 Keep saying always the same thing over and over again, just say something else when starting a stream, we already did like hundreds of those. So you would think getting better by now, probably not that in a second, it seems like we're using the correct microphones, that should be good. So these streams, if you haven't figured it out already, they're all about the bodycaves.com. Please go check out this site, apologies in advance, it does, it is running ads, music from my hands, Genshia was the last tool that we developed, it's fully in JavaScript, there is no data being sent to the cloud or anything like that, those things in your browser, it's that disadvantage because things tend to run slower that way, but it's advantage for my electricity costs, because the server is not doing anything, as opposed to say the CG2 music converter, where we do have both backend and frontend running, and we just, all these tools are in beta testing mode, yes, sorry for the ads, hopefully get relevant ads to you, that's what the EEG sounds like, wanna check it out, I thought those do the volume, just reload this page, so on reload, it should sound something on reload, no it doesn't, anyway there could be, originally the idea was to have like a play button, but then again just, yeah, brought to you by, no one needs, I don't know why this company needs advertisement, like pretty much on everything, way before I get banned on YouTube for commercial advertising, whatever, no I'm not sponsored by that company, so hopefully I'm allowed to say whatever I want, so anyway, when you have auto volume and auto duration, the duration is pretty simple, it's just, they will adjust it based on what's currently the window size, so if you have a longer window size, the sounds will be longer as well, the whole file is actually buggy and laggy, yeah it's when you actually scroll through the file, it becomes a bit less responsive, I'm monitoring some, I'm on Twitch as well, no one is there, on YouTube which I get the messages from YouTube into my streaming software, but sometimes it doesn't work, so please scream if I don't get a message, and now I'm actually monitoring the audio and everything from LinkedIn, and there is a bit of a lag, so apologies for that as well, anyway, so let's go over it again quickly, when you load the page, it defaults to channel 7, I think channel, one of those last channels was a bit more annoying or more festive, depends how you look at it, the auto volume, yeah let's go back to 7, so if you select auto volume, it will be quieter when there is no seizure, and start being louder when seizure comes through, so this is in the middle of the seizure, and this is the end of the seizure, so it becomes, I don't know if you can still hear anything, but the auto volume will adjust itself to amplitude, it's actually meant to be grayed out, so it's all prototyping like that, when you turn it off, it just maintains the volume to be roughly the same throughout the recording, so it doesn't matter if it's seizure or after seizure, so obviously auto volume is kind of better, and again as I said auto duration adjusts to the current window size, so we have a window size of 10 seconds, the sound is shorter, if you make the window size 50 or something, the notes will be longer, but then it becomes a bit laggy, because there's actually like too much data is being loaded here, and this is all, it's mostly happening in javascript, there is a back end to it as well, and next time we actually modify this tool, we will push things as much as possible to the front end, just so the server electricity costs are not going through the roof, just do another like random one, every time we probably might select a random tool and cover it, this selection of the image is actually not doing anything, it's actually taken from another tool where you could actually click on it and it will do something, in this case it's not doing anything, as per the following explanation, this is a image feature detector, it's using three different algorithms, they're pretty common in the field, fast, iris, and she, Tomasi, and here you have a bunch of example images that you can select, some of them are, yes in this case you can see it's not performing very well, because it's just picking up on the text, specifically the iris algorithm, and the fast does a bit better, but it does pick up the features in the image, but then it's just pretty much selecting everything, in this particular case, and the sheet Tomasi is actually selecting, it's actually working pretty well, because it's selecting those that might be areas of interest, but then it's missing this hemorrhage, so not very good, so none of them is actually working well, so again Harris is just picking the text labels on the image fast, it's just all the shootings and labeling everything as the area of interest, and sheet Tomasi is labeling some useful bits, but potentially missing the most important thing here, which would be the lobar hemorrhage, which is in the name of that file, so I assume that's correct, and you have some normal MRIs with, I assume, higher contrast, the sheet Tomasi is doing pretty well, it's labeling some of the areas of interest quite well, and Harris is also not doing too bad, and fast is just labeling pretty much the entire image, which is not very useful, so you could play around with it, see which one you like better, they obviously work differently depending on the quality of the image, and so on so forth, so we might be doing more work on this later on, we have a bunch of articles, what are they called, blogs, yeah articles, not really blogs on the website, yes they were generated using GPT-4 via the OpenAI website, and images generated by Dali 3 beta, it was quite a while ago, yes this article probably will need some explaining, some people say that the articles are no good, text generated by GPT can not be used for research, but that's not quite accurate in this case, it was actually providing relevant references, I'm not sure, that's why it says further reading, and not the actual reference to the text, because yes it's a large language model, so you don't actually know where the text was taken from, but then GPT-4 has also a web search capability, so it can actually search for relevant articles, sorry for that, just trying to sort out my casino windows, so I'm only sharing one screen, I have another two screens, and I have like five or six pages open, it started developing in a second, so you have any questions, comments, I don't particularly care for likes and follows and things, but I don't normally just look in the YouTube analytics, I do a primarily look at watch time, watch hours, which is pretty impressive probably doing a better than a university lecturer or something, maybe, maybe not, I don't know if that's a common KPI, okay so let's move on to the main content for this stream, which is this ECG labeling game, we added an option to add noise, add body folders, like some minimal amount of noise, if you move the slider all the way to the right you get a lot of noise that the QRS complex is barely visible, and in this case we expect the bot to start making more mistakes, I wouldn't know which one, which one is it, the bot seems to be struggling as well, yeah it's creating all this false alarms, that's because it keeps clicking on everything, so I wouldn't know which one of those waveforms is an actually normal ECG waveform, the abnormality score remains 0.73, so it's labeling everything as abnormal, so it keeps essentially clicking on everything if it was a human doing the task, and when there is a hit it clicked correctly on abnormal waveform, when it's a false alarm this means this is a normal waveform, that's just how it looks like, yes a human won't be able to recognize it, but the bot is not able to recognize it either, so but once we start reducing the noise level, let's say put it half way, now a false alarm, I can barely tell still a, I think for a human it will still be above a threshold in terms of being able to detect if this waveform is normal or not, it keeps making mistakes, okay if we reduce noise entirely this would be a normal and it says abnormal, I don't know why, hopefully next time it gets the same waveform it gets it correct, yeah this is wrong again, but the number is reducing it's 0.51, okay it's not doing very well is it, I would expect this number to go lower over time, there's something to do with the thresholds, we're seeing something like 0.3, 0.4 last time for a normal ECG, but we don't get that anymore, it's a bit unusual, we can start it over again, see how it does, yeah we expected 0.51 for a normal ECG to be slightly lower to be well below 0.5 for sure, let's start again, we have this minimal noise on the ECG waveform, we get 0.7 for abnormal wave and 0.7 for a normal one, so we still get a false alarm for it, it's really not doing well with the noise 0.7, yeah I keep getting false alarms, so essentially labeling normal a waveform as abnormal, that's no good, I don't know if it will go any lower, I assume this is the noise throwing it out, throwing the algorithm off, I have to check probably without any noise at all, yeah we get a very high number or normal ECG, so it's labeling it as abnormal, okay so we get 0.59 for this one which is obviously abnormal and that's labeling it correctly, 0.8 for that one which is missing and negative peak which is fine, 0.7 for this one that is missing the first positive peak, we get 0.51 now for a normal waveform but it's not low enough to be labeled correctly by the algorithm, okay to look at the code again, I suspect it would be something like the thresholds might not be correcting themselves, so they like saturated at something, so we popped all this code into GPT-4, let's break it down into two parts, individual code file review and the integration, yeah more worried about the integration but let's go all right one by one, your HTML structure seems well organized with clear sectioning for the game canvas, game rules and the ECG waveform analysis challenge, okay ensure that the commented out sections like the alternative noise slider or the CSS link are either needed for future use or should be cleaned up to keep the code baseline, that's fine, the set time out function for loading the Google Adsense script is a good approach to ensure it doesn't impact the initial load time of the page, yeah that's the ads, the Google Ads are only loading after like 10 seconds or something after loading the page, this hopefully be using the Ava, yeah and by the way I get an ad for whoop a wearable, just this medical wearable whatever, so this video brought to you by yeah my camera is actually covering that image, which is probably good because it's not a sponsored video or anything, do I change the camera, camera is connected, mine still works, not start video so I actually have a second camera just for testing that doesn't want to connect, double check for a sec, that's the one, yeah I don't know if you tried it out, if not please go try it out, I manage, select the second camera, anyway it doesn't work, should work for you if you're not using the camera for something else, I do have two connected but it still doesn't work, anyway yeah if I'm start messing around with the USB connections probably both cameras will stop working with a quick go in another browser, yeah the problem they're called the same name, two cameras probably should change it somewhere in the settings, no yeah maybe the problem is that the two cameras just have the same name so that wouldn't work in waveforms js the waveform generation logic appears sound the use of mathematical functions to simulate ecg waveforms is clever, the variations in waveforms like qr's variation 1, no p wave, add good complexity to the game, yeah we might later add actually more waveform types, those are generated in javascript, it's probably important to note that the waveforms are only generated in javascript whereas the fuzzy logic the bot is working in the background, so in the backend, so the bot cannot possibly cheat because it's only seeing the raw data, it doesn't know how the data was generated so it doesn't have the labels or which signal is what currently we still have trouble with this 0.51 value, just too high should be at least below 0.5 for the abnormality score or the decision to be correctly say normal for a normal ecg wave we can artificially correct that but yeah we don't want magic numbers, we want a like a permanent solution for this, in game underscore logic js the game loop and event handling is set up correctly, the calculation for the score based on hits, misses, false alarms and correct rejections follow standard gaming logic, it's okay, analyze underscore waveform in game underscore logic js correctly posts the data to your flask server for analysis, just ensure the server is correctly set up to handle these requests, you're using flask, a robust framework for web applications, the structure of your flask app seems standard, the analyze underscore waveform route is correctly set up to receive post requests, your use of numpy, skippy and sk fuzzy for waveform analysis and fuzzy logic is appropriate for ecg waveform analysis, the global variables for min, max tracking are a smart approach for normalizing data across multiple requests, yeah that's fine, the fuzzy logic rules in fuzzy underscore logic underscore analysis seem well thought out, especially the combination of different ecg features, frequency, amplitude, peaks, the determine abnormality, right so on this integration between the different files so javascript, javascript to python communication, your javascript sends waveform data to the flask server, analyze underscore waveform endpoint for analysis, ensure cause is handled if the front end and back end are served from different domains, ports, yeah i don't think i have any cause issues, data flow consistency, ensure the data format sent from javascript matches what the python server expects, consistent data handling like waveform array structures is crucial, okay there's no specific issues with that one i would think, asynchronous handling, javascript fetches the analysis result asynchronously, ensure that your games logic correctly handles these asynchronous responses especially on the different network conditions, um okay it's again nothing specific, testing extensively test the integration, check if the python back end correctly receives data from the front end, processes it and returns the expected results, okay can we do some rapid fire quick responses, can you look at the whole code give a quick summary of how the fuzzy logic algorithm is making its decisions, could you primarily focus on how a normal ecg waveform is being detected, the reason i'm asking is that for a normal ecg waveform i'm getting an abnormality score that seemed to be slightly too high, how can we fix this, it's actually grabbed the number 0.8 abnormality score for this which is fine, it looks almost normal but it doesn't have any negative peaks that's why the score is elevated that's okay, this is the abnormality score that i get for a normal ecg waveform that could be an issue if the range of values, so the problem is that they don't go low enough, are they meant to be between 0 and 1, make that last one a question let's pop this in see what it says, sure let's jump right into it, here's a quick summary of how the fuzzy logic algorithm in your code is making decisions especially regarding the detection of a normal ecg waveform, input variables the algorithm uses four input variables frequency amplitude positive peaks and negative peaks, these are derived from the ecg waveform data frequency has low, normal, high, positive underscore peaks and negative underscore peaks have too few, normal, too many, amplitude has low, medium, high, a high or low frequency, or too few, too many peaks indicates a likely abnormality, normal frequency and the normal number of peaks suggest unlikelyhood of abnormality, aggregation and defuzzification, the algorithm aggregates these rules to compute an abnormality score ranging from 0 to 1 and categorizes the ecg as normal or abnormal, if normal ecg waveforms are getting an abnormality score of around 0.513, this suggests the algorithm is somewhat uncertain about these being normal, this could be due to overlapping membership functions, if the membership functions for normal characteristics overlap significantly with abnormal ones, even normal data might be partially classified as abnormal, rule waiting, the way rules are set up might be giving too much weight to certain features, adjust membership functions, refine the membership functions to reduce overlap, ensure the normal ranges in your membership functions accurately reflect what is typically considered normal in ecg waveforms, review and adjust rules, reassess your rules to ensure they accurately reflect clinical standards for normal and abnormal ecgs, adjust the weights if necessary, normalization ranges, check the normalization process for your input variables, ensure the global min, max values are set correctly and are updating appropriately, thresholds and scaling, if the scores are consistently higher than expected, consider adjusting the scaling or introducing a threshold below which waveforms are considered normal, remember fine tuning a fuzzy logic system can be a bit of trial and error, especially when dealing with complex data like ecg waveforms, keep tweaking these elements until you get a more accurate representation of normal and abnormal ecgs, okay, we don't want to be like tweaking them and most people with machine learning algorithms just keep tweaking everything, the rules meant to just make sense, okay, so regards the solution, can you actually look at the code provided, look at the membership functions and suggest how to refine the functions to reduce overlap, I thought there should be a consistent level of overlap, we would like to make sure the output, what's called the abnormalities score goes all the way to zero or close to zero, regards the rules, can you check if we are currently using any weights, I don't think so, generally, yes, we would like some parameters to have less weight, so for example, it will make sense to have less weight to the amplitude and more weight to the number of positive and negative peaks, the frequency should also be lower weight, now regards normalization ranges, yes, we would like to check if the normalization process for your input variables ensure the global mix, max values are set correctly, okay, so we're actually setting them to nans to begin with, to not available, this was working better, would you suggest changing them, there would be some common values that we expect that we could be using instead, this seemed to make a lot of difference, also, could you explain the repeatability, so would the abnormalities score be different every time we run the web application, for the threshold and scaling, yes, I suspect you are suggesting to adjust them manually, we could have done so except the normal ECG abnormalities score is very similar to the value for and one of the abnormal waveforms, we would like to fix that, now main question, the usual question, are you going to be generating the code or do you prefer to provide ROMs for github co-pilot to use, if I, if you didn't understand my voice, it's probably going to be wrong in the YouTube the transcription as well, all right, let's dive into each of your points based on the provided code, looking at your membership functions, they appear to be fairly standard, however, if normal ECG waveforms are getting abnormalities scores higher than expected, tweaking these functions could help, reduce overlap, adjust the ranges in your trim functions to reduce overlap, especially between normal and abnormal ranges, for example, if the normal range for frequency is 0.5, 0.6, 0.7, ensure that the low and high ranges don't encroach too much into this space, normalization and scaling ensure your input data scales correctly into these membership functions, the global min max values are critical here, using NANDs initially is okay as they get updated with actual min max values from the data, common expected values can be used as a starting point, but real data shouldn't form these ranges for accuracy, your current setup doesn't explicitly use weights for rules, to introduce weighting, you can modify the impact of a rule by using or and operators strategically, for instance, using in between two conditions makes the rule more specific, to prioritize certain features, like the number of peaks over amplitude, you might consider adding more rules that focus on these features or adjusting existing rules to emphasize their impact, the abnormality score may vary each time due to the nature of fuzzy logic and the variability in input data, each run could slightly differ, especially if there's noise or variability in the ECG waveform data, however, the score should be generally consistent for similar types of waveforms, manual adjustment can be tricky but necessary, if normal and abnormal waveforms are getting similar scores, this is a clear sign that the system needs recalibration, it's not just about adjusting the membership functions or rules, but also about ensuring the input data is correctly normalized and represented, I can certainly provide code snippets to guide you in adjusting the membership functions and introducing rule weights, however, for a complete overhaul or complex adjustments, using a platform like github co-pilot might be more efficient, as it can offer real-time coding assistance based on your specific needs and context, let's start with some code suggestions, remember, these are just starting points, you need to test and iterate on these adjustments to find the right balance for your specific application, what is it we currently have for frequency, the problem with frequency is that we only essentially have two values, let's down quickly 0.513513 and let's just reload the tool so it's faster as well, yet that level of noise should not impact the result too much 0.76 and when you remove noise expect this number, save this number going lower 0.513, yeah it's that same 0.513, 0.7775, yeah that's for normal, well at least it's behaving as expected, I'm not sure this will necessarily help, the problem with the frequency, we have three or five I think, yeah well it kind of makes sense because it's a single waveform, a single PQRSD complex, so there's no much variability in it anyway, so I think when we have all the positive and negative peaks, I thought this number should go to five, was it only happening when there's no noise, so this will generate 303 which is normalized to 0.4 and on still three, might be something wrong in terms of when the noise is added and then being removed, yeah the scaling, doing something funny because of, ah now it went to five, I don't think this will do much, I don't even know why it's suggesting that, now rule waiting, yeah that will be more likely to help, so why is that for, let's keep it there, it's a rule one, this rule gives more weight to peaks, count then amplitude and that I expect to actually make a lot of difference, let's restart this quickly, yeah receive a 0.8 for the first one, then 0.1, yeah so now it's doing much better, so that suggestion helped to explain this quickly, condition saying that the number of positive peaks is either too few or too many and the amplitude is not high, then the rule is triggered, the operator, yes we have all, meaning that if I have conditions, then we have and then we have and operator, yeah that's fine, have to be true with the whole statement to be true, this operator is the logical note operator in the fuzzy logic, why are we negating that, all right we actually changed, yeah we changed the rule entirely, so we have, originally it was positive peaks too few or too many, then we're also adding and amplitude is too high, yeah we could add low as well, but this seemed to be working much better, let's see in theory when we add more, all right we did nine misses, as it was, and now it's misbehaving, let's see if we reduce the noise all the way to zero, right, labeling this one as abnormal, we expect the false alarm and the red ones, misses and false alarm to remain at what they are zero and eleven, not to go any higher, yet the higher this abnormality score, the more abnormal the ECG waveform is, right, so it's doing well and then you can see how by increasing the noise the robot starts making more and more mistakes, so hopefully this is a useful tool to learn how fuzzy logic can be used in biomedical data signal feature detection, yeah we get the relatively a very low number at 0.12 or a normal ECG is what we want and that just was achieved by tweaking the rules, this is normal you get 0.12, so on the other hand I had like a video telling how you know much better the bot is, on the other hand if you have both humans instead of turning it into a competition, like over here, we can give this abnormality score to the human expert to try and help the decision making, so now in addition to looking at the waveform I can also look at the abnormality score that the machine learning algorithm is providing, I can make a better decision, so if it's still, if this abnormality score is slightly elevated then you know I can pay more attention, currently the bot doesn't seem to make any mistakes, the eleven misses what there when we increase the noise level, so now the real question is there would be another very interesting thing to do is see actually how human and machine compare when there is noise added to the signal, essentially when there, let's see if we add a little bit of noise, so I can tell that's abnormal, it's actually harder for me to tell if something is normal, see if the machine will start making mistakes, yes so they've missed one signal, yeah it keeps doing misses, so I actually have a chance now, so yeah interestingly enough when noise is being introduced to the waveform, I oops made a mistake, I'm still able to do the task and the machine starts making mistakes, this is an interesting case when now I'm actually waning, waning the machine when the noise is the elevated, it's getting faster as well, humans tend to get distracted it doesn't help, but the bot is making a lot of mistakes, so the algorithm needs to be tweaked, yes now there is essentially the opposite problem of, okay so now changing rule one solve the problem when there is no noise it's working really well, however when there is noise all most abnormal ECG waveforms are labeled as normal, which is no good and this is the change that was made, it's great to hear that the system performs well without noise, however the issue with high noise levels causing misclassification of abnormal ECG waveforms as normal is a critical one, here are a few strategies to address this, if the noise significantly affects the waveform you might need to adjust the membership functions to account for this, for instance the noise could be affecting the amplitude or peak counts leading to misclassification, tweaking these membership functions to be more resilient to noise could help, consider introducing a mechanism to evaluate the level of noise in the waveform and adjust the fuzzy logic decision making accordingly, this could be as simple as a preprocessing step that estimates the noise level and uses this information in the fuzzy rules, the current rule modifications might not be sufficient to handle noisy data, you may need to add new rules or further modify existing ones to better classify waveforms in noisy conditions, for example you could introduce rules that specifically address scenarios where the noise level is high, since noise significantly impacts performance reevaluate the weighting of your rules, it's possible that in noisy conditions certain features like the number of peaks become less reliable so their impact on the decision making process should be adjusted, implement a threshold for the noise level, if the noise level in a waveform exceeds this threshold the system could automatically classify the waveformers needs further analysis or unclassifiable instead of forcing a normal abnormal classification, thoroughly test the system with various levels of noise, this will help you understand how different noise levels affect the classification and what adjustments are most effective, here's an example of how you might implement a simple noise handling mechanism in your rules, remember handling noise in signal processing especially in a complex domain like ECG waveform analysis can be challenging, it may require iterative testing and refinement to achieve the desired level of accuracy, well I don't know if you mentioned this already but the obvious problem is that when we add noise the number of positive and negative peaks shoots through the roof and obviously that algorithm doesn't work anymore, now I less inclined to do any filtering, I want the algorithm to see to actually see the same data as what the humans see, so now the question is I well assuming I'm a human still able to do that ask quite well if there is some added noise to the signal, however the machine fails already, now the question is how do I do that, well the main difference is that I don't measure the I don't see the peaks that were generated by the noise added to the signal as the negative or positive amplitudes, peaks, I think it will make sense to look at when counting peaks to actually look at the a certain percentage of the entire waveform or for the peaks to be detected, I think we're already doing this in the code, can you double check, the issue you're describing is common in signal processing especially when dealing with real-world data like ECG waveforms, when noise is added it can cause a significant increase in the number of detected peaks leading to misclassification by the algorithm, the key difference as you pointed out is that humans can often distinguish between peaks caused by noise and those that are part of the actual ECG signal while the algorithm may struggle with this distinction, in your current code the peak detection is done using skippy signal find underscore peaks, the high parameter is used to identify peaks which is set to one, this means the function will detect all peaks that are higher than one unit, in the presence of noise many small fluctuations might exceed this threshold leading to an overestimation of the number of peaks, to address this you can adjust the peak detection to consider a percentage of the overall amplitude of the waveform rather than a fixed height, this approach can help to filter out small fluctuations caused by noise and focus on more significant peaks, here's how you might modify the peak detection, in this modified approach the height parameter for find underscore peaks is set dynamically based on a percentage of the maximum amplitude of the waveform, this makes the peak detection more adaptive to the actual waveform characteristics and can help to reduce the impact of noise, it's essential to test this new approach with a variety of waveforms both with and without noise to find the optimal threshold percentage, you might need to iterate over this value to find the best balance between accurately detecting peaks and ignoring noise induced fluctuations, remember fine tuning these parameters is key to improving the algorithm's performance in noisy conditions, it's a process of trial and error guided by your understanding of the ECG data and the specific challenges you're facing with noise, I think that it has all this context it's really like talking to a phd student or something that actually knows way more than you are even if you are meant to be an expert in the field but yes this is a great suggestion to say the least really impressed so what it's suggesting to do is instead of looking at the specific height of the signal when finding peaks we're doing like a percentage so in this case looking at the 20 percent of the maximum amplitude for the threshold I think this will be more similar to what a human does comment that out and now we expect yeah the numbers are smaller the question is he's 20 percent so obviously if we make the noise larger this negative and positive peaks will go and will become larger numbers as well so in this other question is what's a reasonable number I would say yeah so so yeah this is a bit of a like this number is like four percent a 40 percent will happen is yeah for this level of noise it's a correctly finding four three so what's making mistakes it's 13 what it doesn't actually still finding four whereas it should be three in this case there are three which is correct and there should have been one negative one that it's missing found 51 that one that's obviously incorrect that number should be in this modified approach the height parameter for fine underscore peaks is said dynamically based on a percentage of the maximum amplitude of the waveform this makes the peak detection more adaptive to the actual waveform characteristics and can help reduce the impact of noise sure what that number the threshold percentage is essential to test this new approach with a variety of waveforms both within without noise to find the optimal threshold percentage you might need to trade over this value to find the best balance between accurately detecting peaks and ignoring noise induced fluctuations I'm just suggesting that noise is too much yeah how's it 13 the original number minimize the noise it's fine it's still incorrect if no noise with the original algorithm you get two and one that's right get three and one which is correct instead of a this is giving some arrows how about looking at the median of the signal and taking percentage of that for the number of negative and positive peaks so essentially we like to look at the baseline find the baseline and then do peaks as a percentage above the baseline adjusting the peak detection strategy to consider a baseline value such as the median of the signal is a good approach the median is a robust measure of central tendency less affected by outliers and noise making it suitable for establishing a baseline in noisy data by detecting peaks as a percentage above this baseline you can more effectively filter out noise induced fluctuations here's how you can implement this approach find the median baseline calculate the median of the waveform data this represents the baseline set a percentage threshold determine a percentage threshold above the baseline for peak detection detect peaks relative to baseline test different thresholds you may need to experiment with different percentage thresholds to find one that best suits your data handling variability ecg waveforms can vary significantly between individuals and under different conditions ensure that the chosen threshold works well across this variability compact on fuzzy logic after adjusting the peak detection strategy you might need to revisit your fuzzy logic rules and membership functions to ensure they're still effective with the new peak detection approach validation validate this approach with a range of ecg waveforms both noisy and clean to ensure it effectively discriminates between real peaks and noise induced fluctuations this method provides a more dynamic way of detecting peaks in the presence of noise and should help in improving the classification accuracy of your algorithm this one out but this one didn't really work just the percentage of the maximum level can I just have to finish it another time so I'll say bye bye for now and we'll keep testing don't forget to check out by chaos.com and provide your feedback very important to us adjusting the peak detection strategy to consider a baseline value such as the median of the signal is a good approach the median is a robust measure of central tendency less affected by outliers and noise making it suitable for establishing a baseline in noisy data by detecting peaks as a percentage above this baseline you can more effectively filter out noise induced fluctuations here's how you can implement this approach calculate baseline and adjust peak detection find the median baseline calculate the median of the waveform data this represents the baseline set a percentage threshold determine a percentage threshold above the baseline for peak detection detect peaks relative to baseline python copy code import numpy as np import skippy signal hash assuming np underscore data is your waveform data hash calculate the median baseline of the waveform baseline equals np median np underscore data hash set a percentage threshold above the baseline for peak detection percentage underscore threshold equals 0.2 hash for example 20 percent hash calculate the absolute threshold value based on the baseline threshold underscore value equals baseline plus baseline asterisk percentage underscore threshold hash adjust peak detection to consider this threshold positive underscore peaks underscore equals skippy signal find underscore peaks np underscore data height equals threshold underscore value negative underscore peaks underscore equals skippy signal find underscore peaks np underscore data height equals considerations test different thresholds you may need to experiment with different percentage thresholds to find the one that best suits your data handling variability ecg waveforms can vary significantly between individuals and under different conditions ensure that the chosen threshold works well across this variability impact on fuzzy logic after adjusting the peak detection strategy you may need to revisit your fuzzy logic rules and membership functions to ensure they're still effective with the new peak detection approach validation validate this approach with a range of ecg waveforms both noisy and clean to ensure it effectively discriminates between real peaks and noise induced fluctuations this method provides a more dynamic way of detecting peaks in the presence of noise and should help in improving the classification accuracy of the algorithm instead consider setting the threshold as a percentage of the overall range max min of the waveform added to the baseline this can better account for the amplitude variations typical in ecg data here's how you might modify it in this revised approach baseline the median of the waveform data is used as a stable reference point baseline waveform range the difference between the maximum and minimum values of the waveform represents the overall amplitude range threshold calculation the threshold for peak detection is set as a percentage of this range added to the baseline this method is more reflective of the actual signal dynamics especially in the presence of noise by basing the threshold on the overall range of the waveform rather than just the baseline value this method provides a more adaptable and robust way to handle the variability and noise in ecg waveforms be sure to test this approach with various data samples to fine tune the percentage threshold for optimal peak d based on the values you've provided baseline 6.28 waveform range 361.65 threshold value 78.61 it appears that the threshold value is significantly higher than the baseline this could potentially lead to fewer peaks being detected especially if your ecg waveform data generally fluctuates around the baseline with a smaller amplitude threshold relative to baseline the threshold of 78.61 when compared to a baseline of 6.28 is quite high this threshold might be too strict for detecting smaller yet clinically significant peaks in the ecg waveform waveform range influence the large range 361.65 suggests that there are some very high or very low values in your data possibly outliers or noise this large range significantly impacts the threshold calculation you might want to adjust the percentage threshold or the way you calculate the threshold to make it more sensitive to smaller peaks reduce percentage threshold if the current threshold is too high reducing the percentage eg from 20 to 10 percent or lower might help alternative approach use a fixed value instead of a percentage of the range calculate the threshold based on a different statistical measure like the interquartile range that's less sensitive to outliers here's an example using a lower percentage threshold or using a fixed value testing different scenarios it's crucial to test with various ecg data including those with different noise levels and typical ecg features clinical validation if possible get these thresholds and detections validated by a clinical expert to ensure their line with clinical expectations remember the key is to find a balance where the threshold is sensitive enough to detect real ecg peaks but not so sensitive that it picks up noise fine tuning this will likely require iterative testing and adjustment to address the issue of noise affecting the peak detection algorithm it seems clear from the images you've provided that the noise is causing an overestimation of peaks to modify the code to better handle this without introducing arbitrary fixed values we should utilize percentages that relate to the signals characteristics peak height relative to median since the median is a robust measure against noise you can use it to set a dynamic threshold for peak detection consider the median absolute deviation mad mad is a robust measure of statistical dispersion in essence mad is the median of the absolute deviations from the median of the data set it can serve as a measure to set the threshold for peak detection to be more resilient to noise here's a code snippet showing how you could adjust the peak detection using the median and mad mad multiplier the multiplier for mad is often set to three to detect outliers in statistics which aligns well with the idea of determining significant peaks above the noise level threshold above median this threshold adapts to dispersion of the data making it less sensitive to noise while still detecting true peaks you would still need to validate this approach with your ecg data on the various noise conditions to ensure it works as expected the multiplier for mad can be adjusted based on the level of noise resilience you wish to achieve this approach avoids magic numbers by utilizing statistical measures that dynamically adjusts the given data set providing a more reliable method for peak detection in noisy environments to address the issue of noise affecting the peak detection algorithm it seems clear from the images you've provided that the noise is causing a no-vestimation of peaks to modify the code to better handle this without introducing arbitrary fixed values we should utilize percentages that relate to the signals characteristics suggested approach peak height relative to median since the median is a robust measure against noise you can use it to set a dynamic threshold for peak detection consider the median absolute deviation mad mad is a robust measure of statistical dispersion in essence mad is the median of the absolute deviations from the median of the data set it can serve as a measure to set the threshold for peak detection to be more resilient to noise python code snippet here's a code snippet showing how you could adjust the peak detection using a median and mad python copy code import numpy as np import skippy signal hash assuming np underscore data is your waveform data hash calculate the median of the waveform data to establish a baseline median underscore of underscore waveform equals np median np underscore data hash calculate the median absolute deviation mad mad equals np median np dot abs np underscore data median underscore of underscore waveform hash set a threshold for peak detection based on a percentage of the mad mad underscore multiplier equals three hash this is a commonly used multiplier in statistics threshold underscore above underscore median equals median underscore of underscore waveform plus mad asterisk mad underscore multiplier hash now use this threshold for peak detection positive underscore peaks underscore equals skippy signal find underscore peaks np underscore data height equals threshold underscore above underscore median negative underscore peaks underscore equals skippy signal find underscore peaks np underscore data height explanation mad multiplier the multiplier for mad is often set to three to detect outliers in statistics which aligns well with the idea of determining significant peaks above the noise level threshold above median this threshold adapts to the dispersion of the data, making it less sensitive to noise while still detecting true peaks. Validation You would still need to validate this approach with your ECG data on the various noise conditions to ensure it works as expected. The multiplier for mad can be adjusted based on the level of noise resilience you wish to achieve. This approach avoids magic numbers by utilizing statistical measures that dynamically adjust to the given dataset, providing a more reliable method for peak detection in noisy environments. Creating an advanced web application, especially one that aims to be the best in its field for detecting ECG abnormalities using fuzzy logic, is a complex and iterative process. It typically involves a team of experts including software developers, biomedical engineers and medical professionals, all working together. While I can provide guidance and suggestions, actual development and iterative improvements require active involvement from the development team and subject matter experts. Here are the steps and considerations for further development. Iterative development and testing, implement Implement changes in small increments Just each change thoroughly with a diverse set of ECG data Gather feedback from both the system output and potential medical users Continuously refine the fuzzy logic rules and membership functions Adjust peak detection algorithms to improve noise handling Design an intuitive and user-friendly interface Ensure the UI presents information clearly and allows easy interaction Implement robust security measures to protect sensitive health data Try with relevant regulations like HIPAA or GDPR Work closely with healthcare professionals to ensure clinical validity Incorporate their feedback to align the application with real-world needs Optimize the back-end for fast and reliable analysis Ensure the application can handle a high volume of requests Scalability Design the system architecture to be scalable to accommodate growth Use cloud services and load balancing if necessary Documentation and training Provide comprehensive documentation for users and developers Offer training resources to help users understand the application Regulatory approval If the application is to be used clinically, see regulatory approval Engage with bodies like the FDA for approval processes as needed Set up a process for ongoing maintenance and user support Plan for regular updates based on user feedback and technological advances Given the complexity of the task, the actual code development, implementation, and refinement should be carried out by a dedicated development team with the appropriate technical skills and domain knowledge As an iLanguage model, I'm here to provide guidance where the hands-on development work would be beyond my capabilities MAD Multiply The multiplier for MAD is often set to 3 to detect outliers in statistics which aligns well with the idea of determining significant peaks above the noise level Threshold above median This threshold adapts to the dispersion of the data making it less sensitive to noise while still detecting true peaks You would still need to validate this approach with your ECG data on the various noise conditions to ensure it works as expected The multiplier for MAD can be adjusted based on the level of noise resilience you wish to achieve Alright, let's lighten the mood a bit and tackle this peaky situation Smile When it comes to counting peaks in the midst of a noisy party it's like trying to find genuine fans for pop concert, the there, but they're drowned out by the screamers Your fuzzy logic system is currently acting like an over-excited fan counting every little cheer as a hit song Your algorithm is currently like a hype-active jumping bean seeing peaks everywhere Let's turn it into more of a discerning critic focusing on the quality, size, but the peaks rather than quantity Let's refine the peak detection algorithm to be a bit more snobby It should only care about the peaks that stand out in the crowd Here's a revised approach Use the median It's like the calm friend who isn't phased by the noise Use it to establish a baseline Employ the median absolute deviation, MAD It's the bouncer of our club, deciding what level of peak gets to pass through Filter peaks by size, only the tallest, largest amplitude, peaks give VIP access With this new setup, you should throw a few test waveforms into the mosh pit and see how well the bouncer, algorithm, can pick out the true fans, significant peaks Adjust the MAD multiplier to make sure it's not letting in any riff-raff, noise-induced peaks Waveforms are unique, like snowflakes, each person's ECG has its own groove Noise is annoying, but it shouldn't stop the show A good algorithm keeps the party going Validation is key, always check with the VIPs, clinical experts, to make sure you're not turning away celebrities, clinically significant peaks By focusing on relative peak size and filtering out the noise, we can help the algorithm chill out and become more discerning Just like a seasoned DJ knows the real hits, your system will learn to recognize the peaks that truly drop the beat Keep tweaking until the system's scoring hits the top of the charts Musical note Understood, let's get back to a more technical perspective If the peak detection is still not performing optimally, even after implementing the modifications based on the median and MAD, it might be time to consider other statistical approaches that can help discriminate true peaks from noise-induced fluctuations Adjust the MAD multiplier, the current multiplier of 3 might be too high or too low depending on the noise characteristics of your data You'll want to fine-tune this value based on the noise level Dynamic thresholding, instead of a fixed multiplier, consider a dynamic approach that adjusts the threshold based on the noise level of the current segment of the ECG Peak prominence, this is a measure of how much a peak stands out due to its intrinsic height and its location relative to other peaks Skippy, signal, find underscore peaks can use the prominence parameter to help distinguish real peaks Wave or transform, this advanced signal processing technique can help the noise the signal and make peak detection more accurate In this revised code, we're using the standard deviation to calculate the prominence required for a peak to be detected The value of 1.5 is arbitrary here and should be fine-tuned according to your specific data Testing, after implementing this change, you need to test the system again with noisy and clean signals to evaluate performance Validation, it's critical to validate the results with a domain expert to ensure the peak detection algorithm is identifying the correct peaks Keep in mind that finding the right parameters for peak detection in noisy data is often a process of trial and error, and may require a deep understanding of the data you're working with It's also a good idea to review the data pre-processing steps to ensure that they're optimal for the peak detection algorithm to work effectively In this revised code, we're using the standard deviation to calculate the prominence required for a peak to be detected The value of 1.5 is arbitrary here and should be fine-tuned according to your specific data Great to hear that the prominence based approach is yielding better results To avoid, magic numbers, and instead use a percentage, you can relate the prominence to a measure of the overall signal variability, like the standard deviation If you want to base prominence on a percentage of the maximum amplitude, that could look something like this In this code snippet, prominence underscore value is set to 5% of the maximum amplitude of the signal The exact percentage would need to be determined based on your specific ECG data characteristics and the level of noise present As for a common number to use for prominence without fine-tuning A common starting point could be around 5% to 10% of the maximum amplitude This is often low enough to detect significant peaks, but not so sensitive that it detects noise However, this is a general suggestion and may not be optimal for all datasets Another common approach is to use a multiplier of the standard deviation of the noise If you know the noise level of your signals, you can set the prominence to be 2 or 3 times the standard deviation of the noise Here's how that could look in code In this example, flat underscore segment underscore indices would be the indices of your ECG data that you believe represent a flat or noise-only segment This approach helps to dynamically set the prominence value based on the actual noise characteristics of your data If we aim to replicate the way a human would assess an ECG waveform, we would focus on significant deviations from the expected waveform pattern Humans have the ability to discern patterns and ignore irrelevant variations, including noise To emulate this, we need a dynamic and adaptive approach that considers the relative changes within the waveform itself One way to do this is to adjust the prominence based on a rolling window of the waveform where we evaluate the local behavior of the signal rather than the entire signal at once This method allows the detection criteria to adapt as the signal progresses, which is closer to how a human would visually scan the data Define a rolling window, this is a segment of the waveform that moves along with the data acquisition The size of the window should be large enough to cover a full heartbeat cycle to capture the local variability effectively Calculate local statistics, within this rolling window, calculate local statistics such as the mean and standard deviation Set prominence dynamically, use these local statistics to set the prominence for peak detection dynamically Adjust the window size to match the tickle duration of one heartbeat cycle in your ECG data Prominence multiplier, the multiplier of the local standard deviation, 1.5 in the example, can be adjusted based on empirical observations and testing to match human detection as closely as possible Edge effects, this method will not calculate prominence for the edges of the signal, first and last window underscore size samples You may need to handle the edges separately or accept that the first and last segments of the waveform will not be analyzed for peaks By using a rolling window approach, the algorithm can adapt to the local characteristics of the signal making it more robust to noise and more aligned with how a human interprets an ECG waveform Testing and validation are crucial to fine-tune the parameters and ensure the system's accuracy Understood Keeping the peak detection simple and more in line with how a human would visually interpret the waveform without additional complexity is a valid approach Humans typically recognize peaks by their relative prominence within a certain context of the waveform without necessarily calculating standard deviations or using sliding windows We can simulate this by establishing a baseline threshold for peak detection based on a percentage of the overall signals characteristics rather than a rolling window Here's a straightforward method Baseline determination, use the median to establish a baseline as it is resistant to outliers and represents a central tendency of the waveform Peak relative height, set a prominence threshold based on the median absolute deviation, MAD, from the median, which provides a measure of variability that's less sensitive to extreme values than standard deviation Prominence is a multiple of MAD, set the prominence threshold as a multiple of MAD This is a kinder human looking for peaks that are notably higher than the surrounding noise of the waveform MAD Multiplier, the MAD multiplier can be adjusted based on your dataset and how a typical user would distinguish peaks from noise A value of 2 is a starting point but could be increased if too many noise-induced peaks are being detected or decreased if not enough real peaks are being identified Testing and validation, it is crucial to validate this approach with ECG waveforms under various conditions, including different levels of noise, to ensure the prominence level is set appropriately Human interpretation, keep in mind that while this approach is simplified, human interpretation is inherently subjective Different experts may disagree slightly on what constitutes a peak, so your goal will be to match the consensus as closely as possible This method should align more closely with the principle of Occam's razor, where simpler solutions are preferable, avoiding unnecessary complexity in favour of straightforward, robust solutions Certainly Here's a summary of our conversation and some forward-looking thoughts on the development of a fuzzy logic ECG waveform analysis application Objective, the goal is to refine an ECG waveform analysis application using fuzzy logic to detect abnormalities Challenges, initial Initial peak detection was influenced too much by noise, leading to false detections The aim is to fine-tune the peak detection algorithm without complex statistical methods, giving it as close as possible to human interpretation Adjusting peak detection algorithms using the median and mad to set a dynamic prominence threshold Considering a rolling-window approach, which was later deemed too complex The suggestion to keep the detection method simple by using a straightforward prominence threshold based on a multiple of MAD, which is resistant to noise and outliers Adjustments, it was proposed to adjust the peak detection method to more closely replicate human visual assessment, with an emphasis on simplicity and avoiding magic numbers or arbitrary thresholds Implementation, code snippets were provided to implement these adjustments, focusing on the prominence of peaks relative to the median and mad at the waveform Advancements in I, we can anticipate further integration of machine learning and AI, including deep learning techniques, which could provide more nuanced and accurate detection of ECG abnormalities, potentially outperforming traditional fuzzy logic approaches Wearable technology, as wearable health monitoring devices become more prevalent, there will be a growing need for real-time analysis applications like the one discussed These applications will become more mobile-friendly and integrated with personal health ecosystems Standardization and regulation, with the increasing use of AI in healthcare, there will likely be a push for standardization and regulatory approval of such applications to ensure safety and efficacy Telehealth expansion, as telehealth services expand, web applications for remote monitoring and diagnosis, including ECG analysis, will become more common, facilitating care for patients regardless of location There are several existing ECG analysis applications and platforms, ranging from clinical-grade software used in hospitals to consumer-focused mobile apps that interface with wearable devices These applications typically employ a mix of signal processing and machine learning techniques Examples, Astrisk CardioMobile and CardioApp by LiveCore These provide ECG analysis and are FDA cleared for detecting atrial fibrillation and other conditions Apple Watch, it has an ECG app that uses its built-in sensors to record ECGs and check for atrial fibrillation Clinical software, there are numerous clinical-grade ECG analysis tools used in healthcare settings, many of which are incorporating AI to assist with diagnosis Closing Thoughts As we continue to refine ECG analysis applications, the key will be to balance the sophistication of the algorithms with the usability and interpretability of their outputs Emulating human judgment while leveraging the speed and consistency of AI presents a promising avenue for advancements in this field