 Yeah, we normally start by saying keep repeating the same thing over and over again Hopefully I'll come up with something new To say but yes, if you haven't checked the bodykills.com, please do so There's a bunch of tool on the front page. Go check it out in that note. You think hopefully Start today with eye tracking should be a quick one because we'll probably get stuck Pretty quickly. So currently we have this bite and it's great That works pretty well. However Issues with it. That's the bit that actually does the eye tracking So it's calculating the center of the eye finding the pupil while looking at this regional interest and Doing grayscale Transfer and adjusting brightness and using this hue circle transformation to To actually find the pupil inside the eye and then it's looking at the darkest spot To label the pupil and it's kind of works on one But not so much on the other So the red dot is just the center of that the region of interest and the green one is where the pupil is being found well, that's where the darkest spot inside that region of interest is and Sometimes it even works, but yeah, obviously needs more improvement. This one is using a lot of my CPU GPU and The FPS is only at 15. So normally when I started the first time it was 30 As it should be actually have as you can tell Two cameras connected up. So I was thinking using one for each eye as well But we will see how we go any suggestions And comments on this are more than welcome Yeah, and this one will stop working as well if I move outside the The field of view it will Give an error. Yeah empty empty array the usual the usual stuff it could barely Easily fix that but we actually decided to start fresh and start a new application where we primarily rely on javascript instead of Python libraries So we'll be using We would not be using media pipe It will be still a flask application So there is a potential to have something running in the back end but primarily relying on javascript To reduce my electricity cost because if it's in the front end and you pay For electricity because it's running on your device, but then the obviously the quality of it will depend on your memory and so on so forth so we might Yeah, and the idea is that eventually we'll have some sort of calibration thing and I do believe that we could show That with the right calibration with a simple webcam running at whatever 30 60 or even 415 fps frames per second we can achieve some good results kind of comparable to systems that cost I would imagine You know tens of if not hundreds of thousands of dollars But yes, we'll need some sort of calibration procedure where you follow as some sort of dots on the That appear on the screen and potentially won't be human calibrated. So you click. Yes, I'm happy with that kind of thing That's what I'll be happy with. So not fully automated, but semi automated where it tells you to Look around keep your head still look around the screen like that And do some snapshots and then here. Yes. Yes. I'm happy with that kind of thing So might close this one And move to the second version of it Yeah, so you see when I close the python application server It's still running in my browser because everything is being loaded into the browser And you will also have all the source code just by looking at the browser Yeah, to run it from the get go to to see everything But you you get the idea but if I refresh this So we'll go open A current application So this will primarily rely on javascript This will have it will still be in the form of A flask application. So later if need be We have an option to run things in the background in there on the back end but currently Right port 5000 is already in use Right close this one quickly. You need to stop the other one Yeah, this should be better Yeah, and my cpu went quiet as well. This is currently what we have for the javascript code And it doesn't want to run It's probably using a different camera Yeah, I have two webcoms connected and problem is they have the same name. So that doesn't help When trying to switch between them and the life Quickly try different Browser, right? Well, it is working in a different camera On a different browser and it's doing the eye detection here at the bottom So not great and also I do want to use chrome Because I kind of trust it a bit more Edge should be fine as well, I guess And yes, obviously we need to improve this one quite a bit It's something with trying to use a different camera. Okay, I'll just use Just use edge browser. So those this is where the eyes are and we are from memory getting an error Yes, hopefully One of the bots will be able to fix this for us. How about we do a single shot Romped for this and by the way github copa was not able to Um Manage it fix it. So unfortunately I have to A copy paste. So we have a html. We have a css That this one actually meant to dealing with the transparency of the window And and them being a overlayed one on top of the other But sure if this one. Yeah, so that that's the folder the project A file and folder structure Yeah, that looks legit Up that as well and now the main Thing that does all the heavy lifting is this JavaScript file as opposed to the previous version Where We had the python doing everything running it in a threaded mode should be fine And let's prompt it some more of it. So I shared all the code. This is obviously as you can tell and I tracking Application a flask application. We would like to mainly rely on JavaScript on front end So we use less electricity Not getting so one of the So it's working in terms of detecting the eyes, but the pupil detection is not currently working We're getting the following Error Can you fix this a given overview of the whole application? and If you feel like being funny or something or making funny comments, not as funny jokes ha ha ha funny, but A comments on the application That I'm using go for it and give us a Big overview of what this can be useful for And also touch on calibration. How do we calibrate this thing? And we would like to continue developing it Can you help? yep We want to do the auto talking which is Eh do it ourselves Your app uses a webcam to track eye movements in real time It's a cool blend of flask for back end and heavy lifting JavaScript in the front end for real time processing This setup minimizes server load which is smart for energy efficiency You're leveraging tensorflow js and the blaze face model for face and eye detection pretty high tech stuff Yeah, whatever. Um It's actually originally was generated by ivar co-pilot or a gp4 anyway, so it's just giving praise to itself kind of thing You're getting a typo related to the get image data function This error usually pops up when the parameters pass to get image data are not of the correct type specifically not integers long type in javascript Okay, can you actually fix this for us? The error message in the log nan Undefined values suggest that the issue lies in how the eye bounding box dimensions are calculated in detect pupil Okay, correct data types ensure that the values for x y width and height in eye bounding box are integers You can use math round or math floor to ensure this Boundary checking make sure that the bounding box doesn't go outside the video frame If it does it might result in invalid coordinates Right. Yes. We have a coordinates issue modifications to detect pupil function get the eye bounding box Suggesting a correction for it might comment this out quickly And this one is ensuring that coordinates and dimensions are integers between the bounds Sounds legit But as we know Sometimes it only sounds legit, but actually Does the some rubbish in this case it's good that we can actually test And if it If it's working or not You're Accessibility helping people with mobility issues interact with computers User experience research understanding how users interact with web pages applications Gaming creating more immersive gaming experiences Calibration for calibration. You typically show the user several points on the screen and track their gaze as they look at these points This helps the system understand how the user's eye movements correspond to screen coordinates You can implement this by overlaying points on the canvas and recording the eye positions where the user looks at these points Development tips robustness in various lighting test and improve the model's performance and the different lighting conditions User interface developer user friendly interface for calibration and real-time feedback Performance optimization continuously optimize the code for performance, especially if you're planning to run this on low-end devices And hey if this app can accurately track where I'm looking Maybe it can finally solve the mystery of why I can't find my glasses when they're literally on my head nerd face Keep up the great work and let me know if there's anything else you need If you can keep the jokes more relevant to the subject at hand That will be great It didn't really give a broader Perspective overview of how this tool could be used like could be could be used for I was still trying to Solve the error That's something basic that this isn't working You will need to go over the code again We had the github copilot making some Changes that I'll share with you in a second. We'll have a constant landmarks somewhere We really have it No, you have to ensure that landmarks array pass to the extract i regions function And it has the right structure Okay, so before Copilot is out of whack don't have that function No, I do just call something else Right, so the landmarks should have x y width and height for each eyes It's been a simple task You're not giving up on We do normally give up on github copilot, but we do not give up on GPT-4 Yeah, maybe I'm confusing it by prompting it too much This stuff so it was definitely working better before Let's get into the nitty gritty of your eye tracking application Your script is quite sophisticated integrating tensorflow js lays face and some complex java script for real-time eye and pupil tracking It's impressive. I see there's a hiccup with the pupil detection The error right eye does not have x y width and height properties is a clear indicator It seems that the right eye object is missing the expected properties This can happen if the extract eyes regions function isn't correctly extracting or formatting these properties from the landmarks array provided by the blaze face model Verify landmarks data ensure that the landmarks array provided by blaze face actually contains the eye coordinates in the format you expect Blaze face might return landmarks as an array of points where each point is an array of two elements x and y coordinates rather than an object with x y width and height Adjust extract eyes regions modify dysfunction to correctly interpret the landmarks and calculate the eye regions If blaze face returns simple x y coordinates for landmarks You need to define width and height for the eye regions based on some assumptions additional calculations Check for data availability before drawing or processing check if the necessary data is available and correctly formatted Let's assume blaze face returns each landmark as an array x y Here's how you might adjust extract eyes regions This kind of eye tracking tech has tons of potential applications healthcare for diagnosing and monitoring conditions like strabismus or for use in neurology Human computer interaction improving ux design by understanding how users visually interact with interfaces Marketing analyzing consumer attention on advertisements or products Calibration calibration is critical for accuracy display points on the screen and go ordinates and have the user look at them This process helps the system understand how eye positions correlate with screen coordinates Moving forward keep it rating Make sure to test under different conditions lighting distances, etc to refine the models accuracy And remember the key to grade tech is not just to make it work. They're making it work reliably under varied real-world conditions Okay, so it seemed to be tracking the eye now There is only one eye being displayed below the video footage The x and y coordinates seem to be working okay The width and height is not actually doing anything. Do we actually need it? So the question is where is the other eye? A Where is the A region of interest. I guess that's the little square Is it too small potentially look at the image provided and here is the code again Do I need to change anything in the html? or the css file I was hoping eventually the all the labels will be all overlaid on top of the video footage We eventually want all the processing of the A video to be displayed as well. So the grayscaling circle finding darker regions We want to see everything on the front panel Yep So using this place face tensorflow So I don't know how well is that working We'll explain the code in a sec again the whole code Based on the image you've provided in the code snippet. It looks like you're on the right track with the eye detection However, there are a few things we might need to iron out From the console log in your image. It seems the eye detection box has static dimensions width 30 height 20 These dimensions may be too small depending on the distance of the user from the camera or the resolution of the video feed You might need to dynamically adjust these based on the size of the detected face If only one eye is being drawn onto the canvas, it's possible that either the eye detection isn't working correctly for both eyes Or that the drawing function has a bug For overlaying the detections and other processing visuals on top of the video, your current html CSS setup shall allow for it as both the video and canvas elements are positioned absolutely at the top left corner top 0 left 0 The transparency of the canvas will enable you to see the video underneath any drawings To display all the processing stages you could draw each step on the canvas However, this would mean that your canvas acts as a feedback loop of your processing showing grayscale transformations circle detection, etc This could be done for debugging purposes, but might be visually cluttered for an end user Dynamic eye box sizing adjust the size of the eye detection boxes based on the size of the detected face The eye box should be a certain percentage of the face width height Debug drawing functions verify the draw eye and draw pupil are being called with the correct parameters for both eyes You may want to add more console logs or even draw text labels on the canvas to ensure that both eyes coordinates are being passed correctly Yes, this one doing essentially percent of the face size a width and height That looks legit With doing face detection as well, we will eventually show the whole the whole process The whole process To detect faces. Yeah, when it does the rest of the code stuff It's when I'm having trouble integrating If prediction length is more than zero so essentially there is a face on the screen Doing prediction conscious stuff That's the same That's this line here. I don't know why it doesn't tell you what the specific lines It changed Everything else seemed to be the same So they extract the eyes region now taking the face sizes and input And now the width and height is Changing the eyes a bit off Actually kind of like it instead of overlaying stuff A on top of the image doing it separately. Maybe that's a a better better way to go Where is the ava I ava I Where is the ava I It's like that. Am I using the right microphone? Check By the way, this meant to be an interactive session. If you have any comments Funny comments any compliance suggestions to let me know I'm using the correct microphone. We are missing the ava I and but now it's working better The numbers the width and height including x and y are adjusting Correctly a check out the image that I added And give it the whole code again because we had the github Copilot doing some adjustments as well But if you would like to take over in terms of code development go for it And make funny comments if you feel like it But keep them relevant to the subject at hand We're trying those. I don't know if they still call the single shot prompts because it's this was it third or fourth a prompt or so He Didn't like it there didn't doesn't say what the problem is just throws this Um areas that you so prompt the github copilot a bit as well But it might be complaining that I'm selecting the whole code I'm already doing that. Am I just trying to see if it's working or not Yes, it does work says it does already include the includes an agreement. Here's how it works. So it's good It's explaining what the what we did before Question is where's the second eye? How we have One eye detected and even that being a bit off We need the second eye Yeah, then start labeling pupils in it as well Yeah, so it gets that the accuracy is not great. I didn't say it but just Gets the context use a more accurate model Okay Right, so this will be python isn't it. So we we're currently not using A media pipe. What are we using? using tensorflow blaze face And tensorflow js. Yes, the previous code was using a media pipe But we decided not to go with it. My cameras get stuck Might have to hide it. I don't know this camera is like a mile to something You know, what's happening with them They're probably there's two cameras that I have that they have the same name. They're the same Microsoft hd whatever meant to be a 30 FPS thing And let's read that one quickly Looking at your updated image and code. It seems like you've made progress The bounding boxes for the eyes are now dynamic adapting to the size of the detected face, which is great However, the issue of any one eye being drawn persists and you're right. It's probably time to debug this If you're consistently only seeing one eye tracked, there are a few potential culprits Landmark extraction confirm that extract eyes regions is being passed the correct landmarks from blaze face And the both eyes are indeed being detected If the landmarks are incorrect or not what you expect the function may fail to create proper bounding boxes for both eyes Okay, I'm not getting any errors for it. Am I? Oops. No, that's we don't want to Debug Open a website And we don't get any errors in java script and we mainly do in java script. So if there were any errors, it would be in java script Landmark extraction confirm that extract eyes regions is being passed the correct landmarks from blaze face and the both eyes are indeed being detected If the landmarks are incorrect or not what you expect the function may fail to create proper bounding boxes for both eyes Drawing order ensure draw eye is being called for both eyes You can add a console log within draw eye to verify it's being hit twice with different coordinates Canvas overriding since you clear the canvas at the start of each detection loop It's possible that subsequent drawing calls might be overriding the first eye To check this you could temporarily remove the clear call and see if both eyes are drawn albeit over multiple frames Camera angle or face orientation if the user's face is tilted or turned away from the camera blaze face might only provide landmarks for the visible eye Make sure the user's face is directly facing the camera during detection Yes, we have that little eye over there that like expands as you Come a move closer to the camera. That's good But then it's off and we only have the one I think it's the whichever one it is Let's do console log. We're really doing console log What are we currently logging prediction landmarks? Yeah, so how do we know which one is left and which one is right? And it's common data out for a sec We'll look at the different point in the code This one has an array length of six And what's that Yes, it's come from line 40 when copilot should just be a GPT-4 anyway, right, but he gets very different responses for some reason So it's a array of six subs array each contains two elements. These are likely landmark likely and it's specifics By the place face model each sub array represents landmark on the face for the two elements being the x y coordinates of the landmark Right eye, left eye, nose, mouth, center, right ear, left ear Okay, the function spec the first two landmarks Is it even correct? To ensure that the eyes are being checked correctly could the visualize the landmarks. That sounds good But this function already the way And now we do have detect faces now we need Detect faces to go to call draw landmarks. Why don't just do it inside? left eye Side for starting to draw new detections. Why are we clearing? Can we do it over here? Give an error. We're drawing anything No, we're already We already have that declared somewhere We have it in detect faces Why there's pupil in detect faces? Yeah, we're getting stuck. Let's do a one Drawing order ensure draw eye is being called for both eyes You can add a console log within draw eye to verify it's being hit twice with different coordinates So it is being called twice the draw eye but then the canvas is cleared and we have the This is the whole function. It looks like This will be async the detect faces comment it out right, so we have the stuff The nose the ears the ears moving. Uh, it's actually pretty cool avatar could improve on it then question is to the pupil No, the pupil to not work the eyes are just in the same The pupil are just in the same position all the time We have some warning that warning is about Still have that eye detection That getting smaller larger That's good except we don't have the second eye Canvas overriding since you clear the canvas at the start of each detection loop It's possible that subsequent drawing calls might be overriding the first eye To check this you could temporarily remove the clear call and see if both eyes are drawn We'll be at over multiple frames multiple frames. This one we're still doing like 30 SPS I would imagine we'll display it on the screen canvas is being cleared at the start Is clear Rectangle can just Move it I would imagine nothing will get cleared if I remove this one So we just keep overlying It's moving in and out, which is nice Are the eyes just jumping from left to right? Obviously need to clear We have a function draw No, so why are you suggesting that? I suspect the two eyes are just being overlaid on one one on top of the other the actual eyes Camera angle or face orientation if the user's face is tilted or turned away from the camera Blaze face might only provide landmarks for the visible eye Make sure the user's face is directly facing the camera during detection No, we have the two eyes. That's not what the problem is Remember while debugging is like being a detective in a crime movie where the villain is elusive bugs The end goal is to create a seamless user experience So once you nail the tracking think about the user interface and how to present the tracking in a way that's both intuitive and minimalistic avoiding clutter Will we be able to sort the eyes? For calibration consider a guided process where users follow dot on the screen to map out the extremes of the gaze This can be a playful experience like a mini game to keep it engaging Okay, I have to be beware leave those funny comments While we're at it Let's not forget that while the eyes might be the window to the soul in this case. They're also the doorway to potential bug fixes And if the eyes are misbehaving by not staying put it might just be them expressing their desire for a little game of hide and seek Okay, this becomes deeply philosophical Let's tackle this one step at a time and soon we'll have an eye tracking system that even the Mona Lisa would be envious of Because let's be honest. She's been trying to catch someone's eye for centuries. Okay What was the suggestion And why there's nothing in the log moment? so detecting detect base being called bunch of times, but actually a comment that those calls out so we're clearing the canvas in the Beginning of the face that the face detection the draw eye left eye right eye And left pupil right pupil in the bugging control f5 it and right. That's a lot of stuff There's still only one eye And this is what we get in the console log And this is the current code that we have Hey, can we fix So the other eye is visible also the pupil The pupil labels are not working correctly They don't seem to be moving within the eye I uploaded a couple of images that show you how The interface looks like there's an out problem that the eye that is being displayed only one of them Is out of focus. It's not actually focusing on the eye itself In the little avatar the pupils are not moving at all The second eye Is not being shown and the two images are just being overlayed or something Yep Well, it's generating a world so So this one is a webcam best based eye tracking system We need to still calibrate it and prove it eventually it will be available on bionicchaos.com for you to try out for three So it works a bit better than this We are also head about like 20 sessions developing this waveform feature extraction and detection for ecg for the ecg game So the ecg game is already out there you can go and Play it yourself and then By labeling normal and abnormal ecg and now we will soon Have another version where there is a bot a robot a machine whatever learning not learning. It's a fuzzy logic A system. So it's all preset. It's a deterministic Or more deterministic sense the system than not that will play the game against you So you can see the robots will be taking over The medical time series Data labeling field So that's coming up And another big thing is we also looking at some data sets a publicly available This one is a creative commons. This is what we want to see Please do publish your data sets under creative commons license this is quite forgot Whatever whatever the yeah, I don't want to go into licensing whatever but yes open source essentially So is that is open source still had to register on the website to actually download it, but there is Quite a lot of subjects. So there's about like thousands or so. Yeah, a thousand The functional connect on project website So neural imaging does a set you have both ecg and mri And the patients are doing some sort of tasks. Um, so we This will be a big project to explore this data set So let me know what you think or if you have done look at this data set before do let me know I downloaded some of the files. Apparently I downloaded the wrong ones and a gpt4 Provided some code that could open it Eventually would like to have like a full flask application that's a opening this data displaying it processing it so you can learn Well, so I can learn And you can follow along Let's go back quickly to the eye tracking business From your description and the console log you've provided it seems like there are few issues to untangle Both eyes detection the logs indicate that both eyes are indeed being detected and drawn However, you mentioned that only one eye appears in the overlayed avatar pupil movement The pupil coordinates don't seem to be moving relative to the eye detections suggesting there may be an issue in the detect pupil function Focus and overlays the eye in the avatar is out of focus and the second eye is not visible Which could be due to the drawing order or incorrect coordinates Since the console logs show coordinates for both eyes it suggests the issue might be with how or when you're drawing them on the canvas Look for any canvas clear-rect calls that happened after the first eye is drawn But before the second eye draw call Ensure that the draw eye function is called for each eye with the correct parameters Okay, I obviously want the two eyes to be drawn in different locations So not overlaid one on top of the other and not clearing one each other Not seeing the rest of the code for just Just look at the rest of the code not a problem, is it? And that's getting something super generic if we select more code just being silly, aren't you? Check that the detect pupil function is correctly identifying the darkest point in the eye regions If the pupils don't seem to move it might be that the darkest point detection isn't sensitive enough or the eye region being analyzed isn't correct Verify that the camera resolution is high enough to capture detailed eye movements And consider adding some thresholding to identify the pupil more clearly against the iris The focus issue could be related to the camera's autofocus feature Ensure the camera is set to focus on the face or have a fixed focus that captures the eye region clearly For the overlaid images, it seems like the second eye might be drawn but is being cleared or overwritten This can happen if the drawing in clearing of the canvas are not correctly ordered In your current code, you have overlapping clear rect calls that clear the canvas multiple times within the same detection loop This could potentially clear one of the eyes before it is drawn Here's a revised version of the detection loop I still don't see the other eye Remove any additional clear rect calls within the loop to avoid clearing the drawings prematurely Run this updated loop and check if both eyes are now being drawn and if the pupils are moving correctly If the pupils are still not moving correctly consider adjusting the detect pupil function to better identify the pupil's position within the grayscale image data Can you regenerate the whole detect faces function? This detect faces function does the following Clear as the canvas once per detection cycle to prevent overwriting drawings Loops through each prediction from the face detection model Draws the bounding box for detected face Extracts and draws the eye regions based on the landmarks and size of the detected face Detects and draws the pupils within the eye regions Logs all relevant information for debugging Logs all relevant information for debugging Logs all relevant information for debugging Make sure your extract eyes regions draw eye detect pupil and draw pupil functions are all working as expected before running this loop This structure should give you a clear step-by-step process for detecting and displaying eye tracking information Might leave this one for now And move on to the ECG waveform extraction might take a break You know if there's any comments If so pop them in the chat We can using that window It's called a webcam eye tracker Just crop it a bit It's the The window is too large Might pop it at the bottom like that So we're really touched on the A webcam will take it out We are probably not going to do it currently unless someone asks for it We won't be looking at the public a new A data set public does that we're really doing some work with the IEG dot org data set that's the neuro vista data that a lot of the tools on the side are Using Yeah, we'll do The waveform feature extraction, but Take a break first Take a break We'll start a new GPT Yes, it's called kario quest bot Hey, we have four files for it This html Run the python code see That won't work because we're using bot 5000 Use 5001 Can you do that? Yes Right so actually have Yet the webcam detection doing stuff on bot 5000 and the the fuzzy logic A bot is running on five five thousand and one Probably shouldn't be telling you that if you are trying to hack into my server But we are running everything locally And if you did manage to hack into the server, please do let me know how you did it Default has a bit of noise in it Not exactly where my screen ends. It's not cool The game logic part So in this case, this is a full class complication. There's both Hey, can you explain how this whole application Is working What is it doing? They give some general perspective if you Feel like being funny go for it, but keep it relevant on the to the topic And explain each file the index html waveforms js game logic js and the Python script And give some broader perspective We will also have to fine tune the fuzzy logical rate. I mean the moment. It's not doing very great It's It's always the detecting every waveform as Ab normal so generating Only hits and false alarms and the score goes negative So we'll need to modify the mention functions and the fuzzy logic rules Hey, can you give an overview and suggest specific code changes? Yep These two js files are the brains behind the ecg games operation on the front end Waveforms js this is where the magic of ecg waveform generation happens You're using mathematical functions to create both normal and abnormal ecg waveforms The waveforms are generated dynamically, meaning they're created on the fly as the game progresses Game underscore logic js this file is all about the game's mechanics It controls how the waveforms are displayed on the canvas handles user interactions like clicking on waveforms and keep track of the score It also sends waveform data to the python back end for analysis, which brings us to the next part This python script is the server side brain It's where the heavy lifting of data processing in analysis happens Fuzzy logic analysis the core function of this script is to analyze the ecg waveforms using fuzzy logic When the javascript front end sends a waveform to this script It processes the data to extract features like amplitude peaks and intervals Then it runs a fuzzy logic analysis to determine if the waveform is normal or abnormal Problem area you mentioned the analysis is always detecting waveforms as abnormal leading to only hits and false alarms This skewin analysis is likely due to how the fuzzy logic rules and membership functions are set up They might be too biased towards detecting abnormalities Adjust membership functions in fuzzy logic your fuzzy logic setup needs a tweak The membership functions determine how each input like amplitude or peaks is categorized If these are off your analysis will be off You should adjust these functions to more accurately reflect the characteristics of normal and abnormal ecg waveforms Refine fuzzy logic rules the rules in your fuzzy logic system decide how the inputs are combined to give a final decision These rules need to be refined to improve accuracy For example ensure that the rules for what constitutes a normal waveform are as robust as those for abnormal Validate with real data if possible test your system with real ecg data This can help you understand how well your system performs and what specific aspects need refinement Debugging and testing implement more robust debugging and testing methods Log detailed information about the waveforms and the fuzzy logic systems decisions to see where it's going wrong Your application is a fantastic blend of gaming and education especially for those interested in biomedical engineering and healthcare It's not just a game. It's a tool for learning and understanding ecg patterns, which are crucial in medical diagnostics However, the challenge lies in ensuring the game is not only engaging but also accurate and educational By refining your fuzzy logic analysis and continuously testing with real data, you can enhance its effectiveness as a learning tool Remember biomedical engineering is all about precision and accuracy So your fuzzy logic needs to be on point to make this game a reliable source for learning Keep tweaking testing and refining On the topic of updating the membership functions and fuzzy rules Can we go over each membership function and each rule If you have any suggestions as to how to improve it, do let me know Absolutely, let's dive into the fuzzy logic part of your application Fuzzy logic is all about handling the uncertainties and approximations that come with real world data Which in your case are ecg waveforms Getting the membership functions and rules right is key to ensuring accurate analysis Your membership functions define how each input, like amplitude or peaks, is interpreted in terms of fuzzy logic Let's review them one by one Amplitude, asterisk Low, currently defined as fuzz, trimf, amplitude, universe, 0, 0, 0.5 This means low amplitude is considered anything up to 0.5 Medium, defined as fuzz, trimf, amplitude, universe, 0.3, 0.5, 0.7 This overlaps with both low and high, which is good for a smooth transition High, defined as fuzz, trimf, amplitude, universe, 0.5, 1, 1 It covers the upper range Positive peaks Too few, fuzz, trimf, positive underscore peaks, universe, 0, 1, 2.5, implies a low count of positive peaks is abnormal Normal, fuzz, trimf, positive underscore peaks, universe, 2.5, 3, 3.5, defines the normal range Too many, fuzz, trimf, positive underscore peaks, universe, 3.5, 4, 5, for an excessive count Suggestion, verify these thresholds with typical ECG patterns For instance, if a normal ECG typically has 2-3 positive peaks in a cycle, adjust the normal range accordingly Similar to positive peaks, you need to align the too few, normal, and too many, categories with what's typically seen in ECGs Our peak sharpness This is an interesting parameter Ensure the, low, normal, and, high, categories reflect actual ECG characteristics For instance, a very sharp R-peak might be abnormal, so how you define, high, is critical These intervals are crucial in ECG analysis The membership functions should be based on standard medical knowledge of what constitutes normal and abnormal intervals The rules determine how the inputs interact to give a final output Let's look at them Default rule, it's a bit vague It says if the peaks and amplitude are not normal, then the abnormality is possible This might be too broad and could lead to false positives Rules for peaks and amplitude, these seem to focus heavily on the count of peaks Ensure these rules are in line with medical knowledge of ECG patterns Sharpness rule, how does sharpness correlate with abnormality? This needs clear definition Interval rules, these are critical The rules for PR and RT intervals should be based on well-established medical criteria Refine membership functions, use real ECG data to adjust the ranges in your membership functions The current ranges might not accurately represent real-world ECG patterns Balance the rules, ensure that the rules are not too biased towards detecting abnormalities There should be a clear distinction between what's considered a normal variation and what's genuinely abnormal Medical Input, if possible, get input from a medical professional specializing in ECG interpretation They can provide valuable insights into how to set your membership functions and rules Testing and iteration, test your system with as many real and synthetic ECG patterns as possible Adjust your system based on the outcomes of these tests Remember, the goal is to mimic the complex decision-making process a medical professional goes through when interpreting an ECG Which is quite challenging but definitely achievable with careful tuning and testing Let's get to it To adjust all these parameters So if you were an ECG specialist, how would you adjust the membership functions and the fuzzy rules? Alright, if I were an ECG specialist looking to treat the fuzzy logic system for ECG waveform analysis I focus on ensuring the membership functions and rules closely aligned with clinical knowledge and real-world ECG data Here's how I'd approach it Amplitude, rare Real-world ECGs vary in amplitude due to numerous factors like patient size, electrode placement, etc Low, I might consider defining low amplitude as significantly below the average ECG amplitude seen in healthy adults Medium, this should encompass the typical range of amplitude seen in normal ECGs Positive and negative peaks The number of peaks in an ECG correlates with the heart's electrical activity Too few, this could indicate missing or weak signals, like in atrial or ventricular fibrillation Normal, a normal ECG typically shows a set pattern of P, QRS, and T waves Too many, excessive peaks could indicate extra heartbeats or other abnormalities The R-peaks sharpness can indicate the velocity of ventricular depolarization Adjust the sharpness thresholds based on typical sharpness values in healthy versus diseased hearts These intervals are crucial in diagnosing conditions like AV block, tachycardia, etc Use clinical guidelines to define normal, short, and long intervals Balance sensitivity and specificity, the goal is to minimize false positives and false negatives Adjust rules to be specific enough to detect abnormalities without over-flagging normal variations Integrate clinical knowledge, incorporate rules that mirror clinical decision-making For instance, a very short PR interval might be indicative of pre-excitation syndromes Rule interactions, consider how different parameters interact For example, a high amplitude with a normal R-peak sharpness might still be normal, but a high amplitude with an abnormal R-peak sharpness might indicate hypertrophy Contextual awareness, in ECG interpretation, context is key For instance, a single premature beat might not be clinically significant, but frequent premature beats are The system should account for such nuances Dynamic adaptation, ECG patterns can vary greatly among individuals The system might need to adapt its thresholds based on demographic data, age, sex, etc, if available Real ECG data, test the system with a diverse set of ECG data, including both normal and pathological gazes Continuous feedback, regularly update the system based on new clinical findings or feedback from healthcare professionals Error analysis, for any misclassifications, analyse where the error occurred and adjust the system accordingly Collaboration with clinicians, work closely with cardiologists or other ECG experts to validate and improve the system In essence, the adjustment of this system requires a tight integration of clinical knowledge, statistical analysis of ECG data, and iterative testing It's a complex task, but with careful attention to detail and continuous refinement, the system can become a powerful tool for ECG interpretation We do not currently have easy access to cardiologists or any other ECG specialist So we will have to fine-tune the parameters ourselves I'm sharing a couple of images of how the waveforms look like Can you rewrite the fuzzy logic analysis function? We've updated membership functions and fuzzy rules Include all the comments as to what changes were made So go over each membership function and each fuzzy rule The images you've uploaded appear to be screenshots from a game interface that includes an ECG waveform and scores for both a human player and a fuzzy logic system The scores reflect the ability to correctly identify abnormal ECG patterns The fuzzy system seems to be overly sensitive, marking nearly all ECG segments as abnormal, hits, leading to a high number of false alarms and no correct rejections Given the fuzzy system's tendency to classify waveforms as abnormal and your inability to consult with a cardiologist, we will need to rely on general knowledge of ECG patterns to recalibrate the membership functions and rules Here's how we can proceed Membership function adjustment, we should ensure that the membership functions for normal ECG features are well-defined and based on standard ECG waveform characteristics We will adjust the ranges for amplitude, peak count, and intervals to more conservative values that better match typical ECGs Fuzzy rules refinement, the rules will be updated to reflect a more balanced approach, preventing the system from being overly sensitive to abnormal patterns We will implement rules that allow for normal variations in the ECG I'll now provide a rewritten analysis function with comments to indicate changes Since I cannot execute Python code, this will be a conceptual representation that you would need to integrate and test in your actual codebase We have quite a few rules We have a default rule and rules 1 to 6 And a rule for abnormal intervals It doesn't want to generate any more Just gave up on me Why did you give up on me? Over them in the same way We reload the page Can we continue generating the same analysis? We've suggested changes for all the rules So let it go to that We will have to go over these membership functions again So we have amplitude Haven't changed that 6, 1 haven't changed that So I don't know why it didn't generate that code considering there was no changes made Amplitude We have low, medium and high Let's call it normal That's actually a better name But then it will start complaining So we'll take the... The problem with the current amplitude is that It's really became slow because we're at minus 150 points When the points go low it becomes much slower Yeah, ideally we would not be looking at the signals that we have Eventually we want to add much more as well With the amplitude so we don't want to end up fine tuning each parameter We don't want essentially to use magic numbers We might change the name to medium Comment that out for a second See essentially how good of a cardiologist a GPT-4 can be So we have positive peaks Just make sure that the names are the same We have too few, normal too many 2-3 peaks can be normal Accounting for PNT more than 3 peaks might be normal One positive peak is typical Okay So the thing is it later also important like once we go over the rules It makes a huge difference because we're essentially giving a weight for each situation So we might keep it... Didn't go over all of them Sharpness, pretty much sharpness It's also changed the values, we keep them between 0 and 1 And this one might be a bit finicky So sharp ones are more typical Broad range for normal peak sharpness Very sharp peaks may indicate abnormality Very sharp Yeah, we have low normal high Yeah, let's try that Abnormality So essentially the output So we have both binary, normal, abnormal But we also have likelihood Which is... Abnormality is likely possible or unlikely And it's suggesting I already have that line That's not antecedent I'm pretty sure we have that line somewhere Yeah, so the range for abnormality is from 0 to 1.1 With steps of 0.1 Get rid of it Now the rules... Well, we haven't It didn't go over all the membership functions Which are a bit odd The ranges for the membership functions have been adjusted to be more conservative Reducing the likelihood of false positives Fuzzy rules The rules have been refined to better balance the sensitivity and specificity of the system A new rule, rule 5, has been added to account for cases where our peak sharpness is normal Which could indicate a normal ECG Output decision A decision threshold is used to categorize the ECG as normal or abnormal based on the fuzzy output This threshold can be adjusted based on testing and validation Implement the preprocess underscore waveform function that will extract the required features from the ECG waveform Integrate the new fuzzy logic system into the application Test the system with a variety of ECG waveforms to validate the performance and adjust as necessary Continuously refine the membership functions and rules based on the test results This pseudo-goat needs to be integrated and tested within your actual application environment It should serve as a conceptual guide for how to structure the fuzzy logic system Given the constraints and goals of your ECG analysis game Rule 1 for normal ECG considering all factors Okay It's a lot It might actually keep the rules proper names This one looking at positive peaks So it's saying essentially if everything is normal, abnormality is unlikely Okay So we actually need to make sure this is working for a dataset In our dataset we only have one normal ECG type The other ones are abnormal And I actually don't expect it to work With the GPT-4 suggestions So I'm not sure if it means it's not a great cardiologist or Just in our case the sample data is different So I'll have to tweak things To suit the waveforms that we are generating So it's suggesting So there is like an edge cases of normal ECG So when it's always normal When it's clearly abnormal Rule for potentially abnormal Interestingly enough it's not even looking at the arpic sharpness For being potentially abnormal Rule for potentially abnormality based on arpic sharpness Okay So I take my comment back It actually has a separate rule for arpic sharpness And rule 5 Incorporating arpic sharpness being normal Not just normal Positive peaks normal How is it Rule 5 different from rule 1 That rule 1 doesn't have arpic sharpness It does It seems to be Rule 1 has everything as the and Operators Rule 5 has and and then or Which can make sense But then the description is not right Let's also decide to rewrite the analysis ECG function Which we didn't actually ask for Even have it We have something Well currently it's in the same function I think it's suggesting to split Yeah cause currently The phasological analysis is like way too long So it refracted it into two parts Do we have a calculate membership? Yeah as I said it's a pseudo code So it's not quite relevant To the code we provided Suggesting breaking down What we currently have would not even work Yes something broke That code too many doesn't exist So we have low, medium, high for amplitude We do have too few normal too many Positive peaks Doesn't have too many We had too many before and it used to work It's wrong isn't it Didn't do a mistake? Yes it did Now I don't trust it Made a mistake there as well I think that number might be 6 instead of 5 Because we can make it 5 over there Yeah that one should have been labeled as normal Well that's because of the noise We have like 29 positive peaks That's not right So let's reduce the noise to 0 That's abnormal that's correct Let's just wait for a normal That's abnormal correct 0.7 That's abnormal correct 0.6 What do we get for a normal one? For a normal we get 0.59 Yeah 0.64 0.59 is the normal one Yeah there's not much difference between them That's the problem And we get 0.59 for another That's the abnormal anyway So that's no good I have to modify the rules quite a bit Yeah I guess an error like that Means that for some conditions There were like no rules were met at all Even though we have a default rule So I have to check what that error is all about Yeah ideally we give those rules Some proper names Yeah I like it Wait Before we have more rules just a second ago Alright yeah we do So we have 5 rules Instead of the 7 that we have We are happy to try them out We have 5 rules And why do we need this? Analyze The ECG is not being used It's just suggesting to split the function It can make sense because it's too long It's the abnormality analysis Yeah the decision is not good It shouldn't be resolved below a certain number That's what we have the membership functions for Justing It's dead normal Doesn't tell you for which I remember which one wasn't It was this medium for amplitude Let's forget CTRL SHIFT C Instead of CTRL C alone That helps For a sec Yeah we should have the interval What's the problem? CTRL SHIFT C I don't know where this error is coming from I don't know It might be going in circles now Anyway we need to fix those membership functions Hopefully we can do Later today or tomorrow I'll see you next time Bye Handle undefined values If the PR or RT interval can not be calculated Ensure that your fuzzy logic system can handle Undefined or none Error handling Enhance error handling in the Analyze underscore Waveform function to provide more informative messages When something goes wrong Here's a revised version of the relevant parts Of the Analyze underscore Waveform function With these considerations In the fuzzy underscore logic underscore Analysis function you should add appropriate checks To handle the case when the PR and RT intervals Are none or zero Which can happen if they're undefined These changes aim to handle unexpected Or undefined inputs in a manner that prevents errors And allows the fuzzy logic system to continue operating Potentially with default values The normalization function ensures that the intervals Are scaled to the range expected by the fuzzy system Preventing out of bounds errors In the fuzzy underscore logic underscore analysis function You should add appropriate checks to handle the case When the PR and RT intervals are none or zero Which can happen if they're undefined These changes aim to handle unexpected Or undefined inputs in a manner that prevents errors And allows the fuzzy logic system to continue operating Potentially with default values The normalization function ensures that the intervals Are scaled to the range expected by the fuzzy system Preventing out of bounds errors Okay, it started happening The error started happening after changing The membership functions After changing the fuzzy logic analysis Can we again go Over each membership function One by one Suggest Default Values And then We go over the fuzzy rules as well One by one Of what we currently have So if we can act as a cardiologist That would be great So you should be agnostic to what The waveforms actually look like And we want to generate General membership functions And activation rules For the fuzzy logic Sure, let's tackle the fuzzy logic membership functions And rules as if I were a cardiologist Setting default values and rules based on general knowledge Of ECG characteristics Amplitude Low Normally, ECG amplitude is not very low Unless there is poor electrode contact Or the patient has a low voltage ECG Which is rare We will set this to cover a very small portion of the range Medium This should cover the typical range for an ECG Which is what we expect in most healthy adults High A high amplitude might indicate ventricular hypertrophy Or other conditions So we'll set a range that captures significantly higher than normal values Asterisk Too few In a typical ECG, there should be one R wave per cardiac cycle If there are less, it could indicate a serious condition Normal One positive peak per cycle is normal Which is the R wave Too many, more than one R wave May indicate conditions like ventricular tachycardia Negative peaks Too few, negative peaks are less common And their absence might not always indicate an abnormality Normal Normally, we don't expect to see many negative peaks In a very short range Too many, multiple negative peaks could indicate an inverted T wave Or an abnormal Q wave R peak sharpness Asterisk Low, an R wave that is too broad might indicate a conduction delay Normal A normal R wave will have a certain sharpness That corresponds to a healthy heart's conduction speed High A very sharp R wave could indicate a hybrid cued myocardial infarction Rule for normal ECG I If the amplitude is medium Positive peaks are normal Negative peaks are normal And R peak sharpness is normal Rule for clearly abnormal ECG If If amplitude is high or low Or if there are too many positive or negative peaks Then the ECG is likely abnormal Rule for potentially abnormal ECG If If there are too few positive or negative peaks Or the R peak sharpness is abnormal Then the ECG might be abnormal If there are too few positive or negative peaks Or the R peak sharpness is abnormal Then the ECG might be abnormal Certainly Let's define each membership function And corresponding fuzzy rule Assuming a general understanding of ECG characteristics With our specific waveform visuals The goal is to create a fuzzy logic system That can generalize well over typical ECG features Amplitude Asterisk Low, uncommon in healthy ECGs And could indicate technical issues Or specific medical conditions Amplitude range typically observed in a healthy population High, significantly above the normal range Possibly indicating cardiac hypertrophy Or other abnormalities Positive peaks, e.g. R waves Asterisk Too few, less than one positive peak Might be due to a rarer arrhythmia Or an error in ECG recording Normal, typically one positive R wave But cardiac cycle is expected Too many, more than one positive peak Could indicate extra cislies Or other forms of arrhythmia Negative peaks, asterisk Too few, maybe normal as Negative peaks are less common, e.g. Q waves Normal, typically Zero to one negative peak is expected per cycle Too many, more negative peaks Can suggest ischemia or infarction R peak sharpness, asterisk Low, indicates a less sharp R wave Which could be due to a conduction delay Normal, a sharp R wave Is typical of a normal ECG High, extremely sharp R waves Could be pathological Normal ECG rule, if If the amplitude is medium And both positive and negative peaks are within normal Ranges, then the R wave sharpness is normal The ECG is likely normal Abnormal ECG rule, if If the amplitude is very High or very low Or if there are too many positive or negative peaks The ECG is likely abnormal Potentially abnormal ECG rule, if If there are too few positive or Negative peaks, or if the R wave sharpness Is high or low or if the r-wave sharpness is high or low, the ECG might be abnormal. These rules and membership functions should create a balanced fuzzy logic system that can distinguish between normal and abnormal ECG patterns with reasonable accuracy. Remember, the exact range is 4. Certainly, let's clarify the fuzzy rules and address the area you're encountering. We're suggesting a set of rules that can make educated guesses about the normality or potential abnormality of an ECG pattern based on a general understanding of ECG characteristics. Rule for normal ECG, rule underscore normal, if If the amplitude is either too high or too low, or if there are too many positive or negative peaks, then we assume the ECG is likely abnormal. If there are too few positive or negative peaks, or if the r-peak sharpness is not normal, either too high or too low, then we consider the ECG possibly abnormal. We can certainly define additional rules if needed, especially if there are specific combinations of these features that are known to be indicative of certain conditions. However, the three rules above provide a basic structure for the fuzzy logic analysis. The error message error in Analyze underscore waveform, unexpected input, PR underscore interval suggests that the fuzzy logic system is receiving an input for the PR interval that it doesn't expect or can't handle properly. This could happen if the PR interval value is outside the expected range defined for the fuzzy variable. The PR interval variable is not properly set up in the fuzzy system. Here's how we can troubleshoot and fix it. Ensure the PR interval is within the expected range, Make. Make sure that the PR interval is normalized correctly within the range, 0, 1, as defined by the membership functions. The fuzzy system might not know how to handle, undefined, or none. It's better to set a default value that the system can interpret, such as 0 or a value within the normal range. Verify that the PR interval is properly defined as an input in the fuzzy control system. Here's a code snippet to handle none values and ensure the PR interval is within the expected range. Make sure to apply similar logic for the RT interval and any other inputs to the fuzzy system. The goal is to ensure all inputs are within the expected ranges and that none, or undefined, values are handled gracefully. The error, unexpected input, PR underscore interval, suggests there might be an issue with the values being passed to the PR underscore interval antecedent in the fuzzy control system. This could be because the value is outside the defined range, where it might be a non-numeric value like, undefined. Given that the range for PR underscore interval is defined between 0 and 0.21, ensure that all inputs to this antecedent are numeric and fall within this range. The undefined value must be converted to numeric value that the fuzzy system can handle. Here's how you could modify the PR underscore interval and RT underscore interval antecedents to include handling of undefined values. When setting up the system, ensure that you handle undefined intervals by setting them to a default value within the defined range. For example, you could decide that an undefined PR interval should be treated as a... No, I think the PR and RT intervals should be between 0 and 1. Yes, we might want to double check that. And also for certain waveforms, I get them both as undefined. Yep. Alright, if the PR and RT intervals should be between 0 and 1, then the membership functions for the PR and RT intervals need to be adjusted to reflect this range. This also means that when you get a value of undefined for these intervals, the system should handle it by assigning a default value that falls within this range. Here's how you can adjust the PR underscore interval and RT underscore interval definitions and handle undefined values. In the fuzzy logic analysis function, you need to handle undefined values for PR and RT intervals. A common approach is to treat undefined as the midpoint of the normal range or as a neutral value. It's important to normalize the actual PR and RT interval values to be between 0 and 1 before setting them to the fuzzy control system. If the intervals are measured in seconds or any other unit, you need to find a way to convert them to a 0-to-1 scale that matches the membership functions. If an interval is undefined, you assign the default underscore normal underscore value, which is a neutral value within the normal range. This way, the fuzzy system can handle all cases consistently. To address the issue of the unexpected input, PR underscore interval error, let's ensure that the input for the PR and RT intervals is correctly handled. It seems that when the interval values are undefined, it's not being processed properly by the fuzzy logic system, which expects a numeric value. Here's a step-by-step approach to resolve this. Make sure that the input ranges for PR underscore interval and RT underscore interval cover all possible numeric values as you've set them between 0 and 1. When you receive undefined for PR and RT intervals, you should assign a default numeric value to use. Since these are ratios, the default might be the midpoint of the normal range. In the code where you input the PR underscore interval and RT underscore interval into the fuzzy system, handle the undefined case by assigning the default value. Here is an updated version of your analyze underscore waveform and related functions to include these changes. In the fuzzy underscore logic underscore analysis function, we're making sure that the PR and RT interval values are within the expected range, 0, 1, by clumping them using max and min functions. If the intervals are undefined, they've already been replaced with a default value of 0.5 in the analyze underscore waveform function. Also, ensure that your membership function ranges for PR underscore interval and RT underscore interval reflect this, 0, 1, range in the definitions. The error message suggests that there's an input that the system doesn't expect, and this change ensures that the system will always receive a valid numeric input. Run the application with these changes, and the unexpected input error should be resolved. If the error persists, it's where in the code where the PR and RT intervals are being used. The error, unexpected input, PR underscore interval suggests that the fuzzy logic system may not be properly configured to accept PR underscore interval as an input, or there's an issue with how the value is being passed to it. Here's a checklist to troubleshoot this issue. Verify antecedent setup. Ensure that PR underscore interval is correctly set up as an antecedent in the fuzzy logic system with appropriate membership functions. Check input range. Confirm that the input range for PR underscore interval matches the domain of the input data. Validate input. Before passing the PR underscore interval to the fuzzy logic system, validate that it is within the expected numeric range. Debug print statements. Place print statements before the line where the fuzzy logic system receives the PR underscore interval input to confirm that the input is as expected. Update fuzzy system inputs. If PR underscore interval or RT underscore interval are not used in the rules, the fuzzy system may throw an error. Ensure that each input variable is involved in at least one rule. Check data types. Ensure that the data types of all inputs to the fuzzy system are floats, which are expected by the system. Python is strict about types, and passing an incorrect type, like a string representation of a number, can cause errors. Here is a code example with validation and debug print statements added. Make sure that the validate underscore interval function is called before the values are passed to fuzzy underscore logic underscore analysis, and that the function itself is correctly receiving The debug print statement will help confirm that the values are correct just before the fuzzy logic system uses them. If the error persists even with numerical values, there might be a deeper issue. Consider simplifying the fuzzy system to the minimum working example with PR underscore interval and then build it up step by step, testing at each step to identify where the issue arises. Certainly, let's expand the fuzzy rules to include all the variables, amplitude, positive peaks, negative peaks, r peak sharpness, PR interval, and RT interval. Integrating all these variables will make the fuzzy logic system more comprehensive and potentially more accurate in its analysis. Rule for normal ECG, if the if the amplitude is very high or very low, or there are too many positive or negative peaks, the ECG is likely abnormal. If the r peak sharpness is either too high or too low, the ECG might be abnormal. If either PR or RT interval is too short or too long, the ECG is likely abnormal. Rule for mixed indicators, combining various factors. If there are multiple indicators of abnormality, e.g., abnormal amplitude with abnormal peak counts or abnormal intervals with abnormal peak sharpness, the ECG is likely abnormal. Let's implement these rules. These rules consider various combinations of ECG features to provide a comprehensive analysis. The rule underscore normal is designed to capture cases where all ECG features fall within the normal ranges. The other rules address different combinations of abnormalities, thereby covering a broad spectrum of potential ECG patterns. This setup should improve the ability of the system to classify ECG patterns accurately. These rules consider various combinations of ECG features to provide a comprehensive analysis. The rule underscore normal is designed to capture cases where all ECG features fall within the normal ranges. The other rules address different combinations of abnormalities, thereby covering a broad spectrum of potential ECG patterns. This setup should improve the ability of the system to classify ECG patterns accurately. The issue you're experiencing, as indicated by the error message you've funked, less, did not contain a loop with signature matching types, less than class, numpy, dtypes, float64dtype, greater than, less than class, numpy, dtypes, strdtype, greater than, to none, seems to be related to how your application handles the undefined values for PR and RT intervals. When these intervals are undefined, your application is probably trying to perform a comparison or operation that's not valid with undefined string. To address this, you need to modify the handling of undefined intervals within your fuzzy logic analysis. Here's a suggestion on how you might adjust your code to handle these cases. Adjust the INBO handling. When PR or RT intervals are undefined, you should set a default value or handle them differently in the fuzzy logic system. Update the fuzzy logic analysis function before passing the intervals to the fuzzy logic analysis. Check if they are undefined. If they are, handle them accordingly. In this modified version, if PR underscore interval or RT underscore interval is undefined, a default value, e.g., 0.5, is used. This way, the fuzzy logic system always receives a numerical input, avoiding the type mismatch error. Remember, this is a workaround. In a real-world application, you'd want a more sophisticated approach to handle missing or undefined data, possibly involving consultation with domain experts, like cardiologists, to determine the best course of action. Understood? If the PR and RT intervals show always output a number, but you're facing issues when there aren't enough peaks to calculate these intervals, which revise the calculate underscore intervals function. This function needs to handle cases where there are not enough peaks to calculate PR and RT intervals reliably. Given that we're analyzing individual PQRS complexes and not a continuous waveform, the approach for calculating PR and RT intervals needs to account for different scenarios, like when certain peaks are missing. Here's a revised handle missing peaks. If the necessary peaks for calculating PR or RT intervals are missing, calculate these intervals based on available data or default to a certain value that indicates an incomplete waveform. Fallback values. When specific peaks are not found, use fallback values that make sense in the context of an ECG analysis. For example, if the R peak is missing, we might assume a default position based on the length of the waveform. Here's a modification of the calculate underscore intervals function to reflect this. In this revised function, we estimate intervals based on the number of detected peaks. If there are two peaks, we assume they are RT and estimate the P peak. If there are fewer than two peaks, we use a default value. This approach ensures that the function always returns numerical values, preventing errors due to undefined values. Remember, these are approximations. In real world applications, it's crucial to validate such assumptions and fallback strategies with domain experts. Alright, wearing my virtual cardiologist hat here, let's dive into how this application can be a game changer in the medical field, particularly in training medical students, while also keeping an eye on necessary disclaimers. Practical understanding of ECG analysis, this tool is like a digital sandbox. It allows students to explore ECG waveforms in a controlled environment. They can input different waveforms and see how changes in peaks, intervals, and amplitude affect the fuzzy logic analysis. It's hands-on learning without the risk. Demonstrating a role of AI in medicine, the app showcases how I can assist in medical diagnostics. It's not just about learning to read an ECG, it's about understanding how technology can augment human skills. This is crucial in an era where tech and medicine are increasingly intertwined. Developing critical thinking, by observing how the fuzzy logic rules apply to various waveforms, students learn to think critically about ECG interpretations. They get to see that diagnosis is not always black and white. Sometimes, it's fuzzy, pun intended. Research and improvement. For those budding Einstein's in a medical class, this tool can be a research platform. They can experiment with different membership functions and rules, seeing firsthand how tweaks can improve or impair diagnostic accuracy. Disclaimers, not a replacement for professional advice. Big flashing lights here. This tool doesn't replace a seasoned cardiologist's expertise. It's an educational aid, not a diagnostic oracle. Always consult real-life experts for clinical decisions. Approximations and assumptions. The app makes certain assumptions, like estimating missing geeks. These are educated guesses, not gospel truths. In the real world, each ECG is as unique as a fingerprint and needs human interpretation. Results may vary. The fuzzy logic is based on predefined rules and thresholds, which might not go all the while. Wonderful variations of human algorithms. It's good, but it's not invaluable. Constantly evolving, medicine and technology are fast-paced fields. What's cutting edge today might be old news tomorrow. This tool is a snapshot of current knowledge and technology, not the final frontier. Remember, using this tool doesn't automatically grant you the title, cardiologist. You still need to endure countless coffee-filled nights and survive the wrath of attending physicians. This app is like training wheels for ECG analysis. It helps you stay upright, but eventually, you'll have to pedal the complex world of cardiology on your own. While the app can crunch numbers and analyze waveforms, it can't empathize with patients or laugh at your jokes. That's your job. In summary, this application is a fantastic educational tool that brings a taste of ours potential in medicine to the classroom. It's about blending technology with traditional learning, encouraging students to think critically and creatively. Just remember, it's a stepping stone, not the entire journey. I see you've uploaded several ECG trace images with corresponding scores and analysis details, including PR and RT intervals. Here's what might be going on with the PR and RT interval calculations. PR interval calculation. The PR interval is measured from the start of the P-wave to the beginning of the QRS complex. If the application is only detecting peaks and not differentiating between P-waves, QRS complexes, and T-waves, it might inaccurately calculate the interval. For example, if the first peak is assumed to be a P-wave where it's actually part of the QRS complex, this would result in an incorrect PR interval. RT interval calculation. Similarly, the RT interval calculation requires identifying the R-wave and the subsequent T-wave. If the peak detection algorithm is not accurately identifying the specific waves, the RT interval will be off. Peak detection sensitivity. The algorithm might not be sensitive enough to detect all peaks, especially if they're small or if the ECG is noisy. This could lead to missing P or T-waves, resulting in undefined intervals. Resolution and scaling. If the ECG data is not properly scaled to the actual time units, the intervals calculated could be inaccurate. Make sure that the data's time resolution, samples per second, and scaling used in interval calculation. Algorithm robustness. The algorithm might not be robust against noise or artifacts in the ECG trace. This could lead to full speaks or misspeaks, skewing the PR and RT intervals. Here are some steps to potentially improve the PR and RT interval calculations. Implement a more sophisticated peak detection algorithm that can differentiate between P-waves, QRS complexes, and T-waves. Increase the algorithm's sensitivity to low-ambitute waves while filtering out noise. Ensure that the data scaling in terms of time is accurate. Validate the algorithm against known ECG traces with annotated P, QRS, and T-waves to fine-tune the detection parameters. The application's diagnostic conclusions should always be reviewed by a qualified healthcare professional, and this software should only be used as a supplementary tool, not a definitive diagnostic system. When training medical students, emphasize the importance of understanding ECG waveform anatomy and not solely relying on automated tools. Additionally, make clear that while the tool is in education and pattern recognition, it does not replace the expertise of a cardiologist. Let's get into it now. To address the issues with the PR and RT interval calculations, we need to ensure that the peaks detected correspond to the correct P, QRS, and T-waves. The intervals are calculated only when the necessary waves are identified. The undefined output is handled correctly. Here's an approach to refine the calculation of PR and RT intervals. Based on your description, it looks like the PR and RT intervals are being normalized twice, which would indeed make the values smaller than they should be. The normalization should only occur once. It seems we're normalizing the intervals both inside the process underscore data function and again in the calculate underscore intervals function. Let's fix the code to normalize only once and ensure the intervals are properly calculated. I'll update the calculate underscore intervals function to only calculate the intervals without normalizing, and we'll remove the normalization step from process underscore data. This way, we prevent double normalization. Here's the corrected calculate underscore intervals and process underscore data functions. Now, process underscore data will return none for the PR and RT intervals if they can't be calculated, which is fine for the fuzzy logic analysis as long as it expects to handle none values. Make sure that the fuzzy logic analysis can handle these cases. Remember to test your application after these changes to ensure everything is working as expected. If none values are not handled correctly in the fuzzy logic analysis, you might need to set default values or just the fuzzy rules to account for missing data. Now, process underscore data will return none for the PR and RT intervals if they can't be calculated, which is fine for the fuzzy logic analysis as long as it expects to handle none values. Make sure that the fuzzy logic analysis can handle these cases. Remember to test your application after these changes to ensure everything is working as expected. If none values are not handled correctly in the fuzzy logic analysis, you might need to set default values or just the fuzzy rules to account for missing data. Apologies for any confusion. Just clarify, normalization should be performed once, but it should be done in the right place. If the PR and RT intervals are used in the fuzzy logic analysis and need to be normalized, which is typical, as you wonder, are in good variables to be in the same scale, this should be done just before they're fed into the fuzzy system. The change I suggested was to remove the normalization from the calculate underscore intervals function because it seemed like they were being normalized again in the process underscore data function. However, if they're not normalized in process underscore data, you may need to use the normalization step in calculate underscore intervals. Let's correct that. We'll calculate the PR and RT intervals in calculate underscore intervals, normalize them, and return the normalized values. The process underscore data function then just passes these values through, so it doesn't need to normalize them again. Make sure to adjust your fuzzy logic analysis to appropriately handle none values for these intervals if they're not present. If the fuzzy logic system does not handle none values, you may need to implement a strategy to handle these cases, such as setting default values or adjusting the membership functions and rules. The process underscore data function then just passes these values through, so it doesn't need to normalize them again. Let's go over the issues you're encountering with the PR and RT interval calculations and the fuzzy logic decision making process. PR and RT interval calculation, the PR and RT intervals are ratios that represent time intervals in an ECG waveform. You've normalized these intervals by the length of the waveform, which is a standard approach However, if you're seeing values that are too small or not making sense, it could be due to incorrect peak detection or improper handling of cases where the expected number of peaks is not found. The membership functions or the ranges for normal by not be appropriately defined. The rules may not be comprehensive or correctly prioritized to cover all normal cases effectively. There might be an issue with the inputs being provided to the fuzzy logic system, perhaps due to incorrect data processing or normalization. To correct the code and improve the algorithm's accuracy, consider the following steps. Review peak detection logic, ensure that the peak detection is robust and can accurately identify P, QRS and T peaks in various conditions. Investigate any assumptions made, such as the order of the peaks and consider more sophisticated ECG feature detection algorithms if necessary. Adjust membership functions. If your membership functions for normal intervals are too restrictive, they might not cover all variations of a normal ECG. Re-evaluate these functions accordingly. Examine fuzzy rules. If the rules are too simplistic or not correctly weighted, the system might not differentiate between normal and abnormal effectively. You may need to add more rules or adjust the existing ones to account for a broader range of scenarios. Double-check normalization. If you remove the normalization step, make sure that the raw values you're now using are appropriate for the fuzzy logic system. If normalization is necessary, it should be done consistently and correctly across all data points. Implement a debugging mechanism to trace how the inputs are processed through the fuzzy system and which rules are being triggered for a given output. This can help identify where the system is failing. For the ECG waveforms that are misclassified, you would need to look at the values of each fuzzy variable for that waveform and see which rules are being activated. It could be that the membership functions for the normal range are not capturing the values correctly or that the rules need to be adjusted to better handle the edge cases. Without a detailed look at the exact values being fed into the fuzzy system and the logic of the fuzzy rules, it's challenging to pinpoint the exact issue. However, the suggestions above should give you a structured approach to debugging and improving the system. If after these steps the problem persists, you may need to share more specifics about the misclassified cases and the logic of the fuzzy system for a deeper analysis. The abnormality score in a fuzzy logic system is determined by evaluating the input parameters against a set of fuzzy rules and then aggregating the results to reduce a final score. The specific score will depend on the rules you've established and the membership functions for that variable. Negative peaks, 1 Raw amplitude, 0.5832851527423106 Our peak sharpness, 0.41666666666666667 PR interval, normalized, 0.3 RT interval, normalized, 0.3 We would need to evaluate these against your fuzzy rules. Since I don't have the ability to run your fuzzy logic engine directly or the specific details of the membership functions and rules, I can't calculate the exact abnormality score for you. However, in a typical fuzzy logic system, you would do the following. For each input variable, determine the degree of membership in each of the corresponding fuzzy sets. For example, if positive peaks, a 3 falls mostly in the normal fuzzy set, but slightly in the too many set, you would have membership degrees for each of those sets. Apply the fuzzy rules to degrees of membership. For example, if you have a rule that says, if positive peaks is normal and raw amplitude is medium, then abnormality is unlikely. You would take the minimum membership degree of the normal, set for positive peaks and the medium, set for raw amplitude as the strength of the rule. Combine the rule outputs according to their assigned consequent fuzzy sets for the output variable, abnormality. This is typically done using methods like max min composition or max product composition. Defuzzify the combined output fuzzy set to yield a single number. The defuzzification method often used is the centroid or center of gravity method, which finds the balance point of the output fuzzy set. For a precise abnormality score, you would follow these steps using your fuzzy logic control systems API or tool. If you need to implement this manually or in another environment, you would need to replicate the functionality of your current fuzzy logic system according to its design. The membership functions for the positive peaks in a fuzzy logic system are used to categorize the number of positive peaks in an ECG signal into linguistic terms such as too few, normal, or too many. These are usually defined using triangular or trapezoidal shapes, but other shapes like Gaussian or sigmoidal can also be used depending on the system's design. Here's how you might define triangular membership functions for positive peaks based on the universe of discourse, the range of possible values for positive peaks, which are assumed to be between zero and six. In this example, we're assuming that too few is defined as anywhere from zero to two peaks. Normal is defined precisely at one peak, a range that allows some flexibility from one to three peaks. Too many is defined as more than two peaks with the range extending to the maximum expected number of peaks. The exact numbers used for these membership functions should be based on clinical data and expert knowledge. They will determine the sensitivity and specificity of the fuzzy logic system in identifying normal and abnormal ECG patterns. If more precise or nuanced categorization is needed, you can add more linguistic variables or adjust the shapes and ranges of these functions. In defining membership functions for ECG signal analysis, using Gaussian or generalized bell-shaped functions can provide a natural representation of the data. Gaussian functions are characterized by their mean and spread, while generalized bell-shaped functions are flexible and can create a variety of shapes based on their parameters. Sigmoid functions, which are often used in machine learning, could also be considered as they smoothly map the input between zero and one and are defined by their slope at a specific point. However, these functions have a mathematical complexity that might not always be suitable for real-time analysis or systems with computational limitations. The choice of membership functions should balance accuracy in representing the data with the system's ability to process the information efficiently. For detailed guidance on fuzzy membership functions, you can refer to educational resources that explain these concepts in depth. The error message you are getting indicates that there's an issue with the rule set in your fuzzy logic system not covering the input space adequately. This means that there are some combinations of input variables that are not triggering any rules, leading to an inability to calculate a crisp output. Here are a few steps to troubleshoot and potentially resolve this issue. Rule coverage, ensure that your rules cover the entire input space. There should be at least one rule that will fire for any possible combination of inputs. Membership function overlaps. Check your membership functions for each antecedent. They should overlap to some extent to avoid gaps in the logic where no rules would fire. Default rule, consider implementing a default rule that will fire if no other rules do. This can be a catch-all rule with less weight that provides a default action in case of sparse inputs. Rule adjustment, adjust the existing rules to be less restrictive. If they're too specific, they might not apply to a wide enough range of input scenarios. Input validation, validate the input values to ensure they're within the expected range. It's possible that inputs are not being scaled or normalized correctly before being fed into the fuzzy logic system. System testing, test your fuzzy system with a wide range of input values to ensure robustness and identify any input combinations that don't trigger a rule. Debugging information, print out which rules are firing for given inputs during testing to understand better why certain inputs are not producing a crisp output. After making these checks, if you're still facing issues, you might want to consider consulting a fuzzy logic expert who can look into your specific implementation in detail. Sure, let's start by examining the membership functions. Membership functions define how each point in the input space is mapped to a membership value between 0 and 1 for each fuzzy set. It's crucial that these functions are defined correctly to ensure the system's reliability. Short, define with a trim from 0 to 0.30. This means any PR interval from 0 to 0.30 is considered short. Normal, overlap with short and long from 0.30 to 0.99. A value around 0.66 would be considered completely normal. Long, start overlapping with normal around 0.66 and goes to 1. Short, similar to PR define from 0 to 0.30. Normal, overlap with short and long from 0.30 to 0.99. Long, start overlapping with normal around 0.66 and goes to 1. Low, from 0 to 0.3. Normal, overlap with low and high from 0.2 to 0.8. High, start overlapping with normal around 0.7 and goes to 1. For the positive peaks and negative peaks the membership functions were initially defined with specific ranges that cover from 0 peaks to a maximum number, 5 for positive, 4 for negative. It's important to ensure these ranges reflect realistic counts of peaks that could occur in an ECG waveform. Low, define from 0 to 0.1. Medium, abroad range from 0.05 to 0.95 covering most of the possible amplitude values. High, from 0.9 to 1 overlapping with medium. Now, let's move on to the rules. Rule normal this rule states that if everything is classified as normal, the ECG is likely normal. This is a straightforward rule but it might be too restrictive if all conditions need to be normal for it to fire. Rule abnormal amplitude peaks this rule says that if the amplitude is too high or low, or if there are too many peaks the ECG is likely abnormal. This seems to make sense as extreme values in these categories often indicate abnormalities. Rule potential abnormal sharpness if the R peak sharpness is too high or too low it indicates a possible abnormality. This is a good rule assuming that extreme sharpness values are uncommon. Rule abnormal intervals this focuses on the lengths of PR and RT intervals marking the ECG as likely abnormal if they're too short or too long. It's crucial that these are set correctly according to clinical standards. Rule mixed indicators this rule appears to catch mixed signals from the inputs. If there are conflicting indicators from different inputs, it marks the ECG as likely abnormal. However, this rule could be too complex leading to potential issues if it's trying to cover too much. It's important that these rules are not contradicting each other and that they cover all possible scenarios. Each input should have a path through the rules that leads to an output, avoiding the situation where no rules are triggered. Alright, to adjust the fuzzy logic system to consider an ECG waveform with the given parameters as normal, we can tweak the membership functions like this. Positive peaks, asterisk Too few, keep current from 0 to around 2 Normal, adjust this range include 3 as a strong member so maybe from 2 to 4 Too many, adjust this range to start higher than 4 Negative peaks Too few, keep current from 0 to around 0.5 Normal, adjust this range include 1 as a strong member so maybe from 0.5 to 1.5 Too many, adjust this range to start higher than 1.5 Amplitude Low, keep current from 0 to around 0.1 Medium, broaden this include 0.58 as a strong member so maybe adjust to 0.1 to 0.7 High, adjust to start around 0.7 Low, maybe 0 to 0.3 Normal, broaden this range to strongly include 0.416 so perhaps 0.3 to 0.7 High, adjust to start around 0.7 Normal, broaden this range to strongly include 0.416 so perhaps 0.3 to 0.7 High, adjust to start around 0.7 Short, keep current from 0 to around 0.2 Normal, adjust this include 0.3 as a strong member so maybe 0.2 to 0.4 Long, adjust this range to start higher than 0.4 After making these adjustments you'll want to validate the new setup by testing it with a variety of known ECG waveforms to ensure that it still correctly identifies abnormal patterns when they're present You should also consider consulting with main experts to validate the clinical relevance of these ranges Certainly Based on the guidelines we discussed here is how you can modify the fuzzy logic definitions in your Python code to potentially classify a normal ECG waveform with your specified values as normal Make sure to test the updated system thoroughly to ensure that it behaves as expected across a range of input values This is a starting point and fine tuning may be required to achieve the desired specificity and sensitivity It actually might be in business We're not making any mistakes We did one false alarm but that's pretty much it It's not detecting the normal ECG waveform as abnormal which is good, but we kind of faked it We told GPT-4 to adjust the membership functions to to actually make sure that the specific abnormal ECG waveform that we have does not would not be labeled as abnormal, so will be always labeled as normal So it adjusted the membership functions Obviously the question is How well will it work once we add more waveforms that we want to see And also, well currently already as it is we can see how I'll start making mistakes The bot will be winning And this is the no noise scenario They can start adding some noise to see what happens The hypothesis obviously will be that the robot will start making mistakes as you increase the noise level I can't compete with it already So obviously it does the same task as I am It gets the more waveform values It doesn't have the labels And it's able to classify correctly This one here or something somewhere The log, the terminal This is it I can't do it anymore I think I can push to 1000 score But the bot will just keep going correctly forever No breaks No food No pay It just goes Well, let's just pay for electricity, right? But how much humans gobble up electricity Anyway, I'm done here I can't do it anymore The bot will keep going And obviously it doesn't care about speed as well It does the decision instantaneously Oops, and I'm making mistakes already Yeah, maybe I can If I really push it I can get to 1000 Then it will be really hard for me to keep it there It's the other thing It's the longer-term performance That will suffer If I keep doing the task Because if I stop clicking I think the Pretty sure the score will keep going there So I'm making a lot of Misses already Because it's too fast for me And the bot is going strong And in our case In this session the GPT acted as a cardiologist Well, fake, obviously fake cardiologist Yeah, it did provide disclaimers You can I was able to tune up the parameters for the algorithm to do the classification properly So I don't know where that one false alarm came from Pretty sure it came from where we had the noise in the signal Because the default option there is noise introduced So it will be interesting to see now what will happen once we start introducing noise to the system So just introduce just a tiny bit Yeah, another false alarm Yeah, I'm not sure which one is it not doing correctly, but yes there's essentially too many positive peaks So it's messing up the numbers There's no filter at the moment There would be probably a simple filter would solve this I didn't want to filter so that the human and the bot essentially look at the same waveform So the bot doesn't have any unfair advantage Yeah, so now it's actually classifying everything as abnormal Including the normal waveform That's why the false alarm rate is going up What if we have just the tiniest amount of noise Now it still gets triggered somehow Yeah, this is where it starts picking up errors So that's not great Anyway, we'll continue next time See you in a bit We'll summarize what we did today and say bye-bye to the viewers politely Today, we've tackled a complex task of fine-tuning a fuzzy logic system to analyze ECG waveforms We adjusted membership functions specifically to ensure a particular normal ECG waveform wouldn't be misclassified as abnormal This approach helped reduce false alarm significantly in a noise free environment which is a great start We also touched on the potential for we introduced noise and more varied data hypothesizing that while a human might struggle with increased noise levels, the bulk could potentially maintain high accuracy due to its consistent, tireless analytical capability It's a wrap for today's session We've made solid progress and it's exciting to think about the potential of this system as it gets exposed to more data and scenarios Goodbye for now, and take care