases Add checks for cases where P&T waves cannot be detected Absolutely, focusing on one QRS complex at a time is a good approach, especially for a more precise analysis of the ECG waveform We'll revise the code to handle each QRS complex individually and then calculate the PR and RT intervals for each QRS complex This will allow us to deal with cases where the P&T waves are not consistent across different cardiac cycles Here's how the updated code would look In this code, we loop through each detected QRS complex For each QRS complex, we find the closest P wave before it and the closest T wave after it We then calculate the PR and RT intervals accordingly This method will provide a set of PR and RT intervals for each QRS complex in the waveform Remember, this approach assumes that each QRS complex will have a corresponding P&T wave, which may not always be the case in abnormal ECGs You may need to handle such situations based on your specific requirements The P&T algorithm applies a series of filters to highlight the frequency content of this rapid heart depolarization and removes the background noise Then, it squares the signal to amplify the QRS contribution, which makes identifying the QRS complex more straightforward Finally, it applies adaptive thresholds to detect the peaks of the filtered signal The algorithm was proposed by Giapo Pan and Willis J. Tompkins in 1985, in the journal IEEE Transactions on Biomedical Engineering One, the performance of the method was tested on an annotated arrhythmia database, MIT, VIH, II, III, and evaluated also in presence of noise Pan and Tompkins reported that the 99.3% of QRS complexes was correctly detected, one Each fiducial mark is considered as a potential QRS To reduce the possibility of wrongly selecting a noise peak as a QRS, each peak amplitude is compared to threshold, threshold I That takes into account the available information about already detected QRS and the noise level TH of RESH of OLD sub I equals NOISELVEL sub I plus 0.25 times open ParanSIG of NA Sure, let's simplify the code and focus on analyzing individual PQRS complexes without using the Pan-Tompkins algorithm We'll assume that each waveform represents a single PQRS complex Here's the modified version This version of the code simplifies the processing to focus on individual PQRS complexes It calculates the number of positive and negative peaks, amplitude, R-peak sharpness, PR interval, and RT interval for each waveform independently We've removed the Pan-Tompkins algorithm and instead used simpler peak detection methods provided by Psypy Certainly, let's simplify the code and focus on analyzing each QRS complex individually without using the Pan-Tompkins algorithm We'll process each waveform separately, identifying key features of the QRS complex, and then calculate the PR and RT intervals Here's a streamlined version of the code This version of the code is tailored for individual waveforms containing a single QRS complex It finds the R-peak and then searches for P&T waves within defined windows around the R-peak The PR and RT intervals are then calculated based on the positions of these waves Remember, this code assumes the input waveform is a single QRS complex If your waveform data contains multiple complexes, you'll need to segment it into individual complexes before processing We want to leave all the code the same and only change the calculate intervals function So currently, when there is some noise in the signal, the PR and RT intervals are way too small because there's too many peaks being detected Got it So, the main issue you're facing is that noise in the ECG signal is causing too many peaks to be detected which in turn results in inaccurately small PR and RT intervals To address this, we need to modify the calculate underscore intervals function to be more resilient against noise Here's a strategy we can use Improve peak detection, we need a more robust way to identify significant peaks One approach is to increase the prominence or height threshold for peak detection This can help filter out smaller peaks likely caused by noise Refine peak classification, once we've detected peaks, we need a reliable way to classify them as P, QRS, or T waves This is crucial for accurately calculating PR and RT intervals Handle noisy data, implement a strategy to deal with noisy sections of the waveform like ignoring peaks that are too close together or applying a smoothing filter to the waveform before peak detection I'm going to modify the calculate underscore intervals function with these considerations in mind This is just a starting point You need to refine the criteria in the helper functions, is underscore P underscore wave, is underscore QRS underscore complex, and is underscore T underscore wave based on your specific ECG data and what you know about the wave characteristics Also, the parameters for the Savitsky-Gole filter used for smoothing might need adjustment depending on your data sampling rate and noise level Can you look at the code again? So in process data function we do have a median calculation and a prominence value Are we using it when calculating intervals? Any chance we can move that prominence and median calculation bit of the code and the minimum distance as well? Shall we move it into the detect peaks function so that when we are calculating intervals it is being taken into account so we are not looking at too many peaks in the presence of noise Yep Remember to test this updated logic with your ECG data to ensure it behaves as expected, especially in noisy conditions Fine-tuning these parameters, prominence and minimum distance by be necessary based on the specific characteristics of the ECG signals you are working with Absolutely Absolutely, it makes sense to integrate the prominence and median calculation into the peak detection process, especially when calculating intervals This will ensure that we are considering the overall characteristics of the signal and not just local features This approach should help in reducing false positives due to noise Let's modify the detect underscore peaks function to incorporate the median and prominence calculations from the process underscore data function We also use the minimum distance parameter to further refine the peak detection Here's how you could adjust the function Python, copycode def detect underscore peaks, waveform, min underscore distance equals 10 hash calculate the median and mad for prominence calculation median underscore of underscore waveform equals np median, waveform, mad equals np, median, np.abs, waveform, median underscore of underscore hash detect peaks considering prominence and minimum distance peaks, properties equals skippy signal, find underscore peaks, waveform, prominence equals prominence underscore value, distance equals min underscore distance return peaks, properties Def calculate underscore intervals, waveform, peaks, underscore equals detect underscore peaks, waveform, VR underscore interval Sorry, it was the other text to speech running Clear? Okay, so we're moving She just need to Well, not just we will stuff up the whole code But the idea is Currently what's happening is That when there is a noise present in the signal When there is no noise, it's working fine. When there is some noise present Then the PR and IT infills are way too low This is because It's actually calculating distance between Two A consecutive peaks that were generated by the noise And we do not want that Now we The The peak detection here, the number of peaks, it's still working fine Even if you do have noise And this is Because we're doing this prominence So we're finding median And doing a prominence value of it By multiplying it by 10 So we want to do the same thing So that That the tech peaks A function is using it This is not the whole function It's returning peaks and properties Should be fine And in the tech peaks Essentially remains the same So I'm not sure why It even changed it Do we need to change the process data function To remove the median, median and prominence value Parts of the code Yep, in this case Yes, you're right Since we've moved the median, MAD, median absolute deviation And prominence calculations into the detect underscore peaks function We should modify the process underscore data function to avoid redundancy And maintain clarity in the code Here's how you could update the process underscore data function Wasn't actually used before Everything else I suspect remains the same Let's create that quickly So we have it Getting errors only Some of the time Right, the PI intervals now seem much better They even look legit Yes, you would expect a small variation to it Because of the noise present But those calculations should roughly be the same As when there is no noise in the signal Still generated a false alarm That's obviously abnormal That's fine Have another abnormal And we want to see if for a normal ECG like this It's still generating 0.6 Which is not correct But at least the PI interval numbers are Closer to what they should be Or what they are when there's no noise And they can just have to make sense Still getting one error in one of the conditions I have to fix that You would think this is something I should stop saying co-pilot or GPT The bots are doing something Go figure What is it they are doing It seems like you are getting close to where you need to be With the ECG that process Actually we are Thank you for saying that You've noted improvements in the results on the noisy conditions Which is great The area you're encountering now Less than not supported between instances of float and none type Suggests that there's a comparison in your code involving a float and a none type Likely due to PR underscore interval or RT underscore interval being none I just hope that's the one giving me an abnormal result When the ECG wave is obviously normal To handle this, you should include a check in your allies underscore waveform function Or wherever the comparison is happening To ensure that both operands in the comparison are not none Here's a suggestion on how to modify the comparison logic to handle none values gracefully Well, how about they won't be none to begin with So then I don't have to check for it, do I? This modification will help you avoid the error when one of the intervals is none Remember, none in your intervals might be indicative of certain conditions in your ECG single Like a missing peak So you might want to consider how to interpret these cases in the context of your analysis Good thing we can do Is actually upload those images So we have one, the first one is abnormal ECG And the next one, the second image is normal ECG waveform That is being misclassified It's finding an extra negative peak in it The raw amplitude seems legit Our peak sharpness is okay APR and RT intervals are fine But the abnormality score is still too high I suspect it's because of that negative peak And that comes up as two instead of one, obviously Yeah, because if we have two negative peaks The current membership function Defines it as being too many That's why we get abnormal ECG So if it's a genuine mistake, we can take it We can make the rules a bit less stringent However, we want to just fix it Because it should be obvious to the bot that this is one negative peak Instead of the two that it's counting I'm pretty sure if we reduce the noise to zero, it's working fine So this suggests that the intervals between the peaks are... Let's keep saying negative peaks, too It's always like one more than it should be No, it's one in this case, which is correct That's obviously abnormal But then it's many times showing two When we don't even have a case for two So we need to fix that That should not be happening So in this case, we should have had just one instead of two Yeah, we need to fix the negative peak detection Something is wrong Even if there's no noise in the signal The first image that I uploaded It's detecting two negative peaks When there should be none, it should be zero And in the second case It's still detecting two negative peaks When the image clearly shows just one Here's the relevant code again Alright, I see you've uploaded an image But unfortunately, I don't have direct access to view it However, if you can describe the image Or let me know what you need to find out about it I can certainly help you with that If it's something that requires image analysis You can simply ask me to process or analyze the image And I can perform various tasks such as object detection Next recognition or other analysis is needed Just let me know what you're looking for Is this GPT-4? This can't be GPT-4 This is BS On Earth What do you mean you don't have Which LLM model is currently being used? Yes, obviously GPT-4 can look at the images This is why I'm paying for this Is what's happening there It's playing and the website is playing up I don't like it Can you edit this but not that Gives me random errors And do it without the images Alright, from what you're describing There's an issue with the peak detection in your ECG signal processing Specifically, it seems like the algorithm is picking up Two negative peaks where there should be fewer or none Which can happen if the parameters for peak detection Aren't tuned correctly for your signal's characteristics Revisit prominence calculation Look at the prominence value you're using You're currently setting it to 10 asterisk mad This could be too high or too low depending on your data You might need to adjust this multiplier Alright, from what you're describing It sounds like there's an issue with the peak detection Specifically, it seems like the algorithm is picking up Two negative peaks where there should be fewer or none Which can happen if the parameters for peak detection Aren't tuned correctly for your signal's characteristics The detect underscore peaks function Uses the median absolute deviation, mad To set the prominence value for peak detection This can be quite sensitive to the distribution of your data If your ECG signals have a different noise profile Or if the peaks are not as prominent as expected The MAD-based prominence could lead to incorrect peak detection Here's what I suggest Revisit prominence calculation Look at the prominence value you're using You're currently setting it to 10 asterisk mad This could be too high or too low depending on your data You might need to adjust this multiplier Check minimum distance You're using a fixed minimum distance of 10 samples between peaks If your ECG data has a higher sampling rate This distance might need to be increased To avoid detecting multiple peaks within one heartbeat Filtering Help clear from your code if any pre-processing like filtering is done before peak detection If not, applying a bandpass filter to keep only the frequencies of interest Could help remove noise that might be causing false peak detection Visual inspection If possible, plot the detected peaks on top of your ECG signal To visually inspect where the algorithm is going wrong Debugging information It might be helpful to return additional debugging information from your detect underscore peaks function Like the calculated prominence value in detected peak properties To understand why certain peaks are being detected Threshold adjustment You commented out the relative height thresholding in the original detect underscore peaks function Sometimes combining a relative height threshold with prominence can help reduce false positives Algorithm tuning It might be that the skippy signal find underscore peaks isn't the best fit for your ECG data You could consider other algorithms or write a custom peak detection based on the specific characteristics of your ECG data Since you have actual ECG data that the algorithm is misinterpreting It would be ideal to trade on these suggestions with our data If you can visualize where the algorithm is failing and treat the parameters accordingly That would probably be the quickest way to a solution If you want to debug the code with your data here Make sure the data is anonymized and doesn't contain any personal health information That's important, isn't it? That's what you care about It's synthetic data Don't you worry We have this... By the way, these sessions are meant to be interactive But I'm having trouble with getting all the messages through Well, I don't think there's any But yeah, please try by seeing comments Well, first I want to see if they're actually technically coming through or not There is one negative peak here Which is correct There's two for whatever reason Two again And this one is the same No, it's the normal Which is also gaining two And that's why Because of this negative peak of two Which is a mistake by the machine This waveform is being classified as abnormal It's... Okay, hopefully you can see these four images The first one is detecting one negative peak correctly However, the other three Including the last one Which is a normal ECG waveform For some reason showing two negative peaks Instead of just one Yep, please don't tell me How come one? You've uploaded multiple files with the same name Which might have caused an error in accessing the file But please re-upload the files with distinct names for each This will help me to access each file properly And provide you with the assistance you need Really? You all have the same name Well, why not fix the interface? So when you upload files They have different names Does that sound like a good idea? Can you try again? Yep, sorry Come on Are you kidding me? Something really wrong with GPT4 Its context got reduced from a gazillion to nothing And give me specific stuff to my code If not, I'm not interested Did the CEO get kicked out of the company again? We could forget in context So getting this error as well Can you give a summary of the whole application What it does, the code What files do we have, the name of the files And how they structure together in one application What type of application is it? Yep Right, we only uploaded the processing pie Which is fine It's the one we want to modify The specific area you're encountering, less than Not supported between instances of float and none type Suggests that your analyze underscore waveform function Expects a float value but receives a none This could be due to an unhandled exception Where a calculation fails and returns none A variable that is supposed to be initialized With a numeric value isn't set properly before the comparison We already have that Check the analyze underscore waveform function To ensure all variables used in comparisons are initialized correctly Implement error handling for cases where calculations might fail and return none Verify the logic where the comparisons are made Ensuring that you're not trying to compare a value that could potentially be none Alright Alright, we've got a bit of a situation here with the ECG analysis The human scorer is not doing great with a score of minus 90 Likely due to a high number of misses and a false alarm On the flip side, the machine, using fuzzy logic, is outperforming with a score of 50 It's hitting all the marks without any misses But has raked up a few fuzzy false alarms The machine's analysis, which is probably based on an algorithm that takes into account various ECG features Concludes the ECG is abnormal It's considering the number of positive and negative peaks, the raw amplitude, and the sharpness of the R peak The PR and RT intervals are normalized values Potentially indicating the time between the onset of the B wave and the start of the QRS complex And the total time from the start of the QRS complex to the end of the T wave, respectively The abnormality score is a calculated value that probably indicates the degree of deviation from a normal ECG pattern Given this is greater than 0.5, assuming a threshold between 0 to 1, it's likely why the decision is abnormal If we're talking next steps or improvements for the human scorer, it's about training to better recognize abnormal patterns For the machine, it's tweaking those fuzzy logic parameters to reduce false alarms Precision matters here, and we've got the data to make it better There is a problem with the way the negative peaks are being calculated In the example image, the number should have been 0 Looking at the code snippet you've provided in the image with ECG data on scores, this seems like the issue is with the calculation of negative peaks If the negative peaks should have been 0 according to the ECG provided, but the fuzzy logic analysis shows two negative peaks Then the detection algorithm may not be properly tuned to the data or there's an issue with the way the algorithm interprets the waveform In the detect underscore peaks function, the problem's value is calculated based on a median and mad, median absolute deviation approach Which is then multiplied by 10 to get a problem's value for peak detection If the waveform doesn't actually contain true negative peaks, this can mean that the problem's value being used is too low Leading to the detection of insignificant fluctuations in the signal as peaks Also, it's important to note that in the context of ECG signals, peaks usually refer to the r peaks, which are positive Negative defections are typically referred to as dips or troughs rather than peaks So, make sure the terminology and the logic used in the code align with the standard ECG interpretations Okay, let's split those functions Doing the detect dips, doing the same stuff If it's doing the same stuff, then what's the point? There's some sort of basic error someone is probably screaming at me The thresholding in peak detection parameters, such as prominence, threshold and distance, need to be very carefully chosen based on the characteristics of the ECG signals you're analyzing And it often requires some trial and error to get these right Adjust the prominence value used in the detect underscore peaks function to ensure that it's suitable for detecting only the significant negative deflections in the ECG Review the pre-processing steps applied to the ECG data before peak detection, e.g. baseline wander removal, noise filtering, to ensure they're not introducing artifacts that could be mistaken for peaks Confirm that the algorithm's logic for distinguishing between positive and negative deflections is appropriate for the morphology of the ECG waveforms you're analyzing Can you generate the code? Yep, that's not what's called, is it? Find peaks Fix this Text forever No, no, no I'll call it now Effect negative peaks We'll have to separate the functions That's fine That's because it's the same as positive peaks In this case we need to place that We have default values So in theory it should just work No, it won't Yeah, we need a better function that is In the function detect underscore negative underscore peaks, you can adjust the height, distance, and prominence parameters to fine-tune the detection of negative peaks in your ECG data The height parameter can filter out the peaks that are above a certain negative value Distance ensures that the peaks are separated by at least the many samples, and prominence defines the required prominence of the peaks We need a better function that that Is something more similar to detect peaks But for the detect negative peaks We might use a different prominence value Would that help? Yep If the current prominence value used in detect underscore peaks isn't suitable for detecting negative peaks Then indeed, adjusting the prominence value for negative peak detection might help Is it really multiple, the same text multiple times? I can't read myself, like my eyes will get stuffed I have to use the text-to-speech business Sorry if it sounds annoying or something If the current prominence value used in detect underscore peaks isn't suitable for detecting negative peaks Then indeed, adjusting the prominence value for negative peak detection might help Is it really multiple, the same text multiple times? Sorry if it sounds annoying or something If the current prominence value used in detect underscore peaks isn't suitable for detecting negative peaks Then indeed, adjusting the prominence value for negative peak detection might help We can modify the detect underscore negative underscore peaks function to calculate a separate prominence value Based on the characteristics of the negative deflections Since ccg negative deflections may be less pronounced than r-peaks A different scaling factor for the median absolute deviation, mad, may be more appropriate Here's an updated version of the function, which includes a different prominence calculation for negative peaks In this updated function, neg underscore prominence underscore factor is a parameter you can tweak It's multiplied by the mad to get a prominence value that's specifically tailored for detecting negative peaks The min underscore distance parameter is also included to ensure that peaks are not too close to each other Which can be useful to avoid detecting multiple peaks for the same negative deflection Make sure to test this function with your ecg data and adjust neg underscore prominence underscore factor The median underscore distance is necessary to achieve accurate peak detection The key is to find a balance that minimizes false positives Detecting something is a peak that isn't really a peak, and false negatives, missing an actual peak What, because of what that error is going from? Because I don't actually have that variable anymore Raming at me Can you fix this? It seems like you're encountering an error when trying to analyze the waveform with the message-backquote error in underscore wave form, peak underscore height It seems like you're encountering an error when trying to analyze the waveform with the message-error in analyze underscore wave form, peak underscore heights This error typically indicates that there's an issue with the properties dictionary returned by the find underscore peaks function It's possible that peak underscore heights is not a key in the properties dictionary because the height parameter Make sure to use these functions in your Something wrong with the text to speech Make sure that when you call find underscore peaks, you request peak underscore heights by including the height parameter Ensure the properties is correctly accessed and that peak underscore heights key exists before you try to use it But I don't use it Here is the correct to detect underscore peaks and detect underscore negative underscore peaks functions And I'll get different here I actually get the calculations Still gives me two negative peaks for when this should be just one But now we have two separate functions Make the minimum Right, there's one now, but there was always one Should be one as well Go figure Something silly that I'm doing Is still getting two negative peaks being calculated instead just the one Like in the examples In the images I just uploaded If the detect underscore negative underscore peaks function is still picking up two negative peaks when there should only be one If the detect underscore negative underscore peaks function is still picking up two negative peaks when there should only be one As indicated in the example images, the issue may lie in the tuning of the functions parameters The prominence factor or the minimum distance may not be set optimally for the data you're working with Here's what you could do to try and resolve this Adjust the neg underscore prominence underscore factor If the negative peaks are not very prominent, you might need a lower neg underscore prominence underscore factor Increase the backward min underscore distance Increase the min underscore distance if the two detected negative peaks are too close to each other and should be considered one peak Try experimenting with different values for neg underscore prominence underscore factor and min underscore distance Start with larger changes to see the effect, then fine-tune with smaller adjustments Try experimenting with different values for neg underscore prominence underscore factor and min underscore distance Start with larger changes to see the effect, then fine-tune with smaller adjustments You could also consider visualizing the detected peaks on the waveform to better understand what's happening Here's an example of Here's an example of how you could visualize the detection with map problem, not part of the ECG underscore processing dot py This one might stuff up my processing, but we'll give it a go So these values are default But then where is the function actually being used over here? Yeah, it's not actually being used. That's okay You can leave it there to put an underscore there. It should not matter We test this again It's still wrong. We need to do more troubleshooting Okay, this will mess up a flash application Do not be doing this Right, so it's finding two peaks here and there Keep doing it, whatever Yeah, I knew this will Yeah, here it's finding two peaks So obviously when they are zero Can I copy? Copy, copy, copy That page will obviously not update Okay, I think the simplest solution will be just not label anything as negative peak If it's y amplitude is zero And here's the code again Gotcha, you want to skip labeling peaks with zero amplitude as negative peaks The solution here is pretty straightforward You just need to filter out the peaks that have a zero amplitude after you detect them In the detect underscore negative underscore peaks function, after With this modification, the function now only returns peaks that have a non-zero amplitude This should prevent the issue of labeling a peak with a zero amplitude as a negative peak You can test this updated function with your It's this thing Yeah, it's this thing You can test this updated function with your This is the full function though Let's assume so There's no immediate errors That's good It's not working Yeah, the GUI We'll have this Why do we do the plotting business? Okay, commented this Okay, after a few errors It's actually running It's now indicating the correct number of negative peaks Yay No Okay, one What? Two again Should have been zero for this What? Okay, because it's not exactly zero Let's see if it's working okay when we have no noise Okay, one, it's good Can you explain the prominence factor? How does that work? Absolutely The prominence factor is used to distinguish significant peaks from the noise in the signal Let's break it down Prominence in signal processing, especially when analyzing waveforms like ECGs, refers to how much a peak stands out due to its intrinsic height and its location relative to other peaks A peak's prominence is the vertical distance between the peak and the lowest contour line that doesn't intersect any higher peak Here's the flow of how the prominence factor is used in your code Inversion of ECG signal, since the standard find underscore peaks function is designed to detect positive peaks, the ECG signal is inverted to make the negative peaks positive for the purpose of detection Median absolute deviation, MAD, this is a robust statistic to estimate variability It's used to avoid the influence of outliers that are common in ECG signals Prominence calculation, the prominence value is calculated by multiplying the MAD by the NEG underscore prominence underscore factor The factor is a multiplier that determines how, prominent, a peak must be to be considered a true peak A higher factor means that only peaks that are significantly higher than their surroundings, after inversion, significantly lower in the original signal, are detected Here's the technical part For each peak, the algorithm looks for the lowest points on either side of the peak that are either at the same height as the peak or higher The height difference between the peak and the higher of these two points is the prominence of the peak In summary, by adjusting the NEG underscore prominence underscore factor, you're tweaking the sensitivity of the peak detection A high value means you'll only detect the most prominent peaks, while a low value may include less significant variations in the ECG trace It's like adjusting the threshold for what you consider, true, peak versus what is just noise or minor fluctuations The right value depends on the quality of the ECG signal and the specific analysis goals Okay, I know what the problem is, might be Here's the code again We're looking at a specific window size It's actually working now I haven't changed anything You just have to go away, come back The abnormality score for normal ECG before was 0.1, now it's 0.3 That's fine, I think That's normal again Let's see when we add noise, the negative peak Some sort of weight being We can have this as underscore And we'll actually need those because we have them as the default values We'll check what the prominence is Let's print those quickly I want to filter out the peaks with zero amplitude, obviously But we also I want to do that as well for a sec It's still finding that negative peak That's the problem Why is that happening? How come what's it always the same? It's a bit weird, I don't remember setting it up to be To do that Yeah, the GUI outside the main thread will likely to fail So that's why the plot is not working anymore Okay, that's unfortunate I have to comment it out This will only work the first time around Can I clear it? Can we clear the plot to begin with? Or the figure? Clear the figure before it's being plotted Clearing Still get that very close to zero negative peak Being labeled as a peak Where it should not be only the second one In this case should have been labeled See image attached Yep Alright, from a biomedical engineering standpoint You're probably getting that near zero negative peak detected Your peak detection criteria might be too sensitive The threshold or prominence you're setting for negative peaks Might be catching even very small fluctuations in a signal The detect underscore negative underscore peaks function Uses a neg underscore prominence underscore factor Multiplied by the median absolute deviation, mad, to determine what counts as a peak If the mad is too low, even noise could be identified as a peak To fine tune this, you can Increase the neg underscore prominence underscore factor To set a higher threshold for what's considered a prominent peak Adjust the min underscore distance parameter to ensure the peaks are not too close to each other This might help to ignore that near zero peak if it's too close to the real negative peak you're interested in You might also want to look into the baseline noise of your ECG signal If there's a lot of noise, it could affect peak detection Implementing a filter to smooth out the signal before peak detection could be beneficial Remember to tweak the parameters iteratively and check the output each time to home in only optimal settings Yeah, that GUI doesn't work properly Restart the app, I think that plotting might have stuffed things up Like prominence You always did it The prominence factor should have been larger That's one, now zero Sorry, I have to fine tune it this way How's it even possible? Something wrong There's something wrong with that prominence factor Can we double check the code? Problem with it is if I increase it, then I start getting errors And if I leave it at five at what it is I get two negative peaks when there should be only one And here's the code again I see what you're dealing with here When you're working with peak detection, especially on noisy signals like ECGs, setting your prominence factor correctly is critical Let's run through your code and see where things might be going wrong Inverting the ECG signal, you're inverting the ECG signal to detect negative peaks, which is a common approach No issues here Calculating the median and mad, you're using the median and median absolute deviation, mad, to calculate prominence This is a robust method for noisy data, but it depends heavily on how noisy your data is and the range of your ECG signal Setting the prominence, you're multiplying the mad by a factor of five to set the prominence This might be too low or too high, depending on the actual noise level and the peak amplitudes of your ECG signal If you're getting peaks near zero that shouldn't be there, it suggests the prominence factor might indeed be too low Height parameter in find underscore peaks, you're using a height parameter set to zero in skippy, signal, find underscore peaks This parameter is intended to set a minimum height for peaks Since you're working with inverted data, any peak, above zero, which would actually be a dip in the original signal, would be considered This might be why you're catching near zero peaks as well Filtering zero amplitude peaks, after finding the peaks, you're filtering out peaks where the ECG signal is zero This should theoretically remove flat line data, but if the signal is noisy and there are fluctuations around zero, these would still be considered peaks Given these points, here's what you can try to adjust Prominence factor, you increase the neg underscore prominence underscore factor until you stop detecting the near zero peak If you start getting errors, you may need to look at the specific errors and address them separately Sometimes, you might need to use a dynamic factor based on the range of your signal Height parameter, consider removing the height parameter, or set it to a negative value that reflects the minimum amplitude you would expect from a real negative peak in the inverted signal Filtering mechanism, your current filtering might not be enough You could also apply a threshold based on the expected minimum peak size or other characteristics of a valid peak Signal preprocessing, before running peak detection, preprocess your signal with a filter to reduce the noise You could use a band pass filter to keep only the frequency components typical for ECG signals Visual inspection, plot the detected peaks on top of your inverted ECG signal to visually inspect if the peaks being detected make sense Iterative approach, adjust parameters iteratively and test each change to see how it affects peak detection Let's try adjusting the code with a higher prominence factor and see if that helps I'll also remove the height parameter for now If you can upload the ECG data, I could run the updated function and see how it performs No, no, I'm not done First we need to reduce the number of numbers after the decimal point Because obviously this calculation is too heavy for anyone to manage And why that prominence factor doesn't just work Let's remove that printing for a sec I think I know what the problem is Let me use a percentage of mean max I think we need to replace median with something else Probably not mean, but maybe it should be We should be using the absolute distance between minimum and maximum Or if we can make any suggestions that would be great Essentially the problem is that we are getting the measurement for negative peaks in correct It's giving us two peaks when they should be just one I think we just fixed it by increasing the minimum distance to 1000 So it actually works now with minimum noise Get 0.3159 for normal ECG as an abnormality score It seems to be working correctly now But any suggestions as to how to improve the code are more than welcome Yep, yep, yep, yep, yep Yeah, we've reached the cap This file was originally trying to put in longer prompts So we don't hit the limit It was just annoying Actually thinking and stopping and paying for it The problem is that it's inconsistent So I get what it's doing I can handle hallucinations Well, hallucinations are sometimes considered creativity When someone hallucinates, they might be thought by someone else as being creative So that's a highly subjective thing I'm just annoyed by the user interface That limit, 40 messages per 3 hours Probably should try using the API instead In terms of cost, what difference does it make? But then you don't get access to Delhi I mean, it will be separate And I think those prompts are very expensive Pop this into... Obviously this would not work That's what I thought Yeah, things like this I mean, it's a paid service Come on There's just something wrong on this side as well Control 5 3.5 Can you handle a long prompt like this? It seems like you're dealing with some issues Regarding detecting negative peaks in your ECG signal processing code If you're experiencing problems with multiple peaks being detected Instead of just one, tweaking the parameters Like the minimum distance can indeed help Here are a few suggestions and considerations For median replacement, you mentioned replacing the median calculation with something else Instead of the median, you could consider using the absolute difference Between the maximum and minimum values in the waveform To derive a measure of variability or baseline noise level Parameter tuning, adjusting parameters like the minimum distance Can significantly affect peak detection Increasing the minimum distance can indeed help In distinguishing individual peaks, especially in noisy signals Noise reduction, consider pre-processing steps For noise reduction before peak detection Techniques like filtering or baseline correction Could potentially improve peak detection accuracy Evaluation Continuously evaluate the performance of your peak detection algorithm On various types of ECG signals, including normal and abnormal ones To ensure its robustness and reliability Optimization Look for opportunities to optimize your code for efficiency Without compromising accuracy This could involve refining algorithms, reducing redundant calculations Or leveraging parallel processing if applicable Documentation and testing Ensure thorough documentation of your code And conduct comprehensive testing with different datasets To validate its functionality and performance under various conditions By iteratively refining your peak detection algorithm And considering these suggestions, you can enhance the accuracy And reliability of your ECG signal processing code If you encounter any specific challenges Or need further assistance with implementation, feel free to ask Right, it did one error there But it was doing okay until now And then it generated two errors, almost one after the other So now back to GPT 3.5 It looks like you're encountering some errors and issues While analyzing waveform data, particularly related to peak detection And subsequent processing steps Here's a breakdown of the observations and potential suggestions for improvement Negative peaks detection The current implementation seems to have issues with detecting negative peaks accurately As indicated by the inconsistent results in the number of negative peaks detected Tweaking parameters like the minimum distance and prominence value Can help refine the peak detection process Increasing the minimum distance, as you mentioned Might alleviate the problem of multiple peaks being detected Error handling There are instances where errors occur during the analysis Due to unexpected conditions, such as when certain intervals cannot be calculated Implementing robust error handling mechanisms Such as checking for non-values and handling them appropriately Can prevent such errors from causing the analysis to fail Parameter tuning and optimization Continuously fine-tuning parameters like the prominence value, minimum distance And other parameters specific to peak detection algorithms can lead to better results Additionally, optimizing the algorithm for efficiency Especially if dealing with large data sets or real-time processing Is crucial for improving overall performance Membership function issues It seems like there are issues with calculating membership values Possibly due to sparse data or unexpected input ranges Reviewing the membership function definitions And ensuring that they cover a sufficient range of input values Can help improve the accuracy of the decision-making process Data quality and validation Regularly validate the analysis results against ground-truth data Or expert annotations to ensure the accuracy and reliability of the analysis algorithm Additionally, inspecting the input waveform data for anomalies or artifacts That could affect the analysis results is essential Feedback loop Establishing a feedback loop where analysis results are reviewed And any discrepancies or errors are addressed Can help attractively improve the analysis algorithm over time By addressing these points and iteratively refining the waveform analysis algorithm You can enhance its accuracy and reliability for various types of ECG signals If you need further assistance or specific guidance on any aspect of the analysis process Feel free to ask Okay, so the good, good news is that it's now, oops Yeah, I shouldn't have clicked on that That the machine, the bot, the phasological algorithm Is now doing better Even when there's noise present So that would be a correct rejection there And this would be, oh That shouldn't be normal All of a sudden it keeps giving errors Yeah, we have to So we're trying to improve it there when working with noise as well So when there's no noise, it's kind of no brainer It's doing really well So that's abnormal, that's correct That should be normal, that's correct And abnormality score of 0.3 We should just be lower for normal ECG But 0.3 is okay As long as it's not going above 0.33 or something 0.31, that's correct So that's working okay for when there's no noise Now the question is What happens when we add some noise to the mix And straight away, yeah we're getting this 0.32 It's being classified as abnormal So it's essentially sitting there on the threshold of being normal or abnormal Yeah, because that values Are way too close to 0.3 So that's the problem Yeah, and that one That had a missing negative peak Was also miss classified Yeah, so we'll probably release this tool for you to play with Even though there are some obvious problems That just the way it is Especially when noise is present in the signal So that's kind of what we're trying to show anyway Yeah, this one This negative peak there should have been 0 Negative peak there should have been 0 And it's still indicated 1 That's not good Someone's asking what kind of waves are these I'm learning about the Fourier series Do we have FFT? We shouldn't, we should not have FFT In this case we would have it in another tool On the website Yeah, so go check out Bionicales.com This is for all these general questions That what the answer would be Unfortunately Unfortunately We have Go study at I don't know what the ad was, it's not sponsored By that university Yeah, we kind of don't have a tool specifically covering FFT But FFT is everywhere So it would be FFT somewhere in there We do have some real So a lot of it is synthetic waveforms That you can check out everything you need to know about synthetic waveforms We also have real waveforms So this is, yeah, there will be FFT in here For sure This is the EG data in time frequency domain And yeah, go buy, was it a whoop? No, don't buy the whoop, I heard someone I mean, it's probably fine I have one of those breaking as well I can't talk to it like I'm still working But I can't connect to it using my phone By the way, we'll be finishing in six minutes So if you have any more questions, now it's a good time The six minutes is because we're reaching a four hour mark And LinkedIn doesn't allow streams longer than four hours So it's forcing me a break Same as GPT-4 It's forcing me a break as well I know it's working again Great, it sounds like you've managed to solve the immediate issue with detecting negative peaks in the ECG signal by adjusting the minimum distance parameter This is a common approach in signal processing Tweaking parameters like minimum peak distance can significantly improve the performance of peak detection algorithms, especially for noisy or complex signals Now, about your initial concern regarding the use of median, it's a valid point The median is robust against outliers and is often used in signal processing to provide a more stable central tendency measure than the mean In your specific context, considering the full range of the signal might be more informative Here are a few suggestions Yeah, it's actually not... I don't know why it's refusing Sometimes it's keen on generating code Other times it's like not generating any whatsoever And I keep asking it to either generate code or write prompts for GitHub co-pilot to do it But it forgets Like now Anyway, so this tool on the side will definitely have an FFT in it And there is the same tool that actually can play, can turn this stuff into music So it's essentially the same tool, but you also have an option to listen to the waveform So yes, there will be lots of FFT there processing that stuff There's a hohova, I think, if I'm pronouncing it correctly I was asking if I'm doing this again Yeah, you can look at the YouTube channel, especially the live streams We are pretty much live streaming daily And yes, some of the live streams will eventually, if something interesting happens Especially if you want to come live or something, ask questions We can definitely arrange that with probably using Zoom So we can make better content, more engaging And then I normally turn anything that was interesting in the live stream into standalone videos That's what the videos are on YouTube at the moment And yeah, live stream pretty much daily Hopefully not for 4 hours anymore And go check out the site I don't know if this will work, but I know I can tell you came from YouTube We have some people watching on LinkedIn as well Probably contacts, but they don't want to talk Yeah, we have a lot of other content coming up Mainly around EEG, but ECG as well, as you can tell And potentially getting data of wearables and things See what we can do with that This one is like a clinical, medical grade stuff, but I can't talk to it I think it just can be tossed in the rubbish bin But there is a lot of data sets that we are looking at online We have some downloaded data already, open source data sets If you have any that you know about and you want me to look at Or if you have your own data, pop it up on GitHub or any open source stuff I don't know, I don't want to deal with lawyers later And I'm happy to have a look at it Probably live stream like a first impression stuff So beware And my web page RPM at the moment is quite high So it's a good time to go check the website You'll be supporting this project What else we've got? Yeah, all the tools on the page they need updating These four do not work at all Currently we'll be releasing one that's an irrelevant ad We'll be releasing one that's combining fuzzy logic And this game, where's the game? This one, ECG game So that's what we're currently doing, hopefully It actually starts working, it kind of works Just modifying a few things and we'll be releasing on the other side So you can play against the machine I think my LinkedIn stream will go out Because they actually don't want you to stream over four hours Where's it still going? I don't know Yeah, there's a delay as well, it's quite a long delay No, they did stop it Fuzzy logic is set theory I don't know what do you mean by set theory But can you give a quick overview of fuzzy logic How is it related to set theory? I don't think it's set theory It's like a signal detection theory, maybe Is that what you mean? And can you also give an overview of what we did today? No, you wouldn't know, would you? Because we restarted the thing several times And we'll give it a go Yeah, I don't want it to talk It's annoying, isn't it, when the robot is talking I just can't read myself but We'll find a bit on fuzzy logic and set theory, maybe Yeah, this one For fuzzy logic, it's not directly related to set theory in the traditional sense Fuzzy logic is more about dealing with reasoning that is approximate rather than fixed and exact In classical logic, a statement is either true or false, but fuzzy logic allows for degrees of truth This can be incredibly useful in signal processing where you're dealing with ambiguous, noisy, or incomplete data Fuzzy logic helps in making decisions based on imprecise information, which could be beneficial in interpreting ECG data where the signal can vary significantly between individuals and even within the same individual under different conditions In the context of your ECG processing, you might use fuzzy logic to classify segments of the ECG waveform or to make decisions about what constitutes a peak under varying conditions It adds a layer of flexibility to your signal processing algorithms, allowing them to be more adaptive and robust in the face of real-world signal variability Anyway, it's meant to be explainable AI, explainable machine learning In Python, it's fairly straightforward, there's a Python library for it that anyone can use It's called SKFuzzy and bobs your uncle Essentially you need to design your membership functions Yeah, you design those membership functions which we do here We do this for multiple parameters So in this ECG waveform, this is synthetic waveform The bot keeps telling me I'll be careful with publishing personal information It's synthetic, it's no one's ECG So we're looking at the number of positive and negative peaks In this case 2 and 1 Look at the amplitude The amplitude is calibrated Normalize between 0 and 1 We'll look at RPIC sharpness So in theory it's finding RPIC It's a simple algorithm, nothing like this PEN, Tomkins algorithm and stuff I didn't use it because I think it's assuming you have multiple continuous waveforms Whereas we only have one of those ECG peaks coming once at a time That's kind of different I don't know how that algorithm probably would not work So for the sharpness, 0.42 for this one, 0.4 for that one And we have another example Which should be 0.1 or so Less sharp By the way, you can go play this game, it's available for you You'll be playing against yourself, not against the machine We haven't published this yet Hopefully we'll publish it this week So we can place more ads on the website No, just joking Anyway, we're looking at RPIC sharpness Oh, where is it? Yeah, mostly 0.42 And one of them should be different That's all that you will see Nothing fancy You will enjoy the ads Hopefully this should be too intrusive There's just one banner at the bottom of the page It really should not be any ads for the bottom tools there Because they don't work well or don't work at all Like I say, for example Yeah, what were you asking about the FFT? I'm not sure about the FFT But you also will probably learn about statistics and stuff So there was this statistical tool That talks about the entropy, skewness, kurtosis Things like that, that I used in EEG signals And all that left from that is this YouTube video Because the original was programming in Levyoo, MATLAB I don't do that anymore because the license is too expensive I mean, if Levyoo and MATLAB want to sponsor this channel I can go back and use it again They don't have money to pay for the license So using anything open source So all that left from this tool currently doesn't work And all that left is a broken tool and a bunch of YouTube videos That people for some reason still watch it Even though the tool is not working So that's fine, but currently I will essentially be redoing something similar So it's a statistical analysis, a toolkit in Levyoo Obviously Python will have something similar So we're now re-coding the whole thing Not me, GPT-4 is recording this for me Yeah, so I don't know if some of those ads are not relevant anyway So just get relevant I mean, obviously you can use an ad blocker I have one as an add-on on Firefox Works perfectly well You won't see any ads whatsoever on any page But then keep in mind that you might also not see any content in the future If that content creator is relying on the advertisement revenue I do not, but it will be nice I like ads, no one likes ads Everything is an add-on When you talk you promote yourself Anyway, yeah, there's a bunch of tools that will need some more work here There probably would have been much FFT in this one as well It's a cross coherence between two channels Ensuring you power spectral density for, you can select This is 16 channel data So you can select your channel To add the trending And show your coherence value So essentially how similar or different the two channels are And yes, there will be FFT somewhere In the processing of this data And yeah, this tool doesn't look great We'll have to redo it Yeah, this peak here in coherence essentially shows that At that specific frequency there is something going on between the two channels You can't really see it from the time series You can't really see it from the power spectral density But you can see it in the cross coherence function So essentially if this peak is really high To go look at that frequency And there is something going on there And this data is seizure, epileptic seizure So EEG while the subject having a seizure So if you have any strong correlation between the two channels And it can be measured as the cross coherence between the two Then you know the potential that the seizure is just happening Both channels at the same time And you can see how it changes when you increase the time window Yeah, there is something happening at that frequency So essentially it could be used for seizure detection or something Yeah, this is where I'm using a lot of electricity Because I should limit the window size there Okay, it gets stuck That's the kind of stuff I need to fix So yeah, this was probably from a year ago I have to go back and revisit it What's higher on the page Been developed more recently I hope it will work better as well But I might just sign out Unless there is a check I'm streaming on Twitch, no questions LinkedIn just dropped me out Because for our limit in YouTube We had a bunch of questions But they were not quite technical Yes, we discussed fast logic What kind of waveforms And FFT, maybe Yeah, there will be a lot of content on YouTube On FFT, I don't want to add to that It's probably a saturated content area So I didn't have my lunch yet 5 o'clock, so I'll see you probably tomorrow Maybe Bye