 Maybe not. No, I just have to speak louder. Okay, this should be better. Yeah, I want to speak software. Softer, because otherwise I'll lose my voice. So I know the microphone should not be directed straight into my mouth, but being on a slight angle. Okay, so we are live in theory everywhere on YouTube. Also Twitch and LinkedIn as well. So I do monitor the messages. This is what I'm doing this for, meant to be interactive. So please do share your thoughts. Especially if you check the Bionicaos.com already, the speech to text algorithms that I'm using, like the Google ones. There is no live transcription going on, but later YouTube does audio transcribing. It can never get the name of the website. So I'll just refer to it as the website. So there is a new tool on it. It's a bit of a game. We can generate music using your hands. Actually, I've seen a video of a tool that apparently was invented somewhere in Eastern Europe, that actually has the big antenna to it. I can ask GPT about it. Maybe if no one wants to chat, I have to resort to chatting to the bot. I wonder if it can browse, just mute the site for a second. Yeah, this whole tool, the music generation bit, it's all working. Yeah, there are ads. Yeah, Samsung ad is not too irrelevant. So if I unmute it quickly, yeah, this should be softer sounds. And then you have your musical scale. And it helped me generating the tool, so you might as well explain it. It uses advanced computer vision and audio synthesis technologies, like Media Pipe and Tone, JS, real-time tracking and sound creation. Key features include real-time hand tracking, dynamic sound synthesis, interactive visual feedback, recording capability, and a user-friendly interface. It works best on modern browsers with a stable internet connection and good webcam quality. The application is continuously being updated, and the source code is available on GitHub under the MIT license. All right, that is correct. So we covered it in previous streams for several times. We were developing it live as well. Where is this camera? I want to do something with making it more interactive in a sense that you have more controls in here. Can you find similar tools online or offline? So there is this musical instrument that you can play without touching. I forgot its name. Can you check it? The instrument you're thinking of is called the Thurman. It's a unique electronic musical instrument controlled without physical contact. The Thurman is one of the earliest electronic instruments invented in the 1920s by Leon Thurman. It works by generating electromagnetic fields around two metal antennas, one for pitch and the other for volume. As the player moves their hands in the air near these antennas, they can control the sound produced. It's known for its eerie, otherworldly sound, often used in movie soundtracks and experimental music. The Thurman is played by moving your hands in the air around the device without ever touching it. It's a fascinating blend of music, physics and art. You know what are the controls that are available on a Thurman device? Yep, let me spell Thurman. Sure, let's break down the controls on a Thurman. Pitch antenna, usually a vertical rod on the right side. As you move your hand closer to this antenna, the pitch of the note increases. Moving your hand away lowers the pitch. It's highly sensitive and can detect subtle movements. Tuning controls. Pitch tuning knob adjusts the range of pitch that the pitch antenna can control. This can be set according to the player's arm length and playing style. Volume tuning knob, similar to the pitch, this knob adjusts the sensitivity and range of the volume antenna. Some models have a control to change the waveform of the sound, altering its timbre. This can range from a sine wave, smooth sound to more complex waveforms for a richer or more electronic tone. Brightness control. This might control the harmonic content of the sound, making it brighter, more high frequencies, or darker, more fundamental frequencies. Basic controls for turning the instrument on and off and setting the overall output volume. Headphone and output jacks. For connecting to an amplifier, recording equipment, or headphones. Modern Thurmans might include additional features like preset storage, MIDI capability, or more advanced turn shaping controls. The basic playing technique involves fine motor control and a good ear for pitch, as there are no physical keys or frets to guide the player. It's a unique and challenging instrument, but offers a lot of expressive possibilities. Yeah, we might try and improve this tool next time. It's more of a weekend project this one. And now just jump back on to more biomedical data stuff. Which I mean, your hands, your gestures on camera and stuff is biomedical data as well. But jump into biomedical data. We're doing our own tools. So we're currently working on this fuzzy logic integration with the ECG game. If you haven't checked the ECG game, please do check it out. The fuzzy logic tool currently not working. So there will be a version of it, which will be essentially a bot machine learning algorithm playing the game. So get to that in a second. So I keep it digressing. Do a quick check of this file a friend, a LinkedIn friend sent to me. And just an interesting observation on it. Not the new chat quickly. So that's a file. Eventually I'll turn it into a potentially JavaScript or a flash application. So ideally I turn stuff into JavaScript. So when it's running on the website, I don't have to pay for electricity of the server because JavaScript is front end. It's running on your local machine. The other obvious advantage to it is that you don't need to send me any files or upload them. You can just essentially load them into your own browser. So essentially your browser can be used as a signal review software. So if it's your EG or ECG file, you just load it into a browser and it just works. So if necessary, we can do things on the back end if there's more complicated processing required. And then we'll be turning it into a flash application. So for example, the music to hand gesture, the camera footage is everything. There's nothing sent to the server of the source code is in the browser as well. I don't think it's on GitHub yet. So you can see exactly what it does. And none of the face or incoordinates, no data is being sent to the server. I mean, in tier it could be a flask application. And then the server just receives information essentially like something basic, like how long you used your device. Because I mean, that information is sent to Google anyway. So when you use this tool, play music with it. Oops, it's still actually going to mute this site. So yeah, it's because it's running ads or whatever. Yeah, there's information going to Google just in terms of like how long you've been on the site and so forth. So I could with a flask application get this information as well. Let's jump quickly into this file. Just run it quickly. There's couple important things about recording EEG. So to load this EEG, just go over this code. Quickly it was generated by GitHub Copilot. Well, it's actually available on Bionicaos, but you need the file to go with it, which I might need the permission to use. I do have a permission to talk about it in the live stream, so that's fine. So with the trend that was originally something, for example, if you remove the trending from data, which essentially brings the data to around zero. That's how the original data was recorded. So you can tell channel zero is kind of around zero. Something is off with channel one. The filter ring still works. Well, that's because the filter is actually removing a low frequency using a quick high pass at the cutoff frequency of 0.1. So it's not aggressive at all, and it does the trending for us. So essentially this means that we potentially don't even need to detrend. I don't know how much resources that function is taking, but we might not need it at all. Well, actually when we do the trend, it will make this chart of the raw EEG recording a bit better. So we can see what's going on. So I have to do this every time. We will eventually see how we go. There is an interest in this. So this file was sent to us as an open BCI raw file. So I know a lot of people using open BCI gear. So the first lines is the header. Right, it's actually checking for the header. It's lines starting with the speed how it works. Remember how they're just doing a read lines. Can we plot and print a first 10 lines? Let's run this quickly. Yeah, we set a terminal. It's not great for a viewing. Right, so it's actually, yeah, it found it somewhere. So we're not using open BCI Python library. Well, because ideally we want, if we eventually turning it into a Flask application or something, we want to make it generic. So you can essentially load any file and it will display it for you. Ideally we want to do it in JavaScript as well. Print the header. When it's suggesting something, it's commenting it out by default. Right, so we have eight numbers of channels. I think only two of them are being used. Sampling rate of 250 Hertz. And this open BCI GUI board site and Wi-Fi was used. We used this board before, apparently, yeah, connected to Wi-Fi and recording this type of EEG, which can be quite noisy. Let's go over the rest of the code. And we will talk, touch on a couple of different filter types. So the standard one, and this is also what the bot suggested, the LLM suggested is using a bottom wharf filter. The order here is five by default, but I'm actually reducing it to one. You have the sampling rate and the cutoff frequency of 0.1. So it's working fine. I'm getting rid of this DC component in the signal, and I'm feeding the whole 10 seconds into it. So normally, depending what your application is, or how long the data is, you'll probably do smaller window size. But for this display, just checking the file should be good enough. We're applying the filter, plotting the data, the original, and the filtered one. And then, so if we close this, we will have another window loading in which you can actually scroll at the 10 seconds of the data, 10 seconds being defined. I know, sir, it's 100 seconds, so the whole recording is 10 minutes. And this will be 100 seconds. I might add the controller here for changing the window size. Just a standard thing to do. It's like loading e.g. 101. Let's do it quickly. Can we add a controller for changing the window size when scrolling? I'm used to JegePT that will actually let you finish your sentence. And actually, why not make this figure two? So both of them can be opened at once. Also, there are two figures plotted one after the other. Can we open them both at once, make one figure one and the second one figure two? I have to speak quicker, quickly with this one. The JegePT tool is more forgiving. It will just let you talk forever. Let's see where are we plotting. And later I'll talk about the importance of selecting your filter correctly and not over filtering because it can have all sorts of unwanted effects. Not alone ringing that the filter can introduce but also potentially other even worse things that could lead to mislabeling the data. I'll show you what I mean in a second. Actually, we want to uncomment the second filter, the Chebyshev filter and apply it as well. Sorry, I had to sneeze. It's good that you can turn the microphone off. Okay, we want that the Chebyshev filter at the bottom of the code will uncomment this. It's using even a smaller cutoff. Let's keep it at point one. Same as the other filter. What is this called? A butter wharf. So same as butter wharf. Why is my tab fault for browser is that tabs are unmuted. I have to mute the tabs. That helps a lot. Okay, so we have this another filter option. I'm going to sort out the plot. So we have plot one and two. A figure one and two. Figure one and two. Not figure one. So instead of just the subplotting, instead of just subplotting filter data, so it's this first plot. The two plot shows no tight layout. Slide that on. Okay, where's the... Or inserting the code. Try that. So we have figure one, two and three. Okay, so this shows the raw eG, 10 minute eG. That's the filtered version of it. This one is the same filter, but one that you can scroll through. Ideally, we'll have an option here to just... Anyway, we might be doing it all in JavaScript later. So you can just upload a file on the website. This will all change. But what I wanted to show is these things that might look like some sort of artifacts physiological. I want to show that they actually not. They actually to do with those DC drifts. So for example, at around 200, you have this big DC drift. Yeah, the other two windows will scale at the same time, but we can fix it later. Yeah, so those are these artifacts there. Yeah, I suspect they're not physiological, but I don't know, there's something wrong with this recording. So obviously there's EMG in it. You can see it over here. So say around 300, an obvious EMG. Well, over here, it's that bit there. I don't know where those spiky bits are coming from. But yeah, I'm just trying to figure out if they are physiological or not. And I don't think they are. There's just something wrong with the recording. Because if you had little ones like this, they could have been eye blinks or eye movement. And depending where the electrodes are, I don't exactly know where the electrodes are located. But anyway, I'm just saying that this is unlikely to be physiological. It's something to do with the electrode not being well positioned. Because for example, you have one here. You have a clipping classic. A clipping at around 440. That's this bit here. This is how it looks like after filtering. So yes, the DC component was removed, but you have to be absolutely careful about it. Because again, it might just look like, especially after filtering, it might look like an eye blink or something or eye movement. But it's actually not. And if you're filtering too much, it might even look like epileptic seizures. Which I've seen happening before of things being misdiagnosed because of artifacts like this. Right, because I'll show you an exact example of how it looks like. If we are taking the... So we have this Chebichef high pass. We'll name this data differently. We'll have a chip filter. Filter data. And then we just have a filter data. It will be a more common Butterworth filter. Hey, we want to be plotting. No, plotting filter data and chip filter data. So suppose we're adding another figure. Okay, that's just a plot. That's the second plot. That's the second plot. Yeah, co-pilot. That's a really good job. So we'll see in a second if it's a bit working or not. Plot show. I just don't know what the figure number A this would be. And it won't run because you have to close the previous plots. That's just how mudplot live works. Okay, that's all. It's meant to be plot. We're already having two A. This could be a figure. Two must be command for closing them all. Right, so here for example. So that's the Chebichef. Now it did end up plotting three of them. Let's plot it again. That doesn't work because you have to actually close them and for plot the third one. Okay, and the Chebichef filter does this funny business of applying an envelope to the signal. Is it because it's too aggressive? It might do a cutoff of points. How many plots are being generated? Yeah, there should be three. Are you sure there are no three figures? Every time, yeah, that's the problem with LLMs. Every time you ask them something, depending on your question, you might always have the answer in the question. So the figure, the three plot figure one. I assume it's talking about this one. The three figures. Second figure is empty. Select the whole code. Why the second figure is empty? Have a plot figure two line. Go away. Just should have that. Which is what we had already. Should run it again, right? Well, anyway, I don't remember how I did it before. That's why I'm doing live. So I actually don't forget. It's a bit of a, it's called the lab book. Virtual lab book. There was a setting that we have figure two. Anyway, long story short. This is the original EG data. This is the filtered data that we should have shot. Right. So these two. So we have a regular filter, which is, which is, so we have two types of filters. Yeah. One is bottom wharf and the second one is chebychev. And there's a few things here to note. That the chebychev filter being more aggressive. That's this wicket stuff. And one second. Why does envelopes? Anyway, I cannot reproduce what I showed before. Can just describe it. Some of those artifacts, which I do not think are biological in the signal, with a certain filtering type, all of a sudden looked like more biological. So that that's all I was trying to say. And there is no really good way of showing it. I don't know if you have any suggestions, comments. Otherwise, I might move on to my stuff. To what we were previously doing. Let me understand how the comments are working on LinkedIn. Yeah, LinkedIn is just really bad for streaming. I think my RPM on the website is going up. That's good. This is a good time to check it out. Let's think the comments quickly. Okay, we'll move on in the same, we'll close this for now. But this, let me know if there's an interest for creating a tool like that that can compare filters or do anything else. Essentially be similar to what a combination of anything that is already on the website or something new. Also, let me know what current software do you use. So essentially be making like a simpler three to three of charge, a version of it that you could just use in your browser. Pretty much look anything you like. Questions about this. Quickly move on to a game. So making bot that will play this ECG game still work in progress. It's already doing something useful. So running it locally. So it's showing you an abnormality score. Which later, yes, we can turn into an actual response of it making a decision. This had an abnormality of 0.9. This wasn't 0.56. This is wrong. Okay. Yeah, but change something like that. They have normality for normal ECG, not like this, with another way from there should be 0.1. So we need to check what's going on. Currently does have a button back end. Well, the way this works is that the game is being played in front end. So for example, we have this index H1. This project is a bit complex. So yes, we might need help. So we are making a game called Cardio Quest. And we are making a machine learning algorithm, essentially a robot that will play this game. Now I'll be sharing some code with you. Please do very quick responses. If you want to say something funny, go for it. But keep it short and no emojis. All right, let's dive into your code for Cardio Quest. Here's a quick rundown. Metatags, good use of org tags for SEO and sharing. But you've got duplicate org, image tags. Decide on one image for consistency. External CSS and JS need that you're linking to external styles and scripts. Just ensure the paths are correct. Relative paths can be tricky. HTML structure, look standard. Header, navigation, and main content. Nice and orderly. Canvas element, central to your game. Make sure it's well integrated with your game logic. Analysis results display, good idea to have real-time analysis results. Ensure the backend is robust for live data processing. Data display elements, nice touch showing different data points. Just make sure they update correctly in real-time. Game description and rules, clear and concise. Players will know exactly what to do. Adscript, delayed loading is smart, won't affect initial page load. But keep an eye on how ads might impact user experience. Game mechanics, the concept of increasing difficulty with a score is great for engagement. Just balance it well to keep it challenging yet not frustrating. Accessibility and responsiveness, consider adding some RA labels for accessibility and ensure the game is responsive on different devices. As a biomedical engineer, I appreciate the educational angle on ECG patterns. Just a thought, integrating real ECG data could be an interesting twist, maybe for an advanced level. Keeps it educational and challenging. This is great, but can I first share the entire code with you and then you make your comments and things on the whole thing that is accept this HTML. There's an additional two JavaScript files and another Python backend Flask application. All right, let's break down your waveforms, JS file. Waveform generation functions. You've got a solid setup for generating different ECG waveforms. Using Gaussian functions for our waves and sin functions for other waves is a smart move. It should mimic real ECG patterns well. Variations in waveforms, introducing variations like QR's variation 1, QR's variation 2, No-P wave, etc., is a great way to add complexity to the game. It mimics real world ECG anomalies, which is both educational and challenging. Mathematical modeling, your use of trigonometric and exponential functions to model ECG waveforms is spot on. It's clear you've got a good grasp of how ECG components can be mathematically represented. Generate waveform function. The random element introduced here is key for game dynamics. The decision to make a waveform abnormal with a 60% probability is interesting. Just make sure it's not too predictable or too random for the player. ECG Waveform Array. Storing these in waveforms provides a nice way to access pre-calculated waveforms, which is efficient. Code clarity and readability. Your code is well structured and readable. Comments are helpful, making it easier to understand the purpose of each function. A couple of suggestions. Testing different scenarios, ensure you've thoroughly tested all waveform variations to see how they look and behave in the game. Realistic ECG patterns are crucial for the educational aspect of the game. Performance optimization, consider the performance implications, especially as the game progresses and waveforms get more complex. Optimizing your code for smooth rendering is key for a good user experience. Now, if you have more code or the Python backend review, feel free to share. Yes, as I said, there is more code. Can you wait with the responses and keep them super short and funny if you have to? This is the game logic script. Alright, here's a rapid fire. Slightly humorous taking your game underscore logic, JS. Canvas setup, classic. You're painting your digital canvas like Van Gogh with a mouse. Colors, blue, green, yellow, red, black, looks like you're ready for a paintball game. Score mechanics, starting score of zero. So, everyone's a winner from the start. Drawing waveforms, drawing lines on a canvas, are we all just fancy-edged sketch artists? Click mechanics, clicking on waveforms. It's like whack-a-mole but for future cardiologists. Score calculation, score goes up and down more than my mood on a Monday morning. Game speed, speed increases with score, so, the better you are, the harder it gets. Just like real life. Updating scores, hits, false alarms, correct rejections, misses, sounds like my dating history. Game loop, R, the heartbeat of the game. Keeps going and going. Energize a bunny, anyone. Waveform analysis, sending data for analysis. Just don't let Skynet get ahold of this. Displaying analysis results, spitting out data like a gossiping printer. Start the game loop, and we're off to races. Seriously though, your game logic script is well structured and the flow seems smooth. The combination of gameplay mechanics, scoring system, and real-time waveform analysis can make this an engaging and educational experience. Just ensure the game remains responsive and fun as the complexity increases. Happy coding. Okay, might wanna turn the funny few notches down. Maybe keep it to one joke at a time. Yeah, I forgot about the colors. Originally the waveform was changing color if you were making a wrong decision. But I don't think we do that anymore. That's fine, it's okay to leave it as is for now. Here is the back-end Flask application. Okay, let's jump into your Flask back-end with one quick joke. Your back-end's like a secret agent, it's doing all the heavy lifting behind the scenes. Now, getting serious. Flask setup, standard Flask setup with roots for the main page and waveform analysis. Clean and straightforward. Waveform data processing, you're using NumPy and SciPy for data manipulation and signal processing. Perfect tools for the job. Global variables for ranges, keeping track of min and max values globally. It's like you're setting high scores in a video game, but for waveforms. FFT for frequency analysis, using Fast Fourier Transform for frequency analysis, a classic move in signal processing. Good choice. Peak detection, using SciPy, signal, find underscore peaks for detecting peaks. It's like finding Waldo, but in waveforms. Fuzzy logic for analysis, using fuzzy logic for data analysis is like having a wise sage making decisions, not always black and white, but often white. Fuzzy rules and membership functions, nicely defined rules and membership functions. This is where the magic happens, turning raw data into insightful analysis. Normalization function, you're keeping things in proportion with normalization. Essential for comparing apples to apples. Error handling, including a try except block for robust error handling. It's like having a safety net when walking a tightrope. Debug mode, running the app in debug mode, great for development, but don't forget to turn it off in production. Overall, your backend seems well equipped to handle and analyze ECG data, feeding into your game logic seamlessly. Just ensure the communication between frontend and backend is smooth and efficient to maintain a responsive user experience. Keep up the good work. Okay, I just realized that it's actually working correctly. It's just that there is a learning element of it is that the thresholds, so say the amplitude thresholds change over time as new waveforms come in. So I thought you are transcribing clear, just pop it into another window, clear, clear, clear. So I just realized I was wrong before the abnormality score is actually working correctly. It is showing 0.1 for a normal ECG. It just seems like it needs some time for the thresholds to stabilize to work correctly. Could you explain that bit? And also, there seems to be a problem that sometimes the normalized amplitude output is not showing a value. Alright, let's address your observations. Abnormality score stabilization. The abnormality score taking some time to stabilize is likely due to the global variables who are using to track min, max values for frequency, peaks and amplitude. These values are updated as more waveform data is processed. Initially, the system might not have enough data to establish accurate thresholds, leading to less stable scores. As more data comes in, these thresholds become more refined, resulting in more consistent abnormality scores. It's a bit like your algorithm needs a warm-up session before hitting its stride. Normalized amplitude sometimes not showing. If the normalized amplitude occasionally shows no value, it could be due to a few reasons. Division by 0 in normalization. If max underscore val and min underscore val are the same in your normalized function, the function returns 0 to avoid division by 0. This situation can occur if all your data points have similar amplitude values, especially in the initial stages or in cases with very uniform waveforms. Data range issues. If the range of amplitude data is too narrow or if there are outliers, it might skew the normalization process. Handling of edge cases. Ensure that edge cases are handled correctly in your data processing pipeline. For example, what happens if there are no detectable peaks? To improve the stability and reliability of your system, consider the following. Initial calibration with a diverse dataset. Before starting the game, you could run a calibration using a diverse set of waveform data. This would help establish a more accurate initial range for your global variables. Regular updates to global ranges. Continuously update your global min, max variables as more data comes in to keep the thresholds dynamic and adaptive. Robust handling of edge cases. Implement checks and fallbacks for cases where data might be atypical or sparse. Logging and monitoring. Implement logging for the amplitude values and normalization process. This can help identify patterns or specific cases where the issue occurs. Remember, biomedical signals like ECGs can be quite variable, so building flexibility and adaptability into your analysis algorithms is key. So even after the algorithm was running for a while and it got all the possible waveform types, I'm getting this output. Why is the normalized amplitude is not being calculated in this case? Based on the output you're getting, where the normalized amplitude is not showing up despite the algorithm running for a while, let's explore a few potential causes. Division by zero in normalization. As I mentioned earlier, if max underscore val and min underscore val in the normalized function are the same, the function returns zero to avoid division by zero. If your amplitude values are consistently the same, this could happen. But, given you have a raw amplitude of 199, this seems less likely unless all your amplitudes are exactly 199. Handling of edge cases, it's possible that certain edge cases aren't being handled correctly in your normalization function. For instance, what if max underscore val and min underscore val are uninitialized or improperly updated? Data processing bug. There might be a bug in the way the normalization function is being called or how the results are being processed and passed back to the front end. It's worth double checking the function calls and the data flow. Global variables not updating. If your global min, max amplitude variables aren't updating correctly as new data comes in, this could lead to an inaccurate normalization scale. Logging and debugging. Add logging statements around the normalization process to see the values of min underscore val, max underscore val, and the resulting normalized amplitude. This can give you insights into what's happening. To troubleshoot this. Review the normalization logic, revisit your normalized function and ensure it's logically sound. Double check the conditions under which it returns zero. Debug with static values, temporarily replace your dynamic values with static numbers to see if the normalization function works as expected. Add comprehensive logging, implement detailed logging to trace the values of all relevant variables and function outputs. Check data flow, ensure the normalized amplitude is correctly being sent back from the back end and properly received and displayed in the front end. Addressing these points should help you pinpoint and resolve the issue with the missing normalized amplitude. Yeah, it seems like the... Well, it's a given that the maximum value of 349 returns a normalized amplitude of 1. But then, for the lowest amplitude waveform with a raw amplitude of 199, I get no output for the normalization function. Would it help to not have the global mean amp and the global max amp variables to be plus and minus infinity? Yep. If you're not getting an output for the normalized amplitude when the raw amplitude is at its lowest, 199 in your case. And the global min, max amplitude variables are initially set to plus and minus infinity. This can indeed be part of the issue. Here's what might be happening. Initial values of global variables, setting global underscore min underscore amp to positive infinity and global underscore max underscore amp to negative infinity is a common practice to ensure that any real data point will update these values. However, if these global variables are not updated properly before the first call to the normalized function, it could result in a division by zero or another mathematical error. Updating global variables. When the first waveform data comes in, these global variables should update to reflect the actual min, max values of the amplitude. If this update isn't happening correctly, it could cause issues. Normalization with extreme values. If global underscore min underscore amp remains at infinity and global underscore max underscore amp at negative infinity, any normalization calculation will be skewed, potentially leading to undefined or erroneous values. To address this, Ensure proper initialization. Make sure that the global min, max amplitude variables are updated with real data as soon as possible. You might need to add a check to ensure they're updated correctly the first time waveform data is received. Debugging. Temporarily add print statements or logging around the points where global underscore min underscore amp and global underscore max underscore amp are updated. This will help you confirm they're being set correctly. Handling edge cases, in your normalized function, add a check to see if global underscore min underscore amp and global underscore max underscore amp have been updated from their initial values before performing the normalization calculation. If not, handle this case appropriately, possibly by skipping normalization or using a default value. Test with controlled data. Try feeding in controlled amplitude values to see how the system responds and to ensure that the normalization function works correctly across the expected range of amplitudes. By carefully managing the initialization and updating of your global variables and ensuring robust handling of edge cases in your normalization function, you should be able to resolve the issue with the missing normalize. To modify the initialization of your global min and max variables in a way that better handles the first set of data, you can start with more realistic initial values. Instead of starting with infinity and negative infinity, you can initialize these variables with none. This explicitly indicates that they haven't been set yet. Here's how you can modify it. Then, in your function where you update these global variables, you can check if they're none and, if so, set them to your current value. For example, this approach ensures that the first data point you process will set a realistic initial value for these variables. From there on, the variables can be updated normally with each new data point. This should help with the normalization issues you're encountering. This will definitely work better than what we currently have. Let's reboot a controller 5. We get 0.6.5 for 288A14349, which is the maximum amplitude that we have. We still get nothing for 199. Get any errors? No errors? No errors? No errors? I wonder if it meant to improve over time? So I still get no value for 199. I mean, I'm happy to assume that the minimum amplitude is 0. Shall we do that? So it seemed to be happy to generate code this time. If you're still encountering issues with the normalized amplitude, not showing for a raw amplitude of 199, and you're okay with assuming that the minimum amplitude is 0, you can adjust your normalization approach accordingly. This assumption simplifies the normalization process, as you're essentially scaling the amplitude based on its maximum observed value, with a known minimum of 0. Here's how you can modify your normalization function. In this revised approach, the normalized function now only requires the current value and maximum value. If the maximum value is 0, the function returns 0 to avoid division by 0. This handles the case where there might be no amplitude, flat line. The global maximum amplitude is updated with each new amplitude value. This method assumes that the lowest possible amplitude is 0, which is a reasonable assumption for many real world scenarios, including ECG waveform analysis. This should resolve the issue of not getting a normalized value for certain. So how is it different from what we are currently doing? Can you explain how is it different from what we are currently doing? Can you keep responses short if you want to make them funny? Go for it. Yep. Sure, here's the short and slightly funny version. Current method, it's like trying to find where you stand on a ladder that keeps changing its bottom and top rungs. Your current method uses both minimum and maximum values for normalization, making it a bit like a game of limbo where the bar keeps moving. New method, this is like always starting from the floor and seeing how high you can jump, ignoring how low you can go. We're just using the maximum value for normalization and assuming the minimum is 0, a bit like measuring how high you can stack pancakes without worrying about how thin they can get. In essence, the new method simplifies things by using a fixed starting point, 0, and only scaling based on the maximum amplitude observed. This approach should be more stable and predictable. No more limbo dance. Pancakes. And now you made me hungry. Okay, this is not cool. Don't use any emojis. Keep responses short. Can you rewrite the normalized function? Look at the original code. It should have three inputs. And what did you recommend? Yep. Sure, let's whip up a normalized function that's short and sweet, just like a perfect snack. Based on what we discussed in your original setup with three inputs, here's the revised function. We keep the three inputs, value, min underscore val, max underscore val, for compatibility with your existing code. Min underscore val is set to 0, as per our pancake stack strategy. If max underscore val and min underscore val are the same, like a flat line in ECG, we return 0 to avoid a division by 0. Otherwise, we normalize using the range between min underscore val, which is now always 0, and max underscore val. So, now it's like measuring the height of your pancake stack starting always from the tabletop. Pancakes trade ruler. Get the usage bit. The usage bit. Every time you mention something like not to do, it then picks up on that words. As if it's surely understand the context of not doing something. As in do not use emojis. Emojis. Okay, clear. Clear. You don't want to get into an argument, do we? With a bot. No. Hit that. Global mean amplitude can be 0. Yeah, we should have. For our current data set, we don't have the edge cases. We probably should add them. Let's see if it may be better. It's the first one. Always normal. It seems like this. No, it's not. That's the normal one. Yes, we're getting a wrong value for it to begin with, but then it should improve. Yeah, that 0.5 later should be 0.1. Okay, now we get a value for 199. Just 0.57. 0.57. Okay, so this is better. Okay, this is abnormal. That's the highly abnormal of 0.95. Yes, this bit. So just trying to make sure this makes sense. Otherwise we have to change it. Yeah, for some unknown reason, for an unknown period of time. I guess until it got all the possible waveforms. For a normal ECG, we get 0.56. A 0.56 abnormality value is not right. I think later it's correcting itself to show 0.1 for normal waveform, not like this. Yeah, that 0.56 values seem to be some sort of default. Mean frequency could also be 0. This 0.56424 should not be shown for normal ECG. Right, so not showing 0.1, which is what we want. So there is like a learning thing happening, but this learning thing is fake. It's a simple, there's no actual machine learning there. Or anything, it's just adjusting those thresholds because they are none to begin with. I'm just thinking. So for some time in the beginning, we get this result for a normal ECG. Whereas later, the score of abnormality for normal ECG does go down 0.1, goes down to 0.1, as expected. Considering this is not a real machine learning, can we adjust the initial global minimum and maximum variables? I'm happy for all the minimums to be 0 and all the maximum to say be 1,000. Should that fix this problem? Adjusting the initial global minimum and maximum variables is a pragmatic approach, especially when dealing with a non-machine learning system that relies on predefined ranges. Setting all the minimums to 0 and initializing the maximums to a reasonable estimate, like 1,000, indeed helps stabilize your abnormality scores more quickly. Here's how you can modify the initial global variables. This setup does two things. Quickly stabilizes scores. By setting a realistic initial maximum, your abnormality scores won't be overly sensitive at the start. Simplifies normalization. If the minimum set to 0, the normalization becomes more straightforward, as we're scaling values within a known range. This approach is like giving your system a head start in a race. It knows where to begin and has a clear idea of the upper limits, so it doesn't have to spend time figuring that out through trial and error. It's like saying, hey system, don't start counting from 1, start from 100, but remember, 1,000 is your limit. Remember, though, this method is a bit of a balancing act. If the initial maximum is set too low, you might clip values that exceed this range. If it's too high, you might not get the sensitivity you need initially. But given your context, starting with a max of 1,000 seems like a good middle ground. Check your flag rocket. And later it will be interesting to see when we add more... Okay, later when we add more waveform types of different abnormalities and different variations would be interesting to see if the fuzzy logic is still working correctly or not. Probably not. Can keep responses quick and simple. Yep. Absolutely. Adding more waveform types with different abnormalities will be a real test for your system's adaptability. It's like adding new flavors to a recipe and seeing if it still tastes good. You'll quickly find out if your fuzzy logic needs a tweak or two. Keep an eye on how those new waveforms affect your abnormality scores. If things start to look a bit off, it might be time for some fine-tuning. Happy experimenting. Test your bar graph thumbs up. You'll currently have 356 lines of code, including spaces, including empty lines, carriage returns, whatever they're called. And now the question is... So we are currently not... Are we currently using the abnormality membership function? Keep responses short. Do not use emojis. You're currently not using... No, the current code doesn't directly use the abnormality membership function. It calculates the abnormality score based on the fuzzy logic rules, but doesn't explicitly apply the membership function to this score. The rules determine the score, and the membership functions are more about defining the inputs for those rules. Straight ruler brain. Keeps bumping it with emojis. Okay, are we using... What membership functions are we using currently? And isn't the abnormality membership function an input or output? Also, how would you recommend using it? Yep, ideally it will make the decision. Currently, your system uses membership functions for four input variables. Frequency, amplitude, positive peaks, and negative peaks. These functions categorize the inputs into ranges like low, medium, high, or similar labels. The abnormality itself is an output variable in your fuzzy logic system, and yes, it also has membership functions. These functions define what constitutes, unlikely, possible, and, likely, abnormality levels based on the rules you've set. To effectively use the abnormality membership functions. Refine rules. Make sure your fuzzy logic rules accurately reflect how the input variables, frequency, amplitude, peaks, should influence the abnormality score. Adjust membership functions. If necessary, tweak the ranges in the membership functions of the abnormality to better align with what you consider as, unlikely, possible, and, likely, abnormality. Validate outcomes. Test with various data sets to see if the abnormality scores make sense. If the results are not aligning with expectations, you may need to revisit your rules or membership function definitions. In summary, the abnormality membership functions are crucial as they define the output of your fuzzy logic analysis. Ensuring these are set up correctly is key to getting meaningful and reliable abnormality scores. Did you actually make a suggestion as to how we can use them? I'm thinking in the game logic JavaScript on the front and in the same way we're doing a score calculation for the human clicking on the waveform, can we make a calculation of how well the fuzzy logic algorithm is playing the game and calculate its score in a similar way and display it on the front end, on the right side. So we turn it into a competition. That's a creative idea. Turning it into a competition between the player and the fuzzy logic system adds an engaging twist. Here's a suggestion on how to implement it. If the fuzzy logic is a player, treat the fuzzy logic system like a virtual player. It makes decisions based on the waveform data, similar to how a human player would click on waveforms. Score calculation for fuzzy logic player. After each waveform, calculate a score for the fuzzy logic system based on its analysis, the abnormality score. You can use similar scoring logic as for the human player, points for correctly identifying abnormal waveforms, deductions, fairers. Display both scores on front end, on your game interface, display two scoreboards, one for the human player and one for the fuzzy logic system. Position them on different sides of the screen for clarity. Update scores in real time, as the game progresses, update both scores in real time. This keeps the competition dynamic and engaging. End game comparison, at the end of the game session, compare the scores. You can even add a little commentary on the results, like, human intuition wins. Or, fuzzy logic for the win. This approach not only makes the game more interactive, but also provides a fun way to evaluate the effectiveness of your fuzzy logic system in a real world application. Plus, it's a great conversation starter about AI versus human decision making in biomedical applications. Let the games begin. Video game trophy robot VS person. Please do not use emojis. More emojis. Okay, can you look at the code we have so far and make the necessary adjustments? If you prefer, you can generate the prompts for GitHub co-pilot to change the code. Understood about the emojis. To incorporate the competition element between the human player and the fuzzy logic system in your game, here's a general outline of the changes needed in your JavaScript code. Add a variable for fuzzy logic score. In your game underscore logic, JS, introduce a new variable to keep track of the fuzzy logic system score. JavaScript copycode at fuzzy score equals zero. Initialize the fuzzy logic system score. Update fuzzy logic score. After each waveform analysis, update the fuzzy logic score based on the abnormality value received from the back end. You may need to define a scoring logic similar to the human player. Display fuzzy logic score. Modify the front end to display this new score. You can add a new element in your HTML and update it in the game loop, function or wherever you update the display. Integrate with back end. Ensure that the fuzzy logic's analysis results, abnormality score, are correctly used in update fuzzy score, function. This might involve adjusting the Ajax request handling in analyze waveform. End game comparison. At the end of the game or at a certain point, compare the scores of the human player and the fuzzy logic system, and display the results. Since the specific implementation details depend on your existing code structure and scoring logic, you might need to adjust these suggestions to figure setup. If you're using GitHub co-pilot, you can use prompts like. Update the game logic to calculate and display a score for the fuzzy logic system based on its abnormality score. Integrate the fuzzy logic system score updates into the existing game loop, considering the abnormality value from the back end. Remember, testing these changes thoroughly will be key to ensure everything works as expected in your game environment. Help integrating this thing. How do we do this? The idea, just to be clear, is in addition to the current game score, which is the human score, have a score by an AI, the machine learning bid. So it will be the obvious placement is the right side. If the human score is on the left, share the script. We have score. They both start the same. I mean, later we can give a human an advantage of whatever thousand points in this game. Yeah, human has no chance. The speed as well is important. Because the machine obviously can do it. It just is getting the raw data that the human is seeing on the screen. So the score should be calculated in the same way. Obviously, yeah, this is generic stuff. So I added the initialization for the fuzzy logic system score. Can you look at the specific code that we have so far and give more specific responses? I need help integrating the changes you suggested into the code. What shall I get the GitHub compiler to do it? I can certainly help with more specific guidance on integrating the fuzzy logic system scoring into your existing JavaScript code. Since I don't have access to your entire code base, I'll base my suggestions on the snippets and details you've provided. Let's go step by step. Initialize fuzzy logic system score. You've already added let fuzzy score equals zero, which is a great start. Updating fuzzy logic score. After receiving the analysis result from the back end, you should update the fuzzy logic system score. This will likely happen in the function where you handle the response from your analyze underscore waveform endpoint. Let's assume this happens in a function named analyze waveform in your Ajax response handling. Here's a rough sketch of how you might update the fuzzy score. Display fuzzy logic score. You should update the displayed score each time it changes. If you have a game loop that periodically updates the screen, include the score update there. Otherwise, update it right after you modify the score. Call display fuzzy score after updating the fuzzy score. End game comparison. At the end of the game, compare the human player's score with the fuzzy logic system score. You might add a function to handle this. Call compare scores at the appropriate time, such as when the game ends. If you're comfortable with GitHub co-pilot, you can certainly assist in generating more specific code snippets, especially if you provided with contextual comments from your existing code. However, the suggestions above should give you a solid starting point for manual integration. Remember to test thoroughly to ensure everything works as intended. I did upload the code before, I don't know why saying you don't have access to it. Here is the game logic again. Now, shall we plot the fuzzy logic, the machine learning score in exactly the same way as the human one just on the other side of the page? Also, are you going to be using the fuzzy membership function to make a yes-no decision equivalent to the human clicking on the waveform or not? Thanks for sharing your game underscore logic, JS. To integrate the fuzzy logic system scoring and display it alongside the human player's score, you can follow these steps. Fuzzy logic scoring mechanism. You can use the abnormality score from the fuzzy logic system to make decisions equivalent to a human clicking on the waveform. This can be done in the analyze waveform function where you receive the abnormality score. Updating fuzzy logic score. After receiving the abnormality score, update the fuzzy logic score similarly to how you update the human score. You may use a threshold to decide if the fuzzy logic system clicks on a waveform. Example. Display fuzzy logic score. Add a function to draw the fuzzy logic score on the canvas. You can place it on the opposite side from the human score for clarity. Call the scoring and display functions in the then part of your analyze waveform function call update fuzzy score with the necessary parameters. Then ensure draw fuzzy score is called within the game loop function to continuously update the display. Decide on fuzzy logic click action. Use the abnormality score to simulate a click action by the fuzzy logic system. You can adjust the scoring logic based on whether the system should click on the waveform based on the abnormality threshold. By following these steps, you create a direct competition between the human player and the fuzzy logic system, each with their own scoring and display. This setup makes the game more engaging and provides an interesting way to compare human intuition against algorithmic analysis. Where is that thing being drawn? It's a control shift. See? Ah, it's being drawn on top of the canvas. It's actually happening in JavaScript. It's not the HTML bit. So this will be the human score. And on the other side of the screen we should eventually have machine score. I wasn't actually listening. We have to listen to it again, read it again. Fuzzy logic scoring mechanism. You can use the abnormality score from the fuzzy logic system to make decisions equivalent to a human clicking on the waveform. This can be done in the analyze waveform function where you receive the abnormality score. Updating fuzzy logic score. After receiving the abnormality score, update the fuzzy logic score similarly to how you update the human score. You may use a threshold to decide if the fuzzy logic system clicks on a waveform. Where are we doing the actual scoring business? It's different. We want it to be identical to the handle click function that does the scoring business. If there's a limit of the score not to drop below minus 50, then incorrect rejection and misses. Updating fuzzy logic score. After receiving the abnormality score, update the fuzzy logic score similarly to how you update the human score. You may use a threshold to decide if the fuzzy logic system clicks on a waveform. Display fuzzy logic score. Add a function to draw the fuzzy logic score on the canvas. You can place it on the opposite side from the human score for clarity. We'll do the fuzzy logic score separately as in the code, but then it has to make sure that everything is exactly in the same fashion. The points are getting exactly the same. The threshold is between 0 and 1, which is split the threshold into 0.5. Now we have to look at whether we have normal or abnormal. This is the big thing. One big important thing is that... I don't know. I misunderstood. The front end actually generating the waveform so it knows what the result is. So essentially the JavaScript is the judge. Is that correct? Does that make sense? And it's making a decision of how many points to give for what... We need to decide what the threshold is. So essentially in our case, normal ECG is 0.1 and anything else. So anything above 0.1 should be considered abnormal ECG. How do we do that? That will be just making this threshold 0.1. I see if the JavaScript on the front end knows the type of waveform, normal or abnormal, and you want to use the fuzzy logic system's abnormality score to decide if the waveform is considered abnormal, you can set a threshold based on your criteria. In your case, you want to consider any abnormality score above 0.1 as abnormal. Here's how you can do that. Set the abnormality threshold, the finer threshold for what is considered an abnormal ECG. Based on your description, anything above 0.1 should be considered abnormal. JavaScript copy-coded const abnormality underscore threshold equals 0.1. Update fuzzy logic scoring logic. In the update fuzzy score function, use this threshold to determine if the fuzzy logic system has correctly identified the waveform type. Call the update function with abnormality score. Make sure you call this function with the abnormality score you get from your back end in the analyzed waveform function. Display the scores, as before, ensure the draw fuzzy score is called within your game loop function to update the fuzzy logic score on the canvas. This setup allows the JavaScript front end to act as a judge using the abnormality score from the fuzzy logic system to determine if a waveform is normal or abnormal and then scoring accordingly. This will create an interesting dynamic where the game scores are based on the algorithm's ability to distinguish between normal and abnormal ECG patterns. We've received the data, we display analysis result, that's the human analysis. We also do the, so this would be display human analysis result. Yeah, I've renamed. Do we have access to this function? Now I would like the same, same function, but for the fuzzy logic algorithm, so for the machine. If you've renamed display analysis result to display human analysis result and now want a similar function for displaying the analysis result of the fuzzy logic algorithm, you can create a new function that mirrors the structure of display human analysis result. This new function, let's call it display fuzzy analysis result, will handle displaying the analysis result specific to the fuzzy logic's performance. Here's an example of how you might structure display fuzzy analysis result. In your analyze waveform function, where you handle the response from the back end, you would call this new function with the required parameters. For example, this function will be responsible for updating and displaying the results of the fuzzy logic algorithm's performance. You may need to adjust the details of what's displayed based on the specifics of your game and what information you find most relevant to show to the player. I don't understand why the display fuzzy analysis result need two inputs. Okay, can we quickly do this? Let's say it's also receiving data. Okay, this is wrong, this is wrong. Go back, go back. Made a mistake. We received data updating the fuzzy score, displaying analysis result. Okay, shouldn't this return something? Yeah, the machine seems to make some mistakes to begin with, because for normal, it's still giving 0.36, but it's doing better later. The fuzzy logic score should be using the abnormality score taken from the fuzzy logic algorithm and displaying a score for the machine. It's taking the abnormality in the waveform, the abnormality parameter presents the abnormality score taken from the fuzzy logic algorithm, not the physiological algorithm. The waveform type parameter indicates whether the waveform is normal or abnormal. The function uses the threshold set to 0.1 to determine whether the waveform is abnormal or not. Okay, this is the same. Okay, how do we display the score on the canvas in the same way as the score for clicks is being displayed? Okay, so the first one is the human score. The second one is the machine score. Okay, now I'm playing against the machine. Obviously it's much faster, already lagging behind. Okay, that's 50-50. Oh, I'm winning, I'm winning. I'm winning, winning the machine. For now, it's abnormal. I'm way ahead, should I 110? This is normal, I do not click on. Okay, I'm winning quite a bit. Assuming I'm a human, I'm doing much better, as opposed to the algorithm. Okay, now it's becoming faster. It's normal, most double the score. The machine is stuck at 100, and I'm almost at 300. Okay, the machine score is going down, 350-70. Like, winning by far. Okay, that's normal, abnormal, abnormal, abnormal. Yeah, winning the robot, abnormal. Okay, now it's becoming harder, because it's super quick. Normal, normal, abnormal, abnormal, abnormal. The machine's stuck at 200. Okay, I don't even have enough. Oops, time to click on it. Oops, I was clicking on normal. I'm still at 800, 820, 830. Oops, missed it. This is crazy fast now. Yeah, I reached at 1,000. Oops, it will be hard for me to maintain 1,000. Oops, clicked on the normal. I didn't do 1300. Now, obviously, one advantage for the machine is that it can go forever. And it's picking up score as well. Oops, and I'm making mistakes, because I'm out of focus already. And I should know this data set because I created it. Oops, just way too fast. I think I actually beat my own score. Oops, clicked on normal. So you can see my breakdown of all the mistakes and things I'm making. A bunch of normals. Okay, I'm getting all tired. My eyes hurt. The bot is picking up score. It's making mistakes as well. So what if the human just can't handle it anymore? There is something actually wrong with the bot. There are no 0.1s. Should be 0.1s for normal. There should be 0.1s for normal waveform. Yeah, no 0.1s, which is odd. Okay, now the bot is winning, but then the bot is getting it incorrect every time. There is a normal. Oh, because the number is not going down as much as it should. So it's okay for it to start again. It will go 30. It's abnormal. It will go 30. There, 40, sorry. Now the question is, for normal ECG is getting 0.3. Pretty sure it was getting 0.1 before. Now the question is, getting better with time. I don't know when 3 is not correct. 0.36. Yeah, I don't know why that's 0.36. Yeah, if I make this value 0.36 instead of 0.1, what's the exact number? 0.6, 0.361 or something. Yeah, there's a magic number. It's abnormal. Can do the same. Yeah, so if I tune this thing, yeah, it won't be making any mistakes anymore. That was a quick tune. And I think the human is tansky. Because once it becomes faster, yeah, the machine is not making mistakes anymore. Just by adjusting the abnormality score threshold, I can try it, but I won't be able to beat it. It's okay, my score is a bit delayed, but it's still the same. Did I really make a mistake? No. Yeah, once I start making mistakes, once these reds go, counters go up, I'm done. Humanity 0. Robots 1. Well, haven't happened yet. But it's fairly obvious. Yeah, because every 50 points it starts going faster for me. The bot making much quicker decisions. And once I make the first mistake, I won't be able to recover. Because the bot is not making any mistakes with the current setup. Let's focus. Because we will need a lot of focus. Okay, now it's becoming significantly faster for me. The bot is getting the same information. It's getting the waveform. Yeah, it's getting the waveform. Yeah, I made one miss. That's making decisions much faster. It can go forever. And it doesn't make any mistakes. That was tricky. It was a bunch of normals. And then the slightly abnormal one. Oops, false alarm. Okay. Okay, now it's just way too quick for me even to click on it. Yeah, the bot is winning. Doesn't make any mistakes. I need to slow down before I can start again. As in before I can even see the waveforms. The bot just getting them in raw form. It's looking at the features. A frequency, positive peaks, negative peaks and amplitude. It's extracting the features by itself. And normalizing all the values between zero and one. Using a fuzzy logic algorithm to generate an abnormality score. And then we just set the threshold to this 0.365. And that's it. Humans go out the door in terms of reviewing ECG. If we still review ECG we'll need a lot of luck. Or at least we won't be able to do it this fast. We can do it slowly. But yeah. So the bot currently has doubled the score already. Because it's not making any false alarms and misses. And I already made quite a lot. Well, yeah. So I can have my food now. Just watch the bot doing its job. Just sit back and relax. I think it's pretty obvious for this round there who won. Well, first because I just gave up. Now it's slow again. So I can start labeling. But the robot doesn't make any mistakes. It kept going so it doesn't get tired. And it's already reached a score of 7000. Which is several folds higher than what I could do. So even if I start labeling again I made all these mistakes. Four false alarms and 332 misses. Whereas the fuzzy score, the machine, the robot only did hits and correct rejections. Let's say, let's start a fresh fuzzy count. Draw waveform and draw score. So this bit, let's make it 40. They will overlap. And that one, let's make it say 100,000 to save. It's outside the canvas. Should we make it easier for the human to make the canvas larger? Yeah, so I'm not sure how it works on different screens. Make it 500 just so it fits the screen at the moment. Then this hit should be the same blue. Build text, hit, correct rejection, false alarm misses. Can the bot do this kind of stuff for me? Or should I do it manually? 80, 120, 200, 160. Notice numbers should be 500. And yes, we could. Yeah, I have no idea how it will be displayed on a mobile actually. So that's how it will look like on my screen. Can make the font a bit smaller. Do we have any font settings? Yes, we do. Can keep this one bold. Change 500 to 450. It would be better. It doesn't look too bad on mobile, at least on my mobile. Just those screen sizes for the phone I have. Right on the speed. So yeah, it makes sense for it to only go high. To go faster when the human is doing better. I mean, for the策 logic algorithm, it doesn't make any difference. It doesn't make any mistakes with the current setup. So it might as well be publishing it hopefully today or this week. Generally we don't like magic numbers. So ideally this would be automated. Yeah, so essentially this could show you how machines are better for specific tasks at annotating time, serious waveforms. If you are a human and you are engaged at the task, it will then become faster and faster. The bot will just keep doing its thing. And it's making the decision essentially immediately. So it doesn't mind how fast or quick the scrolling is on the screen. It gets the same waveform as you are. So it doesn't know that the algorithm, this score here, the abnormality, this prediction is being made by the backend that doesn't have access to the waveform generation business. And yes, we can make it harder for the bot by introducing more types of waveforms. So if you have any suggestions as to what did I forget, what type of abnormal ECG I should include, do let me know. Definitely the machine is winning by miles to human assuming I'm human. You should not make this assumption, because I can talk like this as well. And let's go back a couple prompts. Okay, so it's working. Okay, now I still get this for a normal ECG, a normal activity from your system. That's happened. Raw positive peaks, 3, raw negative peaks, 1, these are typical characteristics of an ECG waveform. Raw amplitude, 349, normalized amplitude, approximately 0.35, the normalization appears to be functioning correctly. Abnormality, approximately 0.36, this is slightly above your threshold of 0.1 for normality. The abnormality score of approximately 0.36 for a normal ECG waveform suggests that the fuzzy logic might be overestimating the likelihood of abnormalities. This could be due to... Okay, so the rule might be too sensitive or not accurately reflecting the nuances of a normal ECG. We did go over the rules several times. I thought they were working well. Yes, we need to find a threshold for classifying normal versus abnormal ECG. Now we actually manually look at the number and we set the threshold as a magic number in the code. But we don't want to do this. We actually want to use the... I want to change this code slightly for the backend to actually return... Let's pop in the code again. This is the updated game logic and I'll be sharing the Flask Python script in the next prompt. Great. You've updated the game logic to include scoring for both the human player and the fuzzy logic system. The setup for handling scores, hits, misses, and other counters for both players looks well structured. You've also included a threshold for the fuzzy logic system to determine abnormal waveforms, which is key for making the comparison fair and interesting. As you prepare to share the Flask Python script, ensure that it's effectively communicating with this JavaScript logic, particularly in sending the abnormality score back to front-end. This integration is crucial for the competition aspect of your game to function correctly. Once you share the Flask script, I'll review it to ensure it aligns well with your game logic and provides the necessary data for scoring. Let's see the backend part next. Yeah, if you can review, that will be great. Additionally, how about... What do you think about actually using the... I'll forget what they called. The membership functions from the backend from Python code to actually return a decision, yes, no, normal, abnormal. In addition to the abnormality score, would it make more sense to do it in the backend instead of the front-end? Reviewing your Flask application, it seems well structured to handle ECG waveform data analysis using fuzzy logic. The use of global min and max variables for normalization, application of FFT for frequency analysis, peak detection, amplitude calculation are all in place and should work effectively for your purpose. Regarding the usage of fuzzy logic's output for making decisions. Returning decision from backend, it's a good idea to leverage the fuzzy logic system in the backend to make, normal, or, abnormal, decision. This can be more efficient and robust, as the decision-making logic is centralized in one place, the backend, and it reduces the complexity on the front-end. Implementing decision logic in backend, to make a decision in the backend, to make a decision within the fuzzy logic system and return a symbol, normal, or, abnormal, label along with the abnormality score. This can be done by setting a threshold within your fuzzy underscore logic underscore analysis function. Example. Using the decision on front-end, on the front-end, you will then use this decision, value directly to update the scores and gameplay, instead of applying the threshold logic in JavaScript. This approach has the advantage of keeping your decision logic consistent and centralized, making it easier to maintain and update. It also ensures that the decision-making process benefits from all the data processing and analysis capabilities of your backend. Question is, what happens if I pop this into GitHub Copa as well? Is to do with Python, just pop the whole code into it, back-end, the threshold of 0.5. Why not use the membership functions? Why not use the abnormality membership function? Isn't that what it's there for? For? You're absolutely right. The abnormality membership function in your fuzzy logic system is indeed designed for this purpose. Using it can provide a more nuanced decision-making process compared to a simple threshold. The abnormality membership function categorizes the abnormality score into different levels, like, unlikely, possible, and likely. You can use these categories to make a more informed decision about whether a waveform is normal or abnormal. Here, is how you can modify your fuzzy underscore logic underscore analysis function to incorporate this. Utilize abnormality membership function for decision. After computing the fuzzy logic result, you can check which abnormality category has the highest degree of membership and use that to determine your final decision. Example modification in your fuzzy underscore logic underscore analysis function. Python copycode def fuzzy underscore logic underscore analysis. I don't know why it stops reading, but we have another odd reading. They come in odd reading text. Yes, we have the abnormality membership function. Yeah, there is the membership functions are unlikely, possible, likely. Then all we need to decide for the yes and no is what do we do with the possible and unlikely is essentially 0 to 0.3 the possible is 0.2 to 0.8 which is a problem because currently for the normal thing we still get 0.36. So ideally the algorithm, the bot shouldn't be dealing with magic numbers. It should be dealing with concepts. And if you have three membership functions, you might as well split them third. It's 0 to 1 3 ways. Yeah, the human score, if it goes minus 50 then it stops displaying the negative value. It's kind of well right now it's recovered from it. So that's how well I'm doing at scoring CG waveforms as opposed to a robot. Yes, we need to check how generalizable the robot's algorithm is. So essentially if we add more different types of waveforms, we want to see what happens to this performance. That would be a next stage. Currently we're just trying to remove any unnecessary magic numbers from the code. This 0.365 is currently 0.365 is the perfect threshold. If we make it 0. 4 something, 0.37 we'll start making mistakes. Let it run for a while. Because I'm getting 0.364 normal. But I think one of the abnormals has a very similar number. Yeah, I think it's the one that's missing, that negative peak. That one is getting 0.5. Right, this one getting 0.46. 0.37 Yeah, so if we go 0.377 we'll start making mistakes. But it will always do the one waveform type incorrectly. Yeah, we expect for one of the waveforms to for it to do a miss. And I already did two miss as well, because I wasn't paying attention. So what will always miss that one waveform type. So yes, the idea was the fuzzy rules and membership functions should be just designed based on textbook knowledge. Not actually looking at any waveforms. That's in the backend. Okay, there's no questions in the chat. Let's check all the chats quickly. They might finish up and I'll see you next time. Bye.