 I don't know if you know much about Fuzzy Logic, most people know about neural networks. Fuzzy Logic could be better in the sense that it's more explainable. You actually can see what is it doing, how it's making decisions. The neural networks, well, depends on the complexity of it, not so much explainable. So we're currently trying to get the Fuzzy Logic example up and running again, but thinking how to do it. Originally it was a simple example of various simple shapes detector. Those were time series waveforms, but then we thought we might as well do it a little bit more useful, so we tried applying it to synthetic EEG that has a seizure in it. We do have some real EEG data that we are working with, so we could apply it to that in maybe the later stage. Synthetic signals are always good because you know exactly what's going on with them. Because we're working with ECG data as well, and one of the tools that we have on the side that was quite popular is this ECG game. We will use that one to develop a fuzzy logic neural network location that will essentially play the game and do detections for you or against you. So it would be interesting so you could see how well you're performing against the bot. So we're doing waveforms generally. We're doing waveform detection using AI, looking at the signal of normalities and noise. We're using fuzzy logic and or neural networks, obviously interested in data visualization. Currently for this application we are using Flask, NumPy, SciPy and SKFuzzy, which is the fuzzy logic toolkit for Python. Probably know about our ones already. If not, there's a lot of other content on YouTube that will cover those. We would like to continue development and give a quick summary of the changes to the code that we made. We've been focusing on developing a system for ECG waveform analysis, particularly detecting abnormalities and handling signal noise. Fuzzy logic, neural nets, we discussed the implementation of fuzzy logic in your system. This involved debugging and refining the fuzzy underscore logic underscore analysis function in your Flask application to correctly process ECG data and calculate fuzzy logic outputs. Data visualization, while we haven't directly worked on visualization in this conversation, your project involves visualizing ECG waveforms and the results of the analysis, likely using tools like Flask and JavaScript for web-based interaction. Flask, NumPy, SciPy, SKFuzzy. Flask, we focused on the Flask backend, particularly on routes handling ECG data processing and analysis. NumPy and SciPy discuss their usage for processing ECG data, including Fourier transforms for frequency analysis and peak detection. SKFuzzy, addressed issues in implementing fuzzy logic using scikit fuzzy, particularly in handling membership value calculations. GPT4 and GitHub Co-Bilot, briefly compare their uses in coding. GPT4, ChadGPT, is ideal for explanations and understanding concepts, while GitHub Co-Bilot excels in real-time coding assistance within an IED. Key code changes made. Frequency calculation, suggested modifications to the frequency analysis part of your code to correctly identify the dominant frequency from ECG data. Peak count logic, advised on adjusting the peak detection logic to align with your project specific requirements, e.g., counting positive and negative peaks. Troubleshooting fuzzy logic, offered solutions to fix issues in your fuzzy logic analysis, including correctly extracting and serializing membership values and handling cases where fuzzy rules might not be triggered. Yes, we actually have an implement that changes for the problem that frequency is always showing zero at the moment. Sampling rate is a good question. Can you look at the waveforms JS and tell me what the sampling rate is? I'm pretty sure I'm already doing FFT frequency analysis. So what are the changes I need to make that it's to make sure it's not showing zero all the time? Now regards the normalizing the peak counts. So it sounds good that we have them split into positive and negative peaks. Code change, Python copycoder sampling underscore rate equals. Hashtag find this based on your data's time intervals. Frequencies equals NP.FFT, FFT for Q, LEN, NP underscore data, D equals 1, sampling underscore rate. Dominant underscore frequency equals abs, frequencies, NP.abs, NP.FFT.FFT, NP underscore data, argmax. Separate positive and negative peaks, use skippy, single, find underscore peaks for positive peaks and the same function on the inverted data, NP underscore data for negative peaks. Direct use, if specific numbers of peaks are expected, e.g. three positive, one negative, compare directly with these expected values instead of normalizing. Yes, it seems like you're detecting both positive and negative peaks. That's correct. You're not using those values. Now I don't want to add them together. I don't want to add them together. I want to use them separately because they are important. Okay, for the number of positive and negative peaks, considering normal e.c.g waveform, waveform, how many of them there should be in a normal e.c.g waveform, a single q r s b q r s d complex. In a normal e.c.g waveform, which typically represents one heartbeat, the key components are the p-wave, q r s complex, and t-wave. Here's a breakdown of the peaks. p-wave represents atrial depolarization. It's a small positive peak. q r s complex reflects ventricular depolarization. It usually consists of a small negative deflection, q, a large positive deflection, r, and then another negative deflection, s. t-wave indicates ventricular repolarization. It's a positive peak. So, in a typical normal e.c.g waveform for one cardiac cycle, a single p-q r s t complex, you should expect positive peaks, two p-wave and t-wave. Negative peaks, two q and s waves in the q r s complex. The r-wave is a larger positive peak within the q r s complex. Keep in mind the variations can exist in different individuals and under different physiological conditions, but the above describes a standard normal e.c.g waveform. Well, right, so how would you suggest changing that picture processing by the fuzzy logic detector? The detector meant to give higher probability for a abnormal e.g. So it has abnormality unlikely, possible, or likely options. To adjust the fuzzy logic detector in your system to give a higher probability of detecting abnormal e.c.g.s, you should focus on tweaking the fuzzy rules based on the e.c.g. features you're analyzing, namely frequency, amplitude, and peaks. Since we've identified the standard peak pattern in a normal e.c.g., two positive and two negative peaks, you can use this information to refine your rules. Here's a suggested approach. Define new antecedent for peak pattern. Create a new antecedent in your fuzzy logic system that represents the deviation from the normal peak pattern. This antecedent can have a numerical value based on how much the detected peak pattern deviates from the normal, two positive, two negative. Adjust existing rules, modify your existing fuzzy rules to incorporate this new antecedent. For example, you can create a rule that states if the peak pattern deviates significantly from the normal, then the likelihood of abnormality is high. Fine-tune membership functions. Adjust the membership functions for the new antecedent to accurately reflect the degree of deviation from a normal e.c.g. pattern. Likely abnormal. If the peak pattern deviation is high, or if there are significant anomalies in frequency or amplitude, then abnormality is likely. Possibly abnormal. If there are moderate deviations in peak pattern, frequency, or amplitude, then abnormality is possible. Unlikely abnormal. If all parameters are close to normal, then abnormality is unlikely. Implement and test. After updating the rules and membership functions, implement these changes in your system and test with various e.c.g. data to see how effectively it identifies abnormalities. By making these adjustments, your fuzzy logic system should be better equipped to identify abnormal e.c.g. waveforms, especially those that deviate from the typical peak pattern. Remember, the effectiveness of this approach largely depends on the accuracy of your peak detection and the precision of your fuzzy membership functions and rules. Okay, so it's like giving me prompts for GitHub co-pilot to change the code or something. Or at least that's how we could use it. Current problem. Yeah, we need to change process data to look at both positive and negative peaks, instead of just the overall number of peaks. So let's do that. If we get to change those globals, this will be the same. Then we have the amplitude frequency should be the same, mean peaks, right? So this bit changes. And yes, this normalized values will have to change as well, specifically for the peaks. It's this line. Right, it's not being currently used. Yeah, the whole return will change. Let's do that. So now we don't have any errors that the linter is showing. That's great. You can actually run, look at the run application to show you how it looks like. Yeah, so again, you have those e.c.g. waveforms. Individual PQRST complexes scrolling through the window. And some of them are normal, some are abnormal. And you need to click on the abnormal ones. If I click on normal one, I get a false alarm. If I do not click on abnormal one, I get a miss. This is normal. I click on it so I get a false alarm. Then correct rejection. Yeah, this one I do not click on. Getting our correct rejection. And now a normal. And now a correct rejection. This is abnormal. It's missing one of the peaks and so on so forth. Abnormal as well. And once you get to 50 points, it starts going faster, which by the way, the bot should absolutely not care about. So this is definitely where human will fail. Or start failing as soon as the waveforms are scrolling too fast on the screen. There's a video of me reaching 1,000 points. That's the maximum I can do. Go check it out, see if you can do any better. It's on the website. Now, this is what we're adding in the current change. We have this analysis of a fuzzy logic. Potentially later we'll add another column for neural network analysis. Okay, I just realized, are you sure about the two positive and two negative peaks? In a PQRST complex. I apologize for the confusion earlier. Let me clarify the typical peaks in a standard PQRST complex of an ECG waveform. P wave, one positive peak, atrial depolarization. Q wave, a small negative deflection, not always present. R wave, a large positive peak. S wave, a negative deflection following the R wave. T wave, one positive peak, ventricular repolarization. So, in a typical normal ECG waveform for one cardiac cycle, a single PQRST complex, you should expect. Positive peaks, generally, two positive peaks, P wave and T wave. The R wave in the QRS complex is also positive, but is part of the same complex. Negative peaks, one or two, depending on whether the Q wave is present and visible. The S wave is typically negative. The exact appearance can vary depending on individual heart conditions and the lead configuration of the ECG. However, this is a general guideline for a normal ECG waveform. Okay, so shouldn't there be five of them? So if we have a normal ECG coming through, that's not normal. That's actually a difficult one to detect. Yeah, this one. This is the normal ECG and it has one, two, three, four P, right, Q. This one doesn't have the Q, R, ST. The R is normally negative as well. There should be two negatives and three positives that would be like a textbook. This is not a textbook, a PQRST complex. This image, for example, is missing the negative Q deflection. Ideally, there should be three positives and two negatives. How do we adjust the fuzzy membership functions for this to work? When it's thinking for long like this, it's not a good sign. You still have VX, oops, instead of splitting them up. Just correct. A typical ECG PQRST complex consists of a P wave, QRS complex and T wave. The QRS complex itself typically has a Q wave, negative, an R wave, positive, and an S wave, negative. The P wave and T wave are usually positive. In the image you've shown, the Q wave is not visible, which can sometimes occur in normal ECGs depending on lead placement and individual variation. However, for the sake of setting up fuzzy logic for detecting abnormalities, we can assume a standard ECG should have three positive peaks, B wave, R wave, and T wave. Two negative peaks, Q wave and S wave. To adjust the fuzzy logic to account for the possibility of missing Q waves or other abnormalities, you can set up your membership functions and rules as follows. Membership functions for peaks, you can define membership functions for the count of positive and negative peaks. For example, positive peaks, T, positive peaks, too few, could be zero or one, normal, could be two or three, too many, could be four or more. If the count of positive peaks is too few or too many, then abnormality is likely. If the count of negative peaks is too few or too many, then abnormality is likely. If the count of positive peaks is normal, but the count of negative peaks is too few, then abnormality is possible, accounting for the missing Q wave. If both positive and negative peaks are normal, then abnormality is unlikely. Here's an example of how you might define one of these rules in a fuzzy logic system. Ensure that your membership functions are well calibrated to the data you're analyzing, and the rules reflect the medical knowledge of ECG interpretation. Also, consider individual variability in the context of the measurement when setting up these definitions. Testing with a diverse set of ECG data will be critical to fine-tuning the system's accuracy. Let's still solve this problem technical issue. An analyzed waveform function in a game logic JavaScript. Yeah, we sent a post request, that's correct. 61 peaks. How about all words? There's still 25. Okay. Yeah, can we split it into the negative and positive peaks separately? And can you actually generate the relevant code? I trust you. Yep. Well, not really, but maybe this prompt will help. Certainly, to adjust the fuzzy logic detector to consider positive and negative peaks separately, you need to define membership functions for the count of positive and negative peaks separately. Create rules that consider these counts to assess the likelihood of abnormality. Here's how you can define the fuzzy sets and rules. Right, so breaking the overall number of peaks to positive and negative. Let's close the jet copilot for a sec and comment this out. Comment that out. Give me the numbers. And what's abnormality again? Let's do abnormality quickly. Membership functions for abnormality is the same as before. This code assumes a standard ECG with three positive peaks, including the R wave and one or two negative peaks, Q and S waves. The membership functions and rules should be adjusted based on the actual ECG data you're analyzing. Make sure to test and refine these based on real-world data for the best results. Yeah, we have synthetic, but it's okay. We are using synthetic ECGs for now. Testing the real ECG data will be another flat application and another tool. What else? We're still implementing this. Let's go over it again. Have positive peaks, negative peaks. We got rid of the overall number of peaks. We actually need to use those negative and positive peaks to view too many normal sounds. Check the actual numbers in a sec. We have the abnormality likelihood, so unlikely, possible, likely. Those numbers remain the same. Yeah, they might need adjustment later. Now we have these rules. Yeah, we need to get our rules correctly. So we have rules based on positive and negative peaks, too many to view. We don't need this one anymore. It should be rule 5. It's looking at the frequency amplitude and more rules. That's me that can suggest rule 7. Let's take it. Let's suggest more if I just hit dance, medium, medium. It's not a great rule, but we'll keep going now. So we have tools so far. There's some problems with the code. We have abnormality control, create control system with these rules. Just go, remove this one for a sec and just have that comment as a prompt. Right, it's doing rule 127. It's calling a different variable, which is fine. And then control system simulation abnormality analysis. It's calling it the same name, but it looks legit. And we have all the 127 rules. 124 being rules related to the number of positive and negative peaks. And rules 5, 6, 7 are related to frequency and amplitude. Obviously, this is AND and OR. And we have, I don't have this bit, do we? Immultian analysis, compute, input process data into the control system. It's about that. Compute is there. Okay, let's print this. Yeah, I'm not sure about that. Check it out for a sec. Pop it in here. This thing is actually called normality analysis. To use the system, you will input the number of positive and negative peaks detected in your ECG signal. Here's how to simulate with example inputs. Don't need it for now. We have peaks membership. Still a problem. Check copilot for a sec. Peaks are not defined. Fixes should define peaks. Do we need it? We did split peaks into two separate variables. The number of positive peaks and the number of negative peaks. So we don't need peaks where variable anymore in theory. Here is an updated app by script. Can you detect any problems with it? The whole code removed at last prompt. The provided app.py script appears to be set up for processing ECG waveform data and analyzing it using fuzzy logic. Here are some observations and potential areas that may need attention. Frequency analysis. The dominant underscore frequency is being calculated as the index of the maximum value from the FFT result. This might not represent the actual frequency. You should calculate the frequency values and then find the dominant one. Peak detection. Positive and negative peaks are detected correctly. Ensure the detected peaks correspond to the ECG components. P, Q, R, S, T waves. Global min, max variables. You're maintaining global min, max for normalization purposes. Ensure they're updated correctly across requests, as Flask's threaded nature might cause unexpected behavior. Normalization function. The normalization function looks correct, but you must ensure that max underscore val is not equal to min underscore val to prevent division by zero. Fuzzy logic rules. You have defined rules for both positive and negative peak counts, which is good. However, you have a placeholder for peaks in the input, which should be updated to use positive underscore peaks and negative underscore peaks accordingly. The rules are combining the fuzzy sets with or, for, likely, and, and, for, possible, and, unlikely. Ensure these logical operators align with your system's design intentions. Ensure you are using the correct method to extract the fuzzy membership values. The current method using fuzz. Interp underscore membership is appropriate for getting the membership grade for a single value. Before sending the JSON response, you're converting the numpy types to native Python types, which is necessary to avoid serialization issues. Proper error handling is implemented, which is good for debugging and catching unexpected issues. Here's a suggested change for the frequency calculation part. Ensure that you test the application thoroughly after making changes to verify the correct functionality. Also, it would be wise to handle cases where the FFT might return zero or near zero frequencies to ensure the frequency value makes sense in the context of an ECG. Do we have this functional? Let's fix the peaks a bit first. Here we have frequency, amplitude peaks, so then normalize once. Then you should update the peaks membership dictionary. Positive peaks membership and negative peaks membership may have two returns. First one is okay. The second one isn't. Have results, frequency, amplitude. That looks legit. Still have peaks somewhere. You don't have the same error. Converting numpy to integer. And that's working, but not displaying anything. Why? It's a sensitive thingy. To live roof 5, 6, 7, 7 to work. The 5K logic. Just make a quick overview. Next week, we will fix these values, which is the output of the fuzzy logic analysis. We do have the values working okay, but then it's just not being displayed properly on the front panel. Abnormality 0.8. Let's see if it's actually correct or not. So this is abnormal. Yeah, abnormal ECG. Once it's passed, it's abnormality 0.8. This one normal ECG and it says abnormality 0.5. This one here 0.8 again. This one will be harder to detect. It's always showing 0.8 or abnormal. I think it's abnormal again. And this is a normal ECG and showing abnormality rate. That was wrong. It seemed to be delayed by 1. That was abnormal 0.8. And then normal should go to 0.5. Yes. Abnormal 0.8, normal 0.5. So it works. More or less. This normal again will go to 0.5. So that's the probability or not. To check that. Okay, something is wrong. That's normal. It's stuck. And this is how the decision is actually being made. So 4.8. F positive. It's still normalizing the number of positive peaks. It's no good, no good, no good. I'll fix this next time. It should be fairly straightforward fix. The website is available for you to play with. The current ECG game is just over there. You can access it from the front page of the website by anikers.com. And you can play with it manually. And hopefully this tool, which is a robot playing this ECG game, will be available for you to try out sometime soon. So if you have any suggestions, requests screaming at me for doing something wrong, please do let me know. I'll see you next time. Bye.