 We should be live. If you haven't checked by any Kiosk.com yet, please go do so. This session is all about developing the website. If you have any questions about it or we tried it, tried it out and you have any complaints about what doesn't work. This is a good time to raise them Because we are live developing the site. Let's quickly touch on so If you have a Garmin watch or something apparently I don't have one, but apparently they using this fit file format and Chagabt seem to know a lot about it. So we were it was able to generate a file that loads the fit file from a Garmin watch. It's just an old example from Github from about seven years ago. So it must be a very old watch, but it's able to it's using this it's actually two major libraries for passing those fit files There's a fit file parser and Now one that's called something else. So it's able to Extract what's in the header, but the header looks a bit weird So we have a header length of 12. However, are we only showing four records? I don't know why so we have to fix that and I don't know if there is any interest in doing this, but we will try and display the data. The interesting thing. I don't think all the watches have a PPG data in them So that's the main one I'm after Want to see if it's a textbook PPG like Yes, we'll have the heart rate temperature. It should have accelerometer data Apparently the sampling rate is not Determined like in a standalone number. It's actually but the each supposedly each that point has its own time stamp So we'll see how That works So it's suggested some more functions, but they have Placeholders in them getting a bit Confused If something anyway, the header is there I don't know should we spend more time on this Considering it's a very old file from about the seven years ago Also, so if you have any newer data files, and if you know that your device has recording PPG it's the blood oxygenation level oximetry then Yeah, I've ideally just pop your data on to GitHub So anyone including myself can have a quick look Move on to other things that we're doing So the last Published tools is the eG to music converter if you tried it out If you haven't tried it yet, please do so. Let me know what you think. There is also the music from hand gestures That yes might well will definitely not work on a mobile device So I'm not sure and I haven't tried it on a laptop. It does work with GPU on My machine pretty well. I do have issues with my web cameras. I have two of them connected Yeah, currently I just gave up on troubleshooting them We always looking for public biomedical data set So I have quite a few that we might be looking at There is the Tugh eG data set is quite Interesting from my temple University Hospital And it's quite an old one all them will be quite old If you have any access to newer one once in the you know what find them in the public domain Please do let me know as well in my head and now One that we were looking at more recently and I lost its name And someone can Remind me when sick check my I was downloading some of it said Yeah, it has MRI as well to try Doing that as well. Yeah, we have a raw eG in CSV file format should be Pretty straight over to load my three Started on it already or not He was always trying to do a cochlear model as well, but yes, currently it's really bad Ideal it will just model how a cochlear implant work We've by using your device as a microphone will show you Which electrodes on a virtual? cochlear implant Electrode array being stimulated when you talk into your own microphone So that could be a fun project that know to think if there's no specific suggestions we might into need some sort of Project random number generator Yes, we're doing the eye tracking work. Let's get the stock on it as well. Yeah, the most Successful one well, we actually spend So much time on it So we must publish it considering How much time we spend double check this would be about like 20 Livestreams or so. So we have this flask application They up by the actual flask application. We have to java stream javascript codes one that actually generating the waveforms their simple math Functions nothing fancy at the game logic Start popping it into GPT for Because Weird it's more you know start pulling it into GPT for because we can't code GPT for can a better than GitHub copilot So we have the html code the waveforms A j s and generating the actual ECG normal abnormal waveforms window became Really bad at having issues like that where it would not as roll to the bottom Bit weird. We have the bytes and script. So now the context there. It's not the context length but the window size For GPT for on the open AI Website is very long now. So we can try a single shot prompt Let's run the application as well as this what it looks like You can still play it yourself Hopefully you already know how or try that out already Currently we're getting this here as well. So hopefully should be pretty straight forward for GitHub or well, you would think it have copilot will Solve this pretty quickly But the last time I tried it didn't do a good job. It's prompted some more So hopefully this prompt is not too long Hey, we are sharing all the code for this last application by the way Developing it there while live streaming. So if you like being a co-host Please let me know You have any funny jokes go for it, but keep them relevant to the topic The flask application has this four different files And the HTML to JavaScript files and the Python Back-end flask application that does all the fuzzy logic analysis Essentially a robot that is playing the ECG scoring game Currently we getting the error that I also shared If you could sort that one we can be on our way Developing the tool further and eventually publishing it on our website. So Valuable viewers can access the application play with it and provide feedback Yep. All right diving straight into the issue with your flask app The area you're encountering less than not supported between instances of float and dig suggests a type mismatch in a comparison operation in your Analyze underscore waveform function This usually happens when you're trying to compare a number float with a dictionary which Python doesn't allow Looking at your code. I see the problem likely originates from your calculate underscore intervals function You're returning a dictionary from this function within analyze underscore waveform You're treating the outputs PR underscore interval and RT underscore interval as if they were numeric values In the line You're assigning dictionaries to PR underscore interval and RT underscore interval Which are expected to be numerical values for the fuzzy logic system To resolve this you need to ensure the PR underscore interval and RT underscore interval and numeric If these intervals are supposed to be single values You need to modify calculate underscore intervals to return a single numeric value like an average or median of intervals instead of a dictionary Alternatively if PR underscore interval and RT underscore interval are intended to be collections of values You need to rethink how they're incorporated into your fuzzy logic system as the system expects numerical input for its calculations As for a relevant joke, how about this why the programmers prefer dark mode? Because light attracts bugs. I hope this helps you sort out the error If you need more detailed assistance or have other questions feel free to ask Okay, with the jokes you have to be careful. I heard this one like a thousand times already so if you're going for a joke maybe Make sure it's not in your training dataset Already now regards the code You gonna actually generate the code yourself or are those prompts for github co-pilot to use I Don't mind one way or the other Can you also look? Can you also confirm reading the entire code? Give it a quick summary and some general perspective of what it can be useful for Keep in mind that we already have a Flask web application on the website which allows a human user to Play the ECG scoring game. Yeah, we might try those as github co-pilot prompts Let's select the whole app by because we don't know Where we need to make the change Well, we do know because it's telling us And so let's actually go there. Yeah, we're doing it in the same way It's for the other variables. Hey, can you also look at the what the? Parameters what features of the ECG waveforms the fuzzy Logic algorithm is currently looking at explain some trade-offs between the different features selection in the past we remove the frequency feature because it wasn't Doing much a keep in mind that we are not looking at Continuous ECG. We are looking at individual BQ R sd Waveforms at a time Can you also for reference look at the images provided? This is what the front page of the web application looks like and it's scrolling the ECG waveforms from Right to left Should not matter Yep, I've taken a look at your code and the images you provided Let's break it down Your flask web application is essentially an interactive ECG waveform game that challenges users to identify abnormal ECG patterns The game displays scrolling ECG waveforms and the user must click on sections. They identify as abnormal The game provides real-time scoring based on hits false alarms great rejections and misses Moreover it features a parallel scoring system that uses fuzzy logic for waveform analysis serving as a benchmark or an eye opponent The fuzzy logic algorithm analyzes the waveforms using several features to decide whether a segment is abnormal These features include Positive peaks the number of peaks above a certain threshold Negative peaks the number of dips below a certain threshold Amplitude the difference between the maximum and minimum values in a waveform Our peak sharpness a measure of how peaked or sharp the R wave is within the QRS complex Regarding feature selection you mentioned removing the frequency feature due to its limited utility In the context of ECG analysis Especially when not considering continuous streams But individual complexes the frequency might not be as informative as the morphology shape and characteristics of the waveforms For instance the sharpness of the R peak or the presence of P or T waves can be more indicative of certain conditions like arrhythmias or ischemic events trade-offs in feature selection often involve balancing specificity and sensitivity For example focusing on amplitude might help you detect large abnormalities, but can miss subtle yet clinically significant changes Conversely too much emphasis on our peak sharpness might lead to false positives in cases of benign variations Looking at the images they seem to display the game interface where the waveform scroll from right to left and the users and AI scores are updated in real time This setup provides a dynamic and engaging way for users to learn about ECG interpretation possibly even serving as an educational tool for students or healthcare professionals In terms of the utility of your application it sits at the intersection of education gamification and clinical skill development It can be particularly useful for Medical students to practice ECG interpretation in a risk-free gamified environment Healthcare professionals as a refresher tool to maintain their ECG interpretation skills Cardiac patients to understand their own ECG readings and become more engaged in their healthcare Lastly the scrolling nature of the waveforms adds a layer of difficulty by mimicking the continuous feed of an ECG machine Which can enhance the user's ability to identify abnormalities under pressure akin to real-life clinical situations? I hope this summary gives you the broader perspective you were looking for If you need more detailed feedback specific feature analysis or further development advice feel free to ask Yes, it's working. Okay now. It's actually producing the results. So this is the analysis of the fuzzy logic and Says this signal is abnormal, which is wrong and so she gives a number of peaks of 29, which is no good she reduced the Noise all the way down to zero it produces the correct number of positive negative peaks make the screen a bit bigger So the pot is essentially clicking on everything Making a decision that everything is abnormal including normal waveforms like this one This is no good. We have to fix that to show the Intervals on the front page as well Copilot. Let's try work space can we show the B R and R the intervals on the front panel so it should direct us to the Javascript the game logic JavaScript all and the index html It was meant to be clickable Links, yes So we're still in the pilot and go we are returning this to Yes, we're looking at the absolute the number of positive peaks number of negative peaks The R peak sharpness we should also This to Yes, and we're using a chase and responses to communicate now. Those are not links anymore That's a bit odd Okay, so in Java script, it's making up code. It's not actually looking at the file Is because it's only looked at the Python code Yeah, we don't have the R and R T mentioned in the JavaScript And okay, let me just do this So we have 200 lines in the game logic. Yes, we really did this And last result function Yes, those two look legit Want to display them on the front panel HTML Yes, we look at the analysis result a number of positive negative peaks row amplitude R peak sharpness abnormalities go. So that's the output So here somewhere can we use this instead? That's a comment right this is better. Okay, and we have those two Undefined even though we actually have zero values in the back end. That's interesting any major Aeros in JavaScript. No Let's do this quickly Sounds looks like we're gonna go back to GPT for in a second look Yeah, we definitely not looking at the frequency anymore or not normalizing Amplitude to want to console the data Mainly that we can see the PR interval. No, it's actually zero So what says undefined there? It should give zero Well, even if it is zero, it's not a calculate correctly So it should not be zero. Let's try to reduce noise that always helps to test this intervals should And not be zero No, we don't want to check if it's zero node Calculate the intervals. We have a function for it taking the data and Then input of what type of interval we are looking for and make sure That Function is working correctly. Can we get GPT Do something useful a copilot doesn't not manage the code Can you regenerate the following function with the necessary changes? Yeah GPT for by default is not designed Doesn't want to Generate code But once it starts generating code, then you can't stop it kind of thing certainly The calculate underscore intervals function is intended to calculate specific intervals within the ECG waveform such as the PR and RT intervals To fix the issues and make the function work as intended. I'll rewrite it Firstly, I'm assuming you have a way to identify the indices for P QRS RP and T waves If not, that's a prerequisite step that needs to be added to your processing pipeline The intervals are typically calculated between the start of the P wave to the start of the QRS complex PR intervals And from the end of the QRS complex to the start of the T wave RT interval Here's a revised version of the calculate underscore intervals function that will return numerical values for PR and RT intervals This function now returns the mean PR and RT intervals if there are multiple intervals calculated within the waveform If there are no intervals found it returns zero You might need to adjust this logic based on how you want to handle cases where no intervals are detected Before using this function ensure that you have a reliable way to detect the P R and T waves within the waveform The calculation of intervals is contingent on the accurate detection of these points This piece of code meant to be using the tech peak function is Function a calculate intervals function meant to be using the tech peak Function I provided the whole code that we have so far Can you fix it if the current code I Get the following error From the error message you provided it seems the problem lies in the use of a numpy array in a Boolean context Which numpy does not allow you to ambiguity This typically happens when numpy arrays are involved in conditions that require a single Boolean value like if statements But the operation used results in an array of Boolean values Looking at your calculate underscore intervals function You seem to be attempting to calculate the PR and RT intervals But not using the detect underscore peaks function to find the peaks first Let's rewrite the calculate underscore intervals function properly This function should detect the peaks within the waveform then calculate the PR and RT intervals based on those peaks In your process underscore data function, you need to adjust the following And then since you're returning a tuple you need to ensure that the json response can serialize these values If you're expecting just one PR interval and one RT interval this should work fine However, if you're expecting multiple intervals and you want to return an average or list, you need to adjust the logic accordingly Lastly when you use these interval values in your fuzzy logic analysis, you need to ensure that you're passing a single value not a tuple Please make sure to test these changes thoroughly as the assumptions used for peak detection and interval calculations are quite simplified and may not hold in all cases You will likely need a more sophisticated algorithm for reliable ECG peak detection and interval calculation, especially if you're working with real world ECG data Yeah, we need to rewrite this peak detector It's important. So we'll ask gpt4 to do it as well It's identifying peaks in a given waveform A peak in this case is this is defined As a point that is higher than its immediate neighbors In this configuration looking at the image provider provided We're only getting one peak Which is not right So we have to adjust the the height and distance parameters Ideally, we would not have those magic numbers in the code at all Well threshold could be anything above the noise Or above the baseline level may be distance between Successive peaks Can be automatically determined as well any suggestions. Can you rewrite this function? Yep, so we provided both this image It's only found the one peak With the current configuration, hopefully it can rewrite the whole function The detect underscore peaks function is crucial for identifying the p qrs and t components of the ECG waveform The parameters for height and distance you've used are fixed Which may not be suitable for all waveforms as ECGs can vary greatly from one individual to another and even within the same individual over time We need a more dynamic approach to adapt to different waveforms One method is to use a percentage of the maximum height to set the threshold and analyze the typical width of the qrs complex to estimate an appropriate minimum distance between peaks Here's how you could adjust the function This function first detects all peaks above a dynamic threshold based on a relative height to the maximum peak It then calculates the median distance between these initial peaks and uses this information to rerun the peak detection with a minimum distance set to half of the median peak distance By using the relative height and dynamically determined distance, this approach should adapt better to different ECG waveforms and noise levels Note, this function assumes that your ECG waveform is relatively clean in the p qrs and t waves are the most prominent features If this is not the case, additional pre-processing steps might be necessary to filter out noise or artifacts before peak detection But now it's finding more peaks It will reduce the noise level to zero just for testing It's finding two peaks Even though they meant to be three It just tests that it's working at all In theory Make it point one Reduce the noise to zero Yeah, we get three of them Which is correct There's still three This one should have only two. Yeah, that's correct And we also have another waveform That has the second and third Whatever they call p qrs tp qrs t That's the last one So now we should be able to calculate the distance between them But for some reason pr r t intervals I calculated to be zero That's not right Yeah, so this one is the r and t so we get 320 and 560 which is about right Okay, let's calculate Calculating intervals between peaks Is not Working correctly So the good thing with gpt 4 as opposed to co-pilot Is that we can do this We can load Images into it To use it in the whole code just because we can Oops Because the This message text box now can take very long messages So we had the image we had the text And we need this Output So I uploaded the front panel What the waveform looks like The whole code the flash application back end that we currently have And And the output so we have three peaks we have the Indices For for them they seem to be correct However The pr and rt intervals are still calculated to be zero Can you check the code? And make specific Changes to just make it work Yep Certainly, let's revise the calculate underscore intervals function to correctly calculate the pr and rt intervals based on the detected peaks We'll also need to ensure that the detect underscore peaks function is correctly identifying the p q r s r peak and t waves Which are the key components for calculating these intervals Given the indices of the peaks we should be able to determine the intervals Assuming the first peak is p the second is r and the third is t Which might not always be the case and a more sophisticated peak labeling mechanism might be necessary Let's fix the calculate underscore intervals function Now we'll modify the process underscore data function to use the new calculate underscore intervals function Finally, make sure the analyze underscore waveform route is correctly handling the cases when pr underscore interval or rt underscore interval is none As jason serialization will fail for none values You might want to convert them to a serializable format like a string Make these changes test the application thoroughly and ensure that the assumptions made about the order of the peaks are correct for your specific application If the peaks do not consistently correspond to p r and t in that order, you'll need a more complex peak classification mechanism Certainly, let's revise the calculate underscore intervals function to correctly calculate the pr and rt intervals based on the detected peaks We also need to ensure that the detect underscore peaks function is correctly identifying the p, q, r, s, r peak and t waves Which are the key components for calculating these intervals Given the indices of the peaks we should be able to determine the intervals Assuming the first peak is p the second is r and the third is t Which might not always be the case and a more sophisticated peak labeling mechanism might be necessary Let's fix the calculate underscore intervals function Finally, make sure the analyze underscore waveform route is correctly handling the cases when pr underscore interval or rt underscore interval is none As jason serialization will fail for none values You might want to convert them to a serializable format like a string Make these changes test the application thoroughly and ensure that the assumptions made about the order of the peaks are correct for your specific application If the peaks do not consistently correspond to p r and t in that order, you'll need a more complex peak classification mechanism It's actually seemed to work quite well The pr intervals now Seem legit. However, there's something else That's broken Yeah, kind of don't trust the co-pilot so much Can pop the different examples into gpt again. I think we have Enough of them Try not prompting at all But just do the output the errors and the code As a prompt See what it says The error message you're encountering object of type n 64 is not jason serializable Typically occurs when you attempt to directly serialize numpy data types into jason format using js onify To resolve this you need to convert the numpy types to native python data types before serializing them In the process underscore data function when the pr and rt intervals are calculated They're returned as numpy in 64 types if they're not none To fix the serialization issue you should explicitly convert these to python integers or floats as intervals can be fractions If they're none you should handle them appropriately to ensure that they're in a format that can be serialized to jason Here's how you can adjust the process underscore data function to do that Note that i've used float to convert the intervals since intervals can be fractional in some cases But if they're always whole numbers in your application you could use int instead Make sure to update this conversion in any other place where you might be returning numpy data types that need to be serialized into jason For the calculate underscore intervals function you have a new implementation So just ensure it's called correctly in process underscore data and that it is returning the intervals in the format you need Also, it seems you have a potential issue with the peak detection logic If you have only two peaks detected like in the last error message Then you're expecting pr and t waves then you need to revise your peak detection or handle cases where you don't have all the expected peaks Finally after you've made these changes make sure to thoroughly test your application with various ecg data inputs to ensure It's working as expected and handling edge cases properly Also, it seems you have a potential issue with the peak detection logic If you have only two peaks detected like in the last error message And you're expecting p r and t waves then you need to revise your peak detection or handle cases where you don't have all the expected peaks If the peak detection logic is not consistently finding the p r and t waves in the ecg data You will need to address this issue to ensure accurate interval calculation Here are some steps you can take to refine the peak detection Dynamic thresholding instead of using a fixed proportion of the maximum value of the waveform like 0.5 You could implement a more dynamic approach that considers the variability in the waveform For instance, you could set the threshold to a value based on the noise level or the baseline wanderer of the ecg Adaptive distance the distance parameter in skippy signal find underscore peaks can be dynamically adjusted based on the expected heart rate or the sampling rate of the ecg This can help in distinguishing between consecutive r peaks and avoiding the detection of t or p waves as r peaks Waveform segmentation if the waveform contains multiple heartbeats segment the waveform into individual beats before peak detection This can help isolate the p qrs complex and t wave of each beat Post-processing of detected peaks after initial peak detection apply additional criteria to classify the peaks into p r and t waves For example, you could use the knowledge of typical ecg morphology to identify which peak is likely to be the r wave based on its amplitude And then look for p and t waves relative to the r waves position Error handling implement logic to handle cases where not all waves are detected This could include returning a flag or a special value indicating that the interval can be calculated, which your application logic can then handle appropriately Machine learning approaches consider using machine learning algorithms that can learn to identify p r and t waves from labeled ecg data This can be more robust than threshold based detection, especially if you have a diverse dataset for training Here is an example of a revised detect underscore peaks function with dynamic thresholding in adaptive distance It keeps reverting to r r interval Even though pretty sure I said that we Should not be looking at it because we have the r peaks coming individually one Waveform at a time I mean we can just connect them to kiever Then we could look at the r r as well And the bot is not doing very well at the moment. Is it a couple couple of issues? Yeah, preprocessing ecg looks tempting As sampling rate is defined by the number of data points Auto nerf to gpt is really playing Yeah, we don't like these magic numbers Some basic suggesting a github copilot suggesting some basic but above filter We are not currently doing any filtering. Yeah filtering actually. Yeah, probably don't really Needed well not at least not for this application We adding synthetic noise And we actually want to show how the algorithms performing poorly when the noise is increased So we don't want to filter Yet this one might need another couple of sessions Some simple thing that we could fix right now this display Is not working correctly We do another session Just continue with this one The front panel the html is always showing the same thing Back and seem to work correctly Can you suggest a fix? Looking specifically at the code We have You should have all the code see above Just finish up Soon If you can summarize the whole code that will be great Certainly The back end logic seems to be working correctly and calculating the intervals as expected The problem appears to be related to the communication between the back end and the front end From your provided code the back end is sending the calculated pr and rt intervals correctly However, the front end seems to be expecting string values Underfined for cases where intervals are not calculated Which is evident in this snippet from your flask root If the intervals are calculated as 240.0 This is a floating point number and the check for none will not trigger the undefined string to be returned Instead it should directly send the calculated values to the front end The front end html JavaScript should handle the display logic If the back end sends none or non-numeric value then the front end should display undefined or any other placeholder text This way you maintain the separation of concerns where the back end is responsible for data processing and the front end for display Here's how you can modify the back end to send none if intervals are not calculated And in your calculate underscore intervals function ensure that it returns none if intervals cannot be calculated Yeah, I don't know what's happening to Open the website weird stuff student Hey, we are having the same problem Okay, I might finish up say bye. Bye just uh, yeah, especially When the GPT is not working might try copilot I'll see you next time. Bye The issue seems to be with how the pr underscore interval and rt underscore interval are being handled when they're none The flask application is trying to convert none to a float which is not possible and should be avoided In the process underscore data function, you're correctly checking if pr underscore interval and rt underscore interval are none And setting them to undefined if they are However, in the analyze underscore waveform route, you're trying to convert them to floats without checking if they're none Here's the fix Remove the float conversion in the analyze underscore waveform function for pr underscore interval and rt underscore interval Directly assign the values from processed underscore data to processed underscore data underscore converted as you already handled the none case in process underscore data Here's the corrected code snippet Ensure that the front end javascript code that reads these values can handle both numeric and string undefined values appropriately Also, make sure that the calculate underscore intervals function returns none when the intervals cannot be calculated This logic is already in place in your provided code Finally, make sure that your front end template that displays the pr and rt intervals can handle these undefined values correctly If it is expecting a string undefined and you are passing none You might need to update the front end code to check for none and display undefined Accordingly the javascript code handling the pr underscore interval and rt underscore interval should be adjusted to correctly display the values when they're not undefined Here's how you can update the display analysis result function to handle this In the above code. I am using the to fixed to method to format the interval values to two decimal places This method is only called if the pr underscore interval and rt underscore interval are not undefined If they are undefined it will simply display pr interval undefined and rt interval undefined respectively Make sure that the ids used in getElement by id match the actual ids of the elements in your html If they do not match the correct values will not be displayed After you update your javascript file ensure you clear your browser cache or do a hard refresh control plus f5 on most browsers to load the updated script Normalizing the pr and rt intervals relative to the entire length of the waveform is a sound approach It will give you a ratio of the interval length to the waveform length resulting in a value between zero and one Here's how you can implement this normalization in your process underscore data function in python Now the pr and rt interval values will be between zero and one However, please note that this normalization assumes that the interval of interest pr or rt Is relevant over the entire length of the waveform which might not be the case in actual ecg analysis To reflect these changes in your javascript code You would need to update the display analysis result function to handle the normalized values This modification assumes that the pr underscore interval and rt underscore interval are already normalized when sent from the back end If they're sent as none from the back end, they will be represented as undefined in the front end and handled accordingly Normalizing the pr and rt intervals relative to the entire length of the waveform is a sound approach It will give you a ratio of the interval length to the waveform length resulting in a value between zero and one Here's how you can implement this normalization in your process underscore data function in python Now the pr and rt interval values will be between zero and one However, please note that this normalization assumes that the interval of interest pr or rt is relevant over the entire length of the waveform Which might not be the case in actual ecg analysis To reflect these changes in your javascript code, you would need to update the display analysis result function to handle the normalized values This modification assumes that the pr underscore interval and rt underscore interval are already normalized when sent from the back end If they're sent as none from the back end, they will be represented as undefined in the front end and handled accordingly All right, let's break it down Your app is essentially an ecg waveform game backed by flask server with a side of fuzzy logic for analysis A biomedical engineer's equivalent of guitar hero, but instead of strumming to through the fire and flames, you're identifying aberrant pq rst sequences It's pretty niche, but hey, everyone's got their jam The flask app does the heavy lifting It processes the waveform data from the post requests and calculates key metrics such as the amplitude peak sharpness and those elusive pr and rt intervals The fuzzy logic part is like the quirky judge of the game assessing the normality of the waveform data using rules to find in the back end It's the simon cowl of waveforms if you will We had a hiccup with the pr and rt intervals initially showing up as the infamous undefined duo like a disappointing plot twist in a medical drama But with a bit of python surgery and javascript physiotherapy, we got those intervals normalized and displaying properly Now they're more informative providing a relative sense of duration against the full waveform The changes we made today are like giving glasses to someone who's been squinting at a blurry ecg. We brought clarity and precision We normalized the intervals So now they make sense in the context of the waveform's full length making the game's feedback more insightful for players or budding cardiologists In terms of utility this app sits at the crossroads of education and verification It's a tool that could help medical students professionals were even enthusiasts to hone their skills in ecg interpretation The fuzzy logic adds an is critique providing a score and feedback So while it's not saving lives directly it's training the people who will Plus it's probably more fun than reading a textbook or attending a four-hour lecture on arrhythmias And if we're being funny think of it as dance dance revolution for cardiology nerds Instead of dancing mats, you've got waveforms instead of beats. You've got beats. They're hard get it It's all about staying in rhythm and making the right move or click in this case Overall, this app is one of those ideas that sir out there. It just might work It's like if your ecg machine a new gaming console had a baby and now are teaching their baby how to read heartbeats Welcome to the future of medical training where learning feels less like a palpitation inducing exam and more like a Network error your app is a unique blend of gaming and medical training using flask for back-end operations It analyzes ecg waveforms submitted via a web interface applying fuzzy logic to determine abnormalities Players interact with the game aiming to correctly identify abnormal ecg patterns, which is scored by both human input and an automated fuzzy logic system The app's educational value lies in its potential as a training tool for recognizing ecg anomalies fuzzy logic tuning Rule refinement the fuzzy logic rules may need more precision They should be based on clinically relevant criteria for ecg interpretation If the current rules are too broad or not clinically accurate, they won't provide valuable feedback Variable adjustments fine-tune the membership functions for your fuzzy variables You may need more granular categories or different shapes for the membership functions Expert input can solve with cardiology experts to adjust the rules and membership functions to better reflect the nuances of ecg readings data enrichment Real ecg data train your fuzzy logic system with a data set of real ecg readings that have been annotated by experts Feature expansion you may want to consider additional ecg features that can be incorporated into the fuzzy logic analysis such as qis duration d-wave morphology and same segment changes algorithm complexity Machine learning depending on the scope integrating machine learning could provide a more robust analysis by learning from a large data set of ecg's This could complement or even replace the fuzzy logic component if it proves more effective Hybrid systems use a combination of fuzzy logic and machine learning where fuzzy logic handles clear cut cases the machine learning deals with more complex ambiguous patterns user feedback loop Learning from mistakes implement a feedback loop where the system learns from the hits and misses of human players refining the fuzzy logic rules over time Difficulty levels introduce different difficulty settings Which can help in training users progressively and also aid in fine-tuning the fuzzy logic systems performance performance metrics Details scoring provide more detail feedback on why a certain waveform was marked as normal or abnormal This would be educational and could help in debugging the fuzzy logic system Benchmarking regularly benchmark the fuzzy logic system against a set of known ecg's to monitor and improve its performance interface enhancements Visual feedback offer real-time visual cues highlighting the segments of the ecg the triggerder hit or miss in the fuzzy logic system Interacted tutorials create tutorials within the game where users can learn about ecg features and see how the fuzzy logic system Alesis them by a trading on the fuzzy logic rules incorporating expert knowledge and possibly integrating more advanced AI techniques The app could become a powerful tool for both education and preliminary diagnostic support