 Yeah, we should be good to go What the volume level is like sort it out as we go just Mute some tabs. It's okay. We'll quickly go over this this Eye tracking thing That the GPT made for us is a very quick rapid Development Thing that's okay. It doesn't mean it's No good because of it. I would hope I am Monitoring the chat if you have any comments about anything on body chaos.com. Hopefully this eye tracking tool Will become available soon as well We're actually working in in parallel on several tools The website is running ads. Obviously you can use And add the blocker, then you will see no ads But then I don't get paid Yeah, I'm using an And block on Firefox works like a charm So you know there's a bunch of tools on bodykills.com be adding a As I said, we'll be adding this eye tracking thing. It's not working very well in the month Now, obviously there's a difference between eye tracking and pupil pupil tracking. So this one It doesn't do well Eyes it's pretty spot-on especially if there's two of them Yes, sometimes It's a slow wish to respond if it doesn't see the other eyes making assumption that the right eye is left eye things like that, but It's Very cheap set up to have another camera So I was wondering if I could do a more advanced eye tracking with two webcams what you think I thought we're actually using two webcams, but it's only a One at a month a work space a would a second webcam and do Embellish your bed eye detection or trade like tracking is better. Yeah So so the code eye tracking, but yeah, those pencil system systems. They're obviously not just tracking the eyes. They Tracking the pupil of a single Camera, yeah, we can do both of them. I think the second one is currently used for this stream Yeah, I see it down the bottom there Actually have another tool that we meant to be publishing today the fuzzy logic stuff I'll just go over this one quickly Just read what the pot have written He leveraging the latest developments in web-based experiments It allows for remote tasks and studies reaching larger and hard to reach populations with potential applications in telemedicine The app introduces improvements to remote ET for cognitive and clinical tasks assesses spatio temporal resolution and reliability and tests functionality using the anti-sacade task This cognitive test assesses inhibitory control by comparing horizontal eye movements toward pro saccades or away from anti-saccades a target The results shows stable calibration and performance comparable to high quality laboratory ETS opening new avenues for clinical applications and experimental use Yeah, that's rubbish rewrite this the problem This HTML description is just taking it out of finaire not looking at the other files So I'm not sure if I just ask it to rewrite I don't know what it will do. Just taking literally that as a context as a prompt and Rewriting the thing Actually not doing any of those question is What if we do workspace rewrite paste called eye tracking pie file Egypti 4 can do much better because it can hold context for the whole thing. Yeah, it's really I'm sure it will write a much better description, which actually will be more accurate as well Yeah, I like how this one has the links In that as well. That's handy Yeah, so asking to rewrite the HTML description Again, we can do workspace Can you write a description for this application and including an about section how to use section? Include all the libraries that are being used and look at the other files like eye tracking Python file and main JS to see how the application actually works So the description is as accurate as possible So it's determining deciding which information to collect Gathering info It's used five references You know why that won't open maybe when it's finished generating It's again going on about the sake of circuits, which we are not Doing yeah, that's correct How to use no, I mean it's running it will eventually be a running as a flask application so we don't actually We might have a requirement file eventually might pop it on GitHub as well We had some issues with the kid in general. Yeah, we're more about the first Sophisticated product leverage weapon based eye tracking technology for remote experiments. Okay How to reach population maybe as potential? Yes Remote eye tracking. Okay Yeah, I don't like the any mentions of cognitive tests and things Yeah Yeah, that description is not great. We might get the GPT-4 to write something better This one meant to be using GPT-4, but it's obviously Look, yeah, it did look at this very references, but Maybe if I delete the current Description it was looking at the entire code actually. Yeah, I think it will be easier to get the GPT-4 to do it To do this job Yeah, obviously it will have to be calibrated somehow currently this button doesn't actually work There is a suggestion as to how a calibration system could have worked See one eye is a bit off quickly a Corrected here, I would imagine Double integer tuple tuple tuple tuple don't have to pronounce it But yeah, it's self-working So it's working really well. Yeah, it's always interesting To the right it's when I move to the right it's tracing really well When I move to the left, it's lagging. It's really interesting Must be something in the code again this type of context is better for GPT-4 to handle. Yeah, I'll see if you cover can try and Swish your eyes eyes away by the way, that's obviously what the Nvidia eye tracking thing that the eye replacement thing does It just draws your eye onto this little things Yeah, I can make them a smaller potentially have a circle Round them. Yeah, this fatal a fatal era When Yes, obviously Yeah, let's pop this into GPT quickly and give it Give it the eyes as well. Yeah, I said that's Wrong We would like to fix that It looks like you're trying to process an image to localize eye positions using the media pipe face mesh model in Python The curved snippet you provided sets up the face mesh model processes the input frame to find facial landmarks and then attempts to calculate the centers of the left and right eyes by Averaging the positions of specific landmarks around each eye If you're having issues with the eye localization and want to ensure the eyes are detected correctly, you might need to ensure that You have the correct indices for the facial landmarks that correspond to the eyes The indices you use in the underscore calculate underscore i underscore center function need to accurately reflect the landmarks around the eye region for Both the left and right eyes You're correctly handling the coordinate system of the image Media pipe returns the landmark positions as a fraction of the image width and height So multiplying by the frames width and height to convert these two pixel coordinates is correct The model is performing as expected If the model is not detecting the eyes reliably, you might need to check the lighting conditions the quality of the webcam feed or whether the face is being occluded If the issue is with the visualization ie the red dots are not appearing where you expect them to It's possible that there may be an error in the way the eye centers are being calculated or in how the circles are being drawn on the frame Double check the logic in underscore calculate underscore i underscore center and the parameters for CV to circle Here's a basic checklist for troubleshooting Verify that the media pipe face mesh model is initialized and used correctly Ensure that the facial landmark indices used in underscore calculate underscore i underscore center are the correct ones for the eyes Check that the coordinates are being transformed from normalized to pixel space correctly Make sure the drawing function CV to circle has the correct parameters and is being called with the right arguments Test with different images or in different environments to see if the issue persists If you continue to face issues, you may need to debug the application step by step Possibly visualizing intermediate steps to ensure that each part of the process is functioning as expected Regarding the uploaded image it seems you're trying to demonstrate a specific issue To assist further with this I would need more context about what exactly is going wrong with the provided code snippet When you say we would like to fix that If you can provide more details about the problem, I can offer more targeted advice actually more sophisticated And process frames We leave the yeah, we use the face model face mesh model is from media pipe I think it's a google library yeah one A red dot over the pupil And then blue square over the entire Eye square on the first argument frame the second third arguments pop and blah blah Small stop working again changes made this While now if this one needs the workspace gives This scary era So I have to start it again every time It's a bit annoying. Right. So now I have this A blue A squares and the highs are still inside So I'm wondering if I'm generating the requirements Can just generate the five maybe Okay, it looks good. Let's try GPT again With the uh With the updated code this If it can So obviously it can recognize what's going on in here It's like, can you find any issues with the output of this code? The pupils are not properly detected. Is there a better way of doing this? Yep That's the other good thing about the GPT-4 is that you can upload the images the output of your stuff or whatever it is Detecting pupils accurately in images can be challenging especially when using computer vision and machine learning tools like media pipe The output issues you're experiencing where the pupils are not properly detected could be due to several factors Here are some steps you can take to improve pupil detection Optimize landmark selection ensure that the landmarks you've chosen accurately correspond to the regions around the eyes The landmarks for the eyes in media pipe are numerous and selecting the right ones is crucial for calculating the center of the eye Refine center calculation the center of the eye doesn't always align with the pupil especially when the eye is not looking straight ahead You may need to adjust your center calculation to account for the shape and orientation of the eye Contrast and brightness adjustments sometimes image preprocessing can enhance feature detection Adjusting the contrast and brightness of the image might help in making the pupils more distinguishable Advanced pupil detection techniques instead of relying solely on landmarks Instead of using techniques specifically tailored for pupil detection such as huff circle transform or fresh holding methods to find circular patterns associated with pupils Check for occlusions and lighting make sure there are no shadows or reflections in the eye region that could confuse the detection algorithm Consistent and diffuse lighting can significantly improve detection quality Use of additional libraries some specialized libraries or models are designed for eye tracking in pupil detection Exploring other tools like open eds or gaze ml might provide better accuracy for your specific use case Post-processing after detecting the eyes you can apply additional post-processing to refine the location of the pupils For example, you could analyze the area within the detected eye region to find the darkest point which often corresponds to the pupil Machine learning for more advanced applications consider using machine learning models that have been specifically trained to detect pupils These models might require a labeled dataset for training but could yield more accurate results Debugging visualize the landmarks detected by media pipe on the image to ensure they're being placed correctly This will help you understand if the issue is with landmark detection or with the subsequent processing steps For more detailed advice specific to the media pipe library or advanced computer vision techniques You might consider consulting the official media pipe documentation or looking into computer vision focused communities and forums where similar projects are discussed If the pupils are being detected but not aligned correctly you might need to adjust the landmark indices used or the method of calculating the eye center Make sure the indices match the eye region landmarks exactly and consider looking for the darkest part of the eye image to locate the pupil more accurately If you want to delve deeper into computer vision techniques for eye tracking It might be beneficial to explore academic papers or tutorials that focus on this specific area These resources often provide insights into the latest methods and best practices for eye and pupil detection For a new function Ah, yeah, it did give it to me So extracting the region of interest convert to gray scale and finding the darkest point And returning the location of the darkest point Yeah, those magic numbers Eh, obviously they will make the script not generalizable. So if you use a different camera, a different angle, it might not work anymore We happy to try it out So it's hopefully should correct The circles and processing frame So instead of Eyes we draw A pupils Yeah, we keep the red dots where they are And in addition should have green ones Where the pupil is What is working surprisingly well The one keeps flickering around It's like a lot of the show So it's working Yeah, it's really funny. Like one eye is perfect. Maybe there's something wrong with my eye. Someone has someone else has to try it as well But uh Which one is it? This one Works, oops. Yeah Well, that's a Age condition when only one eye is visible. So yes, it would not work But yeah, it works Almost perfectly, not perfectly, but like nine percent with one eye And It's a bit funny with the other eye. So I guess the red dots are just the center of the square Yeah, it's just funny. Whom did it for us? Was it? right, so Yeah, so the One of the good seem to be Working ways to go about the developing is using gpt And then essentially generating prompts for github copilot to use seem to work well Both of them are jumping around You have the correct indices for the facial landmarks that correspond to the eyes The indices you use in the underscore calculate underscore i underscore center function need accurately reflect the landmarks around the eye region for both the left and right eyes You're correctly handling the coordinate system of the image Media pi returns the landmark positions as a fraction of the image width and height So multiplying by the frames width and height to convert these two pixel coordinates is correct open never Maybe another example is it the same different? Well, I can tell because I can Look at the camera and the screen at the same time if you had the nvidia, what's called the I think the correction I guess correction without then you can but that's fake Optimize landmarks selection ensure that the landmarks you've chosen accurately correspond to the regions around the eyes The landmarks for the eyes in media pipe are numerous and selecting the right ones is crucial for calculating the center of the eye Refine center calculation. The center of the eye doesn't always align with the pupil, especially when the eye is not looking straight ahead You may need to adjust your center calculation to account for the shape and orientation of the eye Contrast and brightness adjustments. Sometimes image preprocessing can enhance feature detection Adjusting the contrast and brightness of the image might help in making the pupils more distinguishable this code now You have two examples They're Open the UI website is not scaling properly If I turn on the light, no, I still understand To the regions around the eyes The landmarks for the eyes in media pipe are numerous and selecting the right ones is crucial for calculating the center of the eye Refine center calculation. The center of the eye doesn't always align with the pupil, especially when the eye is not looking straight ahead You may need to adjust your center calculation to account for the shape and orientation of the eye Contrast and brightness adjustments. Sometimes image preprocessing can enhance feature detection Adjusting the contrast and brightness of the image might help in making the pupils more distinguishable Advanced pupil detection techniques instead of relying solely on landmarks Instead of using techniques specifically tailored for pupil detection such as half circle transform or fresh holding methods to find circular patterns associated with pupils Check for occlusions in lighting make sure there are no shadows or reflections in the eye region that could confuse the detection algorithm Consistent and diffuse lighting can significantly improve detection quality Use of additional libraries some specialized libraries or models are designed for eye tracking and pupil detection Exploring other tools like open eds or gaze ml might provide better accuracy for your specific use case Post-processing after detecting the eyes you can apply additional post-processing to refine the location of the pupils For example, you could analyze the area within the detected eye region to find the darkest point which often corresponds for pupil Machine learning for more advanced applications consider using machine learning models that have been specifically trained to detect pupils These models might require a labeled dataset for training but could yield more accurate results Debugging visualize the landmarks detected by media pipe on the image to ensure they're being placed correctly This will help you understand if the issue is with landmark detection or with the subsequent processing steps For more detailed advice specific to the media pipe library or advanced computer vision techniques You might consider consulting the official media pipe documentation or looking into computer vision focus communities and forums where similar projects are discussed If the pupils are being detected but not aligned correctly, you might need to adjust the landmark indices used or the method of calculating the eye center Make sure the indices match the eye region landmarks exactly and consider looking for the darkest part of the eye image to locate the pupil more accurately Yes, that's what the co-pilot made this code for us Should have Yeah, we have these two two examples In the code you've shared and based on the images you uploaded it appears You're attempting to detect the pupils within the eye regions detected by media pipes face mesh model Your method involves finding the darkest point within the region of interest ROI around the eye center, which is a common approach in cubal detection Here are some suggestions to potentially improve the accuracy of the pupil detection Increase the contrast of the ROI before converting the ROI to grayscale You might want to increase the contrast to make the pupils stand out more This could be done using histogram equalization or adaptive histogram equalization Gaussian blur applying a Gaussian blur before finding the darkest point can help in reducing noise and making the pupil detection more reliable Thresholding instead of looking for the darkest point. You could apply a threshold to create a binary image where the pupil is clearly segmented from the rest of the eye Then you could find the centroid of the largest blob which should correspond to the pupil Refine search area the size of the ROI is currently set to a fixed size You may want to dynamically adjust this size based on the distance of the face from the camera or based on the size of the detected face in the image Edge detection utilize edge detection methods to find the boundary of the pupil The pupil edge is typically well defined and could be detected using algorithms like the canny edge detector Use infrared lighting if you have control over the hardware using infrared lighting can significantly improve pupil detection as the pupil reflects less infrared light compared to the iris Robust detection algorithms consider using more advanced algorithms specifically designed for pupil detection such as a combination of edge detection and half circle transform to detect the circular shape of the pupil Machine learning use a machine learning based approach where a model is trained to detect pupils This would require a labeled dataset but could potentially offer better performance Here's how you might implement some of these improvements Adjustments to find underscore pupil function Remember to carefully handle edge cases where the ROI might extend beyond the boundaries of the frame which could lead terrors in the find underscore pupil function Improving pupil detection can be an iterative process and you may need to experiment with different techniques and parameters to find what works best for your specific application and lighting conditions Yeah, there's a couple of things so One eye is working better than the other like this one especially when I do this Kind of both of them work Might be the light Yeah, I have light coming from three different sources so that might Make a lot of difference Yeah, this one works much better as you can tell But as we're going through a couple of improvements Had some prompts so might update The find pupil function Looking at the ROI in the same way A turning to grayscale set alpha circles huge circle transform. We have to read about that Might as well Comment this out Just make the whole thing a bit smaller A popped that in for find pupil No Yeah, I'm getting this a fatal error To restart the application if I make any changes to the eye tracking python code Okay, it's not much better. Is it? and Poof and P not defined. What? That's obviously rock We have and P is Numpy anything else Minimum value is not being used Try this again Yeah, so the red just stays in the center of the eye now. This one works really well That one not so much And I also have like two cameras and thinking if I should use the second camera like for correction or something It's the kind of stuff we can fix with A co-pilot Apparently we just don't need that variable at all The app is not currently running. There was another A mean valve is not used. We select the whole code Pop it in It's finding the darkest point Be working. Okay, it doesn't matter if I Have it there or not run The application Yeah, I'm just Surprised the eye is always better than the other Maybe it's something with my eye. I need someone else testing this for me as well Yeah, we'll need to rewrite this description as well The GPT currently only had access to And to the eye tracking by Python code Read this again quickly in the code you shared and based on the images you uploaded It appears you're attempting to detect the pupils within the eye readings detected by media pipes face mesh model Your method involves finding the darkest point within the region of interest RII around the eye center, which is a common approach in pupil detection Here are some suggestions to potentially improve the accuracy of the pupil detection Increase the contrast of the RII before converting the RII to grayscale You might want to increase the contrast to make the pupil stand out more This could be done using histogram equalization or adaptive histogram equalization cloud Gaussian blur applying a Gaussian blur before finding the darkest point can help in reducing noise and making the pupil detection more reliable Thresholding instead of looking for the darkest point you could apply a threshold to create a binary image where the pupil is clearly segmented from the rest of the eye Then you could find the centroid of the largest blob which should correspond to the pupil Refine search area the size of the RII is currently set to a fixed size You may want to dynamically adjust this size based on the distance of the face from the camera or based on the size of a detected face in the image Edge detection utilize edge detection methods to find the boundary of the pupil The pupil edge is typically well defined and can be detected using algorithms like the canny edge detector Use infrared lighting if you have control over the hardware using infrared lighting can significantly improve pupil detection as the pupil reflects less infrared light compared to the iris Robust detection algorithms consider using more advanced algorithms specifically designed for pupil detection such as a combination of edge detection and half circle transform to detect the circular shape of the pupil Machine learning use a machine learning based approach where a model is trained to detect pupils This would require a labelled dataset but could potentially offer better performance Here's how you might implement some of these improvements Adjustments to find underscore pupil function Python copy code def find underscore pupil frame i underscore center size equals 40 hash extract the region of interest r i Around the ix y equals i underscore center row equals frame Remember to carefully handle edge cases where the r i i might extend beyond the boundaries of the frame Which could lead errors in the find underscore pupil function Same thinking a controller for the A find pupil size of the r i You want to control the Magic numbers on the page Yeah, it's always that one eye is working better than the other. Anyway, I don't think we'll be making much more improvements to this today We've got 10 minutes left um you With this will or something similar to this will be on bodykills.com They go check it out if you haven't done so already There's a lot of tools that you might be interested in The more recent ones are up the top and some of the old ones at the bottom they Do not work anymore because we haven't transferred them into A python they still Love you the videos are Where the tool is actually working like the neural net and the fuzzy logic These annotations statistical analysis so these four They were originally written in lab view and they do not work anymore because we Well, don't have the license Migrating everything into python the new tool that will come up will actually combine This ecg game and the fuzzy logic So you could play Um Both At the same time and run two Scripps just it's probable. I'll say bye-bye for now Um, so we formally that's nothing formal, but it's formally finished Just try opening an hour window with the It's called a cardio quest port like and yeah, we need to turn it into A flask application Just run on Can we run it on a different port? Why is this problem? Are we specifying port 500 for this? No, so why not? Yeah Why don't just do it automatically We stop This one and run that surely we should be able to push idea before Can run both of them At the same time. Yeah, I want to just do it Anyway, this is the other application This is actually what we have the description here for so sorry when working on that one should have had a different description And this is the one we plan to release A shortly For you to play with can then now set your noise levels That's the noise level at default A you can obviously still play the game as in the original two Oops, not doing well anyway Not paying attention. That's the normal ecg So I should get 10 points for not clicking on it. And there's one missing a bit So I do click on it and get another 10 points So I got one hit one miss and the current port which we changed the parameters of Is doing badly it's essentially clicking on everything So I can win the bot except that yes, we can Can artificially tune it up and it will work much better A ideal we're trying not to use any magic numbers in the algorithm. So it's a So the thresholds and everything are just The There as if they were provided by By yes, I'm the cure cure kids. Yeah Get a Nobel Prize Yeah, so those are the rules so this is the fuzzy logic analysis We're looking before we're looking at amplitude positive peak negative peak Abnormality and frequency Now we got rid of frequency looking at the sharpness of the our peak. So assuming the our peak is the highest positive peak and then we're going I think half a way a measuring the distance between You know left and right coordinates in it That that works fine Then we normalize it. So we'll get a 0.4 sharpness for Like a regular thing and a 0.1 for Less sharp our peak So that's working fine Except there's still something wrong So for this signal the made the decision that's an abnormal ecg because the abnormalities core didn't go low enough And that's a problem um, and this is because we Have and done the membership functions correctly a So for example with amplitude We have a low medium and high because we were normalizing it. We are not normalizing anymore so the Amplitude should change The maximum amplitude is about uh, yeah, which is so before three five six So we have to fix the code Question is should we do it today? Yeah, that was a false alarm clicking on a normal normal ecg Well question the question always is I can't call myself Questions if they go there One of the boards Can fix this for us try Co pilot This let's start a new one Is this couple of things? So our sharpness is calibrated now the yeah the problem with raw Amplitude Is there a calibration for the raw amplitude? Do we normalize that value or do we use the raw value? If so, it is not from zero to one We might need to fix the code The maximum value is about 360 Looking at the entire python script Yeah, it's adjusting a normalization Dividing by the maximum value and say quickly Amplitude universe I'm mentioning amplitude like 37 times amplitude Yeah, I only have the raw one Yeah, should we normalize don't need a function A normalize using a max value Should we make it like 400 or something? Yeah, we don't like magic numbers. That's a quick fix Um Yeah, just give me zero all the time. Should we try not the Looking at amplitude at all now. It's one That was a normal I know it wasn't normal Yeah, that's normal the raw amplitude is always one A what should I divide it by Was it 350 or something wasn't it? Why is this giving me zero? Let me print this value out 1 8 so Yeah, that's working perfectly. It's just not showing A decimal places Space raw amplitude Is not showing decimal places It's finding what to look at Taking forever So I was muted can leave all the comments. So it knows what we were doing And we have this image as an example is So we have a Now this code Should have processed the waveform in the image as normal ACG waveform it had a good number of positive and negative peaks The amplitude should have been A labeled as normal Can we double check that? I think that might be What's what the problem is? sharpness Should have been okay So the abnormality score should have been low enough For the algorithm to make a Decision that the waveform is normal All right, we're dealing with an ecg waveform and some fuzzy logic analysis in the python flask app you've provided You want to confirm if the code should have processed the waveform as normal based on certain parameters Let's break it down Positive and negative peaks the process underscore data function counts the number of peaks above and below a threshold Defined by the prominence underscore value The waveform image shows there are two positive and two negative peaks which seems to match your description Amplitude in the process underscore data function the amplitude is calculated as the difference between the max and min of the waveform data It's then scaled by dividing by 350.0, but the code comment suggests it should be 400 This could be an inconsistency Sharpness the calculate underscore r underscore peak underscore sharpness function Calculates the sharpness based on the width at half the maximum height of the r peak Your code shows the sharpness score is 10 divided by the width at half maximum This could be correct as long as the r underscore peak underscore index correctly identifies the r peak Abnormality score the fuzzy logic analysis considers multiple inputs and uses defined rules to compute an abnormality score This score is then interpreted to make a decision Looking at the given image the fuzzy logic analysis has marked one false alarm which has led to a fuzzy score of minus 10 This could be due to the way the rules are set up in the fuzzy logic controller or due to the threshold set for peak detection or amplitude calculation The peak detection thresholds are correctly set The amplitude scaling factor is correct The r peak sharpness calculation is being done correctly. The fuzzy logic rules are correctly interpreting the inputs Without running the code with the actual data, it's hard to pin down the exact issue But these are the likely places you'd want to start troubleshooting If the peaks count amplitude and sharpness are as expected then focus on the fuzzy logic rules to ensure they're not too stringent Causing normal waveforms to be classified as abnormal Sure, let's target the potential issues one by one Amplitude calculation if the maximum expected amplitude is 400 based on your comment You should normalize by dividing by 400 instead of 350 This will scale the amplitude correctly between zero and one r peak sharpness ensure that the r peak sharpness calculation is robust Especially in cases where the r peak is not the absolute maximum or the waveform is noisy You might also want to normalize the sharpness score to a value between zero and one to match the fuzzy logic universe Fuzzy logic rules the decision making could be too sensitive, especially with only one false alarm leading to an abnormal classification Consider adjusting the rules to be less stringent For example, you could adjust the antecedents to require more than one condition to be abnormal before classifying the signal as such Error handling it seems that if there's an error the app currently just prints it out It might be helpful to also log the data that caused the error so you can debug effectively Logging to help with debugging consider adding more logging throughout the process underscore data and fuzzy underscore logic underscore analysis functions To understand how the input is being processed and what each step of the fuzzy logic is outputting Validation you could also add a validation step to check if the r underscore peak underscore index is reasonable This is to ensure that the peak detection is working correctly Implement these changes and then test your system again with a no normal waveform to see if the classification is now correct These suggestions are aimed at making your fuzzy logic system more robust and your application more debugable Can we go over each membership function? and each rule one at a time And specifically the one to do with amplitude because that's what we Changed recently the calculation of it Absolutely, let's examine the membership functions and rules focusing on amplitude since that's where you made a recent change In your fuzzy logic setup amplitude is an antecedent with three automatically generated membership functions low medium high since you use auto mf 3 Let's redefine them manually for more control Low amplitude zero to zero point five with the peak at zero This captures the lower end of the normalized amplitude range Medium amplitude zero point three to zero point seven peaking at zero point five This range covers the middle values of amplitude High amplitude zero point five to one peaking at one This captures the higher end of the amplitude range Let's look at the rules that involve amplitude They determine how the fuzzy logic system interprets the amplitude in the context of other variables Rule one says that if there are too few or too many positive peaks then the amplitude is not high Then it's likely an abnormality The tilde operator might be negating the high membership. So if the amplitude is not high the rule triggers This might not be what you want if you're trying to say that a high amplitude is concerning If the intention is to mark it as likely abnormal when there are too many or too few peaks and the amplitude is high Then remove the negation Rule five seems to be directly related to the amplitude Rule five says that if the amplitude is low then it is likely an abnormality This makes sense if you expect a certain threshold of amplitude for a normal waveform Since you've adjusted how amplitude is calculated you should review how it fits into your fuzzy logic Make sure that the ranges you've defined for low, medium and high amplitude align with the actual data you expect from normal and abnormal ecg's If not adjust the membership function ranges accordingly. You might also want to consider the implication of each rule For example, is a low amplitude always likely to be abnormal or should it be combined with other factors? Make sure each rule accurately represents the logic you want to apply based on your domain expertise Lastly if you find the rules are too sensitive you can adjust them to be more specific For instance, you could require that both the amplitude be low and the peak count to be abnormal before deciding that the waveform is likely abnormal This would look like This way you create a more nuanced and potentially accurate system that can better distinguish between normal and abnormal ecg waveforms Just realized that the frequency actually was uh detecting the difference between Normal and one of those examples where the R peak is shifted forward in time Because that was producing a frequency of five And now we do not have a variable to detect it We don't have a feature That we can use to detect that variation Yeah, every time you fix something you break something else, isn't it? Yeah, so we already have this rule should be working. Okay Rule one says that if there are too few or too many positive peaks and the amplitude is not high then it's likely an abnormality The tilde operator might be negating the high membership. So if the amplitude is not high the rule triggers This might not be what you want if you're trying to say that a high amplitude is concerning If the intention is to market is likely abnormal without too many or too few peaks and the amplitude is high then remove the negation Yeah, the amplitude is between them I think I know what the problem might be The amplitude scaling Let's try 500 for a sec So we get the amplitude of 0.6 Then 0.7 So 0.7 should be In the normal range, which is not quite true. Yeah, this meant to be normal Yeah, we can manually tune it up And sorry for that That's 0.6 0.3 abnormal 0.6 It's classifying that one as normal. I don't know why The rules should just make sense. It's always one of those things that should have just designed You know once not touch ever again Should have just worked Yeah, all right. Well that rule now it's two Not sure This one might get A might rewrite it without looking at the amplitude It don't think load Amplitude just keep it simple Yeah, so if positive peaks uh, too few or too many The abnormality is likely Yeah, understand now. It kind of makes sense. I understand now why Uh, that first one gave a miss because it was abnormal This one is wrong Wait, why does two negative peaks Should have only one negative peak? Yeah, there's something wrong with the negative peak detector So that one is correct and this one wasn't That's a case of one negative peak When there is Should be zero Yeah, this one Can we check the negative peaks? Um The counter the detector is uh incorrect It the I don't know. Can you explain why? In the first two images it's detecting two negative peaks And the last image is detecting one In all three cases, there should be just one Negative peak Yep The discrepancy in the number of negative peaks detected across the three images Maybe due to how the peak detection algorithm interprets the waveform data Especially considering the prominence parameter set in the process underscore data function In the first two images the code is detecting two negative peaks while in the last one it detects one This could be because of the following reasons Prominence parameter if the prominence value is set too low the algorithm might pick up minor variations in the waveform as peaks It's calculated as 1.5 times the standard deviation of the waveform which may not be the optimal threshold for these waveforms Waveform variations the actual waveform data might have variations that are being picked up as peaks For example, if there is a slight bomb or dip before the main negative peak and it exceeds the prominence threshold It would be counted as an additional peak Algorithm sensitivity the algorithm uses skippy signal find underscore peaks to detect peaks which is quite sensitive to the data It analyzes Even small fluctuations that meet the prominence criteria will be counted as peaks To correct this you could Adjust the prominence value increase the prominence value to ensure only significant peaks are detected Use other parameters the find underscore peaks function allows for other parameters such as height and distance Which are used to refine peak detection Filter the waveform apply a smoothing filter to the waveform data before peak detection to remove minor fluctuations Here is how you might adjust the code to include a height parameter By fine-tuning the peak detection parameters, you should get a more accurate count of the negative peaks Make sure to test these changes with a variety of waveforms to ensure the algorithm performs well across all expected scenarios Using the standard deviation to set the prominence for peak detection is a common method, but it's not without its limitations Here's why it might not always be the best approach especially for ecg waveforms Sensitivity to noise ecg signals can have a lot of noise If your waveform includes noise the standard deviation will be higher potentially leading to a higher prominence threshold This means smaller yet clinically significant peaks could be missed because they don't meet the exaggerated prominence criteria Variability of ecg components ecg waveforms consist of different components p-wave qrs complex t-wave each with varying amplitudes A prominence value based on the standard deviation might not be suitable for all components Especially if your qrs complex is very high compared to p or t waves or vice versa Waveform baseline wonder ecg signals may exhibit baseline wonder due to respiration or movement This can affect the standard deviation calculation leading to incorrect prominence values that could either miss real peaks or detect non-peaks Non-stationary t ecg signals are non-stationary, their statistical properties chained over time Using a single standard deviation value as a threshold might not be representative for the entire duration of the signal Outliers if the ecg data has artifacts or sporadic transient spikes these can greatly increase the standard deviation skewing your prominence value To address these issues you might consider a more adaptive or multifaceted approach to peak detection Filtering preprocess the signal with a bandpass filter to remove noise and baseline wonder before calculating the standard deviation Adaptive threshold instead of using a global standard deviation use a moving window to calculate a local standard deviation and set the prominence dynamically Fixed threshold if clinical ecg standards suggest typical amplitude ranges for different wave components You might use these fixed values to define prominence Machine learning employ machine learning models that can learn the complex patterns of an ecg signal and identify peaks more accurately Each method has its pros and cons and the best approach often depends on the specific characteristics of the data you're working with It's also common to combine several methods for the best results For instance preprocessing with filtering followed by an adaptive threshold based on the local standard deviation might yield better peak detection in noisy or variable ecg data Using the median instead of the standard deviation can be a more robust approach in certain contexts because the median is less sensitive to outliers and skewed data distributions In ecg waveform analysis if the signal contains occasional spikes or artifacts These could significantly affect the standard deviation but have much less impact on the median In the context of your code the median could be used to establish a baseline of the signal or to set a threshold for peak detection that isn't as affected by noise or outliers Here's how you might use the median Baseline noise level calculate the median absolute deviation mad from the median of the signal which provides a robust estimate of the signal variability Peak detection use the mad to set the prominence in skippy signal find underscore peaks The prominence parameter helps to distinguish peaks that are significantly above the surrounding data Here's how you might adjust the code Regarding the filtering you're correct that you don't necessarily need to apply it twice if it's already being done effectively Let's check the code for existing filtering steps Look for any code that might be using functions like skippy signal fillfilt skippy signal l filter or any other filtering methods Check if there is a band pass low pass or high pass filter applied to the ecg data before the peak detection step If there's already a filtering step that removes the noise and potential artifacts adding another filter might not be necessary unless there's still too much noise affecting the peak detection If that's the case consider adjusting the filter parameters or using a different type of filter to better clean the signal Without the actual code execution or signal data, it's a bit of a challenge to give more precise advice However, these guidelines should give you a starting point to refine the peak detection algorithm using the median and mad from a more robust analysis An example data in the image provided should have given only one Negative peak instead. It's giving two. Can you look at the code? and explain why and how to fix yep The issue you're facing with the detection of negative peak stems from how to skippy signal find underscore peaks function is used in your code The prominence parameter is currently set based on 1.5 times the standard deviation of the data, which might incorrectly consider minor variations as significant peaks Moreover, the height parameter for negative peaks detection is hard coded to minus 10, which might not be appropriate for all data sets Here's a step-by-step approach to troubleshoot and potentially fix this issue prominence issue if the prominence is too low the algorithm may detect minor variations as peaks Since you're using a standard deviation based prominence it can be sensitive to noise height parameter the height parameter for negative peaks is set to minus 10 This seems arbitrary and could be inappropriate for the data This parameter specifies the height of peak must be from its surrounding points Data normalization your code normalizes the amplitude by dividing it by 600 which is not standard and may affect the height parameter Ensure that the normalization factor aligns with the actual range of your ecg data Filtering if the signal is noisy consider applying a filter to smooth out minor fluctuations that are not clinically significant peaks To address the negative peak detection issue Adjust the prominence value you might want to calculate the prominence dynamically based on the signal characteristics For instance using the median absolute deviation mad as mentioned previously or using a percentile of the data Review the height parameter you need to reassess the hard coded value of minus 10 for the height parameter This value should correspond to an expected negative peak height based on the range of your waveform data after normalization Normalization check double check the normalization factor of 600 to ensure it aligns with your data range The normalization should reflect the true dynamics of your ecg signal Here is how you could update the peak detection part of your process underscore data function In this updated code I replaced the normalization divisor with 400 to align with the standard peak to peak amplitude for ecgs The height underscore for underscore negative underscore peak is now dynamically set based on the 95th percentile of the negative values Of the data which should be adjusted based on your specific data characteristics Remember that the values for mad asterisk 1.5 and np percentile mp underscore data 95 adjust starting points and might need fine tuning based on the specific characteristics of the ecg signals you're analyzing After making these changes you would need to run the code with several test cases to ensure that the peak detection is working as intended Doing filtering somewhere Already because last time I was saying if the bot has access to filtered data the human should as well So might like overlay the filtered A bit over the black one on the top Yes, we expected to be smaller amplitude I replaced the normalization divisor with 400 to align with the standard peak to peak amplitude for ecgs The height underscore for underscore negative underscore peak is now dynamically set based on the 95th percentile of the negative values of the data Which should be adjusted based on your specific data characteristics Remember that the values for mad asterisk 1.5 and np percentile mp underscore data 95 adjust starting points and might need fine tuning based on the specific characteristics of the ecg signals you're analyzing After making these changes you would need to run the code with several test cases to ensure that the peak detection is working as intended Regarding the filtering you're correct that you don't necessarily need to apply it twice if it's already being done effectively Let's check the code for existing filtering steps Look for any code that might be using functions like skippy signal fulfill skippy signal alpha or any other filtering methods Is there any filtering in this code? Which lines are filtering in this python code? It's because it's too long Yeah, might be way too long Not doing frequency might be a problem Okay, can you summarize the whole session? The whole code that you have access to All the modifications that we made Can you also touch on the difference between peak sharpness calculation and frequency analysis? Also, can you check the code for? Uh, any filtering that we are doing? I already checked we do not use field Uh functions But I think that might be a different more simple filter Can you find that? And uh We might be finishing now so I can give an overall summary of how this application can be useful And if there is anything similar Um in the wild Yep Certainly Let's summarize what we've discussed and the modifications suggested for your code that analyzes ecg waveforms using fuzzy logic Amplitude normalization the code initially divided the amplitude by 600 for normalization We discussed that the typical peak to peak amplitude for ecg should be normalized to a more standard value possibly 400 depending on the range of your data Peak detection We suggested shifting from standard deviation to median absolute deviation mad for calculating prominence as mad is less sensitive to outliers And might provide a more robust threshold for peak detection We recommended reevaluating the hard-coded height parameter for negative peak detection Instead a percentile based approach was suggested to dynamically set the height based on the characteristics of the data Peak sharpness calculation This is about how sharply the peak rises which is determined by the width of the peak at half its maximum height The sharper the peak the narrower it is at half its maximum This is relevant in ecg for identifying the r-wave sharpness which is important for certain diagnoses Frequency analysis this would involve examining the frequency components of the signal often using a Fourier transform This can help identify periodic components within the ecg signal such as regular heartbeats However, we did not discuss modifications to this part since it wasn't part of the provided code snippet We did not find any use of filtering functions like filth filter or L filter in the code snippet provided A simple filter in ecg analysis could be a moving average or median filter Which will help smooth out the signal and reduce noise However, no explicit symbol filter was found in a provided code The application you're developing is a tool for analyzing ecg waveforms using fuzzy logic This kind of tool can be very useful in clinical settings or for a search where ecg data is used Clinical decision support it can help clinicians interpret ecg data by highlighting potential abnormalities and providing a quantitative assessment of the waveform characteristics Remote patient monitoring for patients with wearable ecg monitors This tool could provide real-time analysis and alert systems for abnormal heart rhythms Research and development, researchers can use such applications to gather data on ecg waveform characteristics across different populations and conditions Comparable tools or systems in the field might include Commercial ecg machines, these typically have built-in software for analyzing ecg waveforms, often using proprietary algorithms Research software, tools like MATLAB or Python libraries, e.g. BiosPPY, are used in academia for ecg signal processing Wearable tech devices like the Apple Watch have FDA approved ecg monitoring capabilities that can detect irregular heart rhythms To ensure your application stands out or is as robust as possible consider integrating Advanced signal processing techniques like wavelet transforms for denoising ecg signals Machine learning for more sophisticated pattern recognition that can learn from a large data set of labeled ecg waveforms Use a friendly interface, ensuring that the output of the analysis is presented in a way that's easy for the end user to interpret Finally, always remember that any medical tool should go through rigorous testing and, if applicable, regulatory approval to ensure its reliability and safety for clinical use The code and modifications we discussed are initial steps and thorough testing with real-world data is essential Okay, we might, yeah, we really broke the fuzzy logic Detector, it doesn't work at the moment We'll have to fix it next time that will be next week until then to go check out bionicales.com and Don't forget to provide your feedback. See you next time