 It should be live Just check LinkedIn because it's the only one I can't just go live with a press of a button. I have to set it up every time Okay, where's the least of What we are covering today Pop it in here for a sec. Let me spell visualization not just suggesting Interestingly, it's not suggesting American spelling. It's suggesting the English one living as but Yeah, no one cares really. I think let me know if you do Well, actually, yeah, this topic rewind We'll cut it in the editing but the general Overview that I always do Covering by any chaos on this site. Oh, that's quite obvious because that's what the YouTube channel is called But this is the tools available on the side, please go a Check them out They are by any chaos.com. Let me know what you think a Currently we have these tools at the bottom that are Still under development. Well, they actually not under development. They used to work, but they used to be live view Matlab now we don't have access to those the license is too expensive So we read doing it in Python and JavaScript Eventually making a flask application that will that most of these tools are They will be available for you to play with And so I don't know if you know much about fuzzy logic. Most people know about neural networks a fuzzy logic Could be better in the sense that it's more explainable You actually can see What is it doing? How it's how it's making decisions? the neural networks well depends on the complexity of it and Not so much explainable Now so as you know, we mainly dealing with eG and eC g data on this channel It's part of this project and some other a image processing a little bit and other things are welcome to Please let me know in the comments If you wanted to look at any other data the data for this project will have to be publicly available So send me a link to the public repository of any data you wanted to look at now Yeah, so we currently trying to get the fuzzy logic example up and running again Let me thinking how to do it a Origin it was a simple example of various simple shapes detector those war Time series a waveforms But then we thought we might as well do it more Little bit more useful So we tried applying it to synthetic eG That has a seizure in it and We do have some The real eG data that we are working in is who could apply to that and maybe the later stage a Synthetic signals are always good because you know exactly what's Going on with them but and because we're working with eC g data as well and we One of the tools that we have on the side that was quite populous this eC g game We will use that one to a Develop a fuzzy logic and neural network location that will essentially play the game and do detections Or you or against you? So it would be interesting so you could see how well you are performing against the bot So we're already doing this project generally that's the comments where they Know this way it's a mirrored image. So we're doing a waveform So 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 They was the interest in data visualization Currently for this application We currently for this application. We are using flask a NumPy SciPy and SK fuzzy, which is the phisologic toolkit for Python Probably know about the other ones already If no, if not, there's a lot of other content on YouTube that will cover those Shake if there any Questions quickly before Before we move on we are streaming everywhere pretty much on Twitter. There's no questions LinkedIn And YouTube No questions. Let's move on just No one wants to talk by the way, there is a there is an option of you coming live on on this Stream if you like can set up a zoom a Room that you can join in and potentially instead of this text you will appear your face will appear And we can just have a chat But if no human is up for chat We will use we'll talk to the board Unfortunately, it is second best, but That's what we have Well now guess if we are live Streaming right now This are the topics That we cover in this stream We would like to continue Development that we did last time Can you Mention how many prompts in the current conversation And give a quick summary of the changes to the code that we made Yep Wanted to respond Quickly and sure in short absolutely. Let's summarize the progress and topics covered in this conversation so far Total prompts in conversation. We've interacted through a series of 16 prompts including this one Topics and development overview so waveform detection using I 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 skippy skate fuzzy Flask we focused on the flask back end particularly on routes handling ECG data processing and analysis Numpy and skippy discuss their usage for processing ECG data including Fourier transforms for frequency analysis and peak detection Skate fuzzy addressed issues in implementing fuzzy logic using psyched fuzzy particularly in handling membership value calculations GPT for and GitHub co-pilot briefly compare their uses in coding GPT for chat GPT is ideal for explanations and understanding concepts while GitHub co-pilot 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 Feel free to ask if you need more specific details or further assistance as you continue with your development during the live stream 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 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 That code the microphone failing on us because we using microphone Sure, let's keep it brief and to the point Sampling rate in waveforms JS to determine the sampling rate from your waveforms JS I need to see how the data points are generated over time If you can show the part of the code where the time intervals are set or how the waveforms are updated I can help deduce the sampling rate a just frequency calculation implement the correct Fourier transform method considering your sampling rate Use NP dot FFT FFT for Q to get the correct frequency values Code change Python copycoder sampling underscore rate equals Has to find this based on your data's time intervals Frequencies equals NP dot FFT FFT for Q LEN NP underscore data D equals 1 sampling underscore 8 Dominant underscore frequency equals abs frequencies NP dot abs NP dot FFT FFT NP underscore data argmax Separate positive and negative peaks use skippy signal 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. 3 positive 1 negative compare directly with these expected values instead of normalizing Let me know if you need more details or further clarifications Okay on the peak detection Okay, first of all, do you have access to the previous code? That I already shared I already shared the waveforms. J. S. Viviou viv you can you also mention This is low priority, but how does the limit of 40 messages per three hours work? How can I tell what's my car and? a message count a Pay time window. Yes, I do have access to the previous code you shared including the waveforms J. S. Script Let's dive into that to deduce the sampling rate Regarding the message limit as a language model. I don't have the capability to track or display the current count of messages or time windows Typically uses manage this by keeping track of their usage If you're concerned about reaching the limit consider spacing out your queries or consolidating multiple questions into a single message where possible Now let's analyze the waveforms J. S. Script to determine the sampling rate Could you please provide the specific section of the waveforms J. S. That includes the waveform generation or time interval setup? This information is crucial for accurately deducing the sampling rate. Okay It's not very useful try get up go pilot and Then we also, you know Reduce the load on the GPT-4 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 a Normal ECG waveform waveform a how many of them they should be In a normal ECG waveform a single qrs bqrst Complex in a normal ECG waveform which typically represents one heartbeat the key components of the p-wave qrs complex and t-wave Here's a breakdown of the peaks P wave represents atrial depolarization It's a small positive peak qrs 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 depolarization It's a positive peak So in a typical normal ECG waveform for one cardiac cycle a single pqrs t complex you should expect positive peaks to p-wave and t-wave Negative peaks to q and S waves in the qrs complex The R wave is a larger positive peak within the qrs complex Keep in mind the variations can exist in different individuals and under different physiological conditions But the above describes the standard normal ECG 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 EEG 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 ECGs You should focus on tweaking the fuzzy rules based on the ECG features you're analyzing namely frequency amplitude and peaks Since we've identified the standard peak pattern in a normal ECG 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 than 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 ECG 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 ECG data to see how effectively it identifies abnormalities By making these adjustments your fuzzy logic system should be better equipped to identify abnormal ECG 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. 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 got to change those globals don't need to change this will be the same then we have The amplitude frequency should be the same mean peaks, right? this bit changes and Yes, it's normalized a values will have to change as well specifically for the peaks and this line Right, it's not being currently used and we get all return will change Just do that So now we don't have any errors that the linter Is showing that's great can actually run look at the run application Just to show you how it looks like Yeah, so again, you have those ECG waveforms individual PQ RST complexes scrolling through the window and some of them are normal some 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 Fossilum Then correct rejection Yeah, this one I do not click on getting our correct rejection and have a normal and our 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 The waveforms are scrolling too fast on the screen. There's a video of me reaching 1000 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 adding in the current change We have this analysis of a fuzzy logic that should later will add an hour column for neural network analysis To just the fuzzy logic detector in your system to give a higher probability of detecting abnormal. ECGs Basically Features you're analyzing namely frequency amplitude and peaks Since we've identified the standard peak pattern in a normal ECG to piles To just the fuzzy logic detector in your system to give a higher probability of detecting abnormal ECGs You should focus on tweaking the fuzzy rules based on the ECG features you're analyzing namely frequency amplitude and peaks Since we've identified the standard peak pattern in a normal ECG, 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 ECG pattern. Update fuzzy rules for abnormality. 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 ECG data to see how effectively it identifies abnormalities. By making these adjustments, your fuzzy logic system should be better equipped to identify abnormal ECG 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. Yeah, just realized, are you sure about the two positive and two negative peaks in a PQRST complex? Yep. 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 a 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. PQRST complex. This image, for example, is missing the negative Q deflection. So, ideally, there should be three positives and two negatives. How do we adjust the fuzzy membership functions for this to work? Yeah, when it's thinking for long like this, it's not a good sign. We still have PX, oops, instead of splitting them up. 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, P 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 are analysing 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. Right, we'll do this in a second to still solve this problem technical issue. An analysed waveform function in a game logic JavaScript. Yeah, we sent a post request, that's correct. 61 peaks. How about all words are still 25? Okay, 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. Breaking the overall number of peaks to positive and negative. Let's close the chat co-pilot for a sec and comment this out. Comment that out. Give me the numbers. And what's abnormality again? Still abnormality with me. 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 analysing. 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, another tool. What else? We're still implementing this. Let's go over it again. We 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 as needed. It's suggesting rule 7. Let's take it. It suggests more if I just... Yeah, it does. It's not a great rule, but it will keep going, though. Okay, so we have till so far. There's some problem 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. It's doing rule 127. It's calling a different variable, which is fine. And then control system simulation abnormality analysis. Don't buy it. 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. Then we have... I don't have this bit, do we? What's this? Compute input process data into the control system. It's about that. This is there. Okay, let's print this. Yeah, I'm not sure about that line. Shake it out for a sec. Pop it in here. This thing is actually called abnormality 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. Okay, so we don't need it for now. We have peaks membership. Still a problem. Check copilot for a sec. Peaks are not defined. Let's fix this to the defined 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. We have multiple returns. What do you mean if your process data function previously returns a dictionary like this? Why don't you check what was it exactly? Can you check the code? I'm already doing this. Yeah, that's the problem with GitHub. Copilot doesn't understand context very well. At some stage we'll need to pop it into GPT-4A anyway. So we might as well do it now. Here is an updated script. Can you detect any problems with it? The whole code removed that 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 flasks 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 to find 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 function? Let's fix the peaks a bit first. The fuzzy logic analysis function is correctly expecting a single peaks input, but the process does function returning separate counts of positive and negative. That's correct. To modify, accept both positive and negative analysis. We have frequency amplitude peaks, so they're normalized ones. Then you should have the peaks membership dictionary, the positive peaks membership, and the negative peaks membership. We have two returns. The first one is okay and the second one isn't. We have results, frequency, amplitude. That looks legit. The app is not running. We need to run it. Still have peaks somewhere. Oops. At the same error. Air messaging is okay. Peaks is not found actually. Peaks is not present in the dictionary. Process are used instead of peaks. Should update, analyze, waveform. Here, waveform data, process data, converting them by to integer. Adds these two bits. Why is all the same? Here, that's working, but not displaying anything. Why, likely, does not exist for consecutive abnormality. Available, right, is a PSC. All peaks. It's a sensitive thingy. Do with rule 5, 6, 7. It's being saved. Clear. Control of 5. To work okay. To work. If I keep logic, what do I do understand? What if we do workspace? Based on the recent changes in this code, how do we change the JavaScript? Play silly. Yes, we did split the peaks. We're fetching that analyze, waveform. Endpoint your JavaScript code. You will need to update the code. It's just added the console login. Did just add console login, didn't it? That would not really help. We have this industrial loan over outside. So it will be my queue to finish the shop. We'll continue probably next week. See how we go making slow progress. 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. Let's see if it's actually correct or not. So this is abnormal. Yeah, abnormal AG. Once it's passed, it's a normality 0.8. This one normal ACG and it says abnormality 0.5. 0.8 again. This one will be harder to detect. It's always showing 0.8 for abnormal. It's abnormal again. Well, and this is a normal ACG and showing abnormality rate. That was wrong. It seemed to be delayed by one. 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 just stuck. And this is how the decision is actually being made. So 0.4, 0.8. Have positive, but still normalizing the number of positive peaks. It's no good, no good, no good. We'll fix this next time. It should be fairly straightforward fix. Just check if by any chance there are any questions. Doesn't seem like. Let me... The stream will be available both on LinkedIn, YouTube. Not sure how Twitch works. The streams just go away after you finish streaming. But all the videos are on YouTube. You can go check them out. The website is available for you to play with. The current ECG games just over there. You can access it from the front page of the website by AnyCares.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 you know what to do. Like, subscribe. I most care about actual meaningful comments. 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!