 one that's actually generating the waveforms. They're simple math functions, nothing fancy at the game logic. Here I start putting it into GPT-4 because we can't code GPT-4 can better than GitHub Copilot. So we have the HTML code, the waveforms, JS, generating the actual ECG normal, abnormal waveforms. And we have the bytes and scripts. So now the context, it's not the context length, but the window size for GPT-4 on the OpenAI website is very long now. So we can try a single shot prompt. Let's run the application as well. This is what it looks like. You can still play it yourself. Hopefully you already know how or tried it out already. Right, currently we're getting this error as well. So hopefully it should be pretty straightforward for GitHub 4. Well, you would think GitHub Copilot will solve this pretty quickly. But the last time I tried, it didn't do a good job and it's prompted some more. So hopefully this prompt is not too long. We are sharing all the code for this Flask application. By the way, we're developing it while live streaming. So if you like being a co-host, please let me know. If you have any funny jokes, go for it, but keep them relevant to the topic. The Flask application has these four different files, the HTML to JavaScript files and the Python backend Flask application that does all the fuzzy logic analysis. Essentially a robot that is playing the ECG scoring game. Currently we're getting 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 our valuable viewers can access the application, play with it and provide feedback. Okay, with the jokes, you have to be careful. I heard this one like thousand times already. So if you're going for a joke, maybe make sure it's not in your training data set already. Now regards the code. Are 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 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. Okay, 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. Can you also look at 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 removed the frequency feature because it wasn't doing much. Keep in mind that we are not looking at a continuous ECG. We are looking at individual PQRST 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. It's working okay now. It's actually producing the results. This is the analysis of the fuzzy logic and says this signal is abnormal, which is wrong. It also gives a number of peaks of 29, which is no good. If you reduce the noise all the way down to zero, it produces the correct number of positive and negative peaks. The bot is essentially clicking on everything and making a decision that everything is abnormal, including normal waveforms like this one. This is no good. We'll have to fix that. Okay, we need to show the APR intervals on the front page as well. Let's try workspace. Can we show the PR and RD intervals on the front panel? Should it direct us to the game logic JavaScript and the index HTML? It was meant to be clickable links. Yes, so we're still in the bottom code. We are returning these two. Yes, we're looking at the absolute, the number of positive peaks, the number of negative peaks, the R peak sharpness. Yes, and we're using JSON responses to communicate. This is better. It's in JavaScript. It's making up code. It's not actually looking at the file. It's because it's only looked at the Python code. We have 200 lines in the game logic. Yes, we really did this. And let's result function. Yes, those two look legit. Want to display them on the front panel. 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. Can we use this instead? That's a comment. Right, this is better. Okay, we have those two undefined, even though we actually have zero values in the backend. That's interesting. Any major errors in JavaScript? No. Let's do this quickly. Sounds, looks like we're going to go back to GPT-4 in a second. Look, yeah, we're definitely not looking at the frequency anymore at not normalizing amplitude. Do you want to console the data mainly that we can see the PR interval? No, it's actually zero. Don't know why it says undefined there. It should give zero. Well, even if it is zero, it's not calculated correctly. So it should not be zero. Let's try to reduce noise that always helps to test. This interval should not be zero. No, we don't want to check if it's zero or not. Calculate the intervals. We have a function for it, taking the data and then input of what type of interval we are looking for. We want to make sure that function is working correctly. Can we get GPT to something useful? A copilot doesn't not manage the code. Can you regenerate the following function with the necessary changes? Yep, GPT4 by default is not designed doesn't want to generate code. But once it starts generating code then you can't stop it. This piece of code meant to be using the detect peak function. This function, a calculate intervals function meant to be using detect peak function. I provided the whole code that we have so far, can you fix it? With the current code I get the following error. 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 defined as a point that is higher than its immediate neighbors. In this configuration, looking at the image provided we're only getting one peak, which is not right. We have to adjust 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. Maybe distance between successive peaks can be automatically determined as well. Any suggestions, can you rewrite this function? We provided both this image. It's only found one peak with the current configuration. Hopefully it can rewrite the whole function. Now it's finding more peaks. We'll reduce the noise level to zero just for testing. It's finding two peaks, another one to be three. We just test 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 it, PQRSTPQRST is the last one. So now we should be able to calculate the distance between them. But for some reason PRRT intervals are calculated to be zero. That's not right. Yeah, so this one is the RNT. So we get 320 and 560, which is about right. Okay, this 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. Let's use it, pop in the whole code just because we can, because 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 complication backend that we currently have and the output. So we have three peaks. We have the indices for them. They seem to be correct. However, the PRRT intervals are still calculated to be zero. It's actually seemed to work quite well. The PRR intervals now seem legit. However, there's something else that's broken. I 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. It keeps reverting to our 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 Kieva and we could look at the RR as well. The port is not doing very well at the moment. There's a couple of issues. Pre-processing ECG looks tempting. The sampling rate is defined by the number of data points. We don't like these magic numbers. We can have co-pilot suggesting some basic butter-warf filter. We are not currently doing any filtering. We don't really need it. At least not for this application. We're adding synthetic noise and we actually want to show how the algorithm is performing poorly when the noise is increased. So we don't want to filter. This one might need another couple of sessions. Some simple thing that we could fix right now. This display is not working correctly. The front panel, the HTML is always showing the same thing. The backend seemed to work correctly. Can you suggest fix looking specifically at the code we have? You should have all the codes above. We'll just finish up soon if you can summarize the whole code that will be great. 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 undefined for cases where intervals are not calculated, which is evident in this snippet from your flask route. 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 a 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. I don't know what's happening to open the website. It's like, weird stuff. It didn't. And we're having the same problem. Yeah, I might finish up saying bye-bye. Just, yeah, especially when the GPT is not working. Might try copart. I'll see you next time. Bye. It's pretty niche, but hey, everyone's got their jam. 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 defined in the back end. It's the Simon Cowell 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, or 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, or heart, get it. It's all about staying in rhythm and making a right move, or click, in this case. Overall, this app is one of those ideas that serve out there. It just might work. It's like if your ECG machine into a 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 and terminal abnormalities. Players interact with the game, aiming to correctly identify abnormal ECG patterns, which are 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. For example, you may need additional 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, consult 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 fuzzy logic analysis, such as QIS duration, T-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 ECGs. 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, and 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 pairs, 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 system's performance. Performance Metrics Details Scoring Provide more detailed 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 Benchmark the fuzzy logic system against a set of known ECGs to monitor and improve its performance. Interface Enhancements Visual Feedback Offer real-time visual cues highlighting the segments of the ECG that triggered a 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 aliases them. By attrating on the fuzzy logic rules, incorporating expert knowledge, and possibly integrating more advanced AI techniques, the app could become a preliminary diagnostic support.