 Okay, so we have this fuzzy logic web application, it's playing this game for us, it's an ECG detection game. We have this project structure, it will be a flash application eventually. So this is the label that suggests we're doing a waveform feature detection using AI, AML. So this ECG signal has abnormalities. We could add noise to it as well. We have the waveform JS. We're trying to do everything from like math level from scratch for the waveform generation is basic math JavaScript functions. Currently we're trying to add a noise to it. So we essentially can monitor how well the fuzzy logic analysis algorithm is doing. Currently I just changed the threshold a bit. Reshould should be 3.0 something. No, 1.3, 0.3 something. Okay, it was actually in the game logic. That's why I can't find it. Right, the threshold is 0.377. So we also don't like magic numbers. Trying to get rid of any magic numbers. So with 0.365, this current algorithm is giving a perfect result. But we do not like perfect results. We like for it to do mistakes. Yeah, it's always missing. Currently, if I change, make this number a bit higher. This is the threshold for the abnormality score that the fuzzy logic algorithm spits out. So fuzzy logic algorithm looking at the raw frequency, scaling it, positive peaks. I don't know why it's raw. It should be raw. It's just positive peaks. Because originally we, yeah, for whatever reason, we were scaling them as well, but we don't need to do that. It's just the number of the positive and negative peaks. Just check. Yeah, the raw frequency is just frequency. Okay, that makes sense because then you have raw and scale. That's fine. So we have four files. We have HTML to JavaScript, the waveform JavaScript that's actually generating the waveforms that came logic. So before anyone is complaining about not being able to win the robot in detecting labeling abnormal ECG, just keep in mind that the robot is not cheating. It is just seeing the raw data. So just seeing the waveform, nothing else. It's making this decision. It's currently set on purpose to always make a mistake to miss, well, because of that threshold. Yeah, made a miss. We can quickly fix that by adjusting this threshold back to 0.365, then it will never make mistakes again. But we actually want to get rid of it. Is the abnormality membership function being used? Can we make sure we use the abnormality membership function in the back end and in the back end make a decision as to yes, no, if the signal is abnormal or not? I think we're really doing some of it, but it's not being implemented correctly throughout the code. As in we may changes to the Python code, but from memory still need to change the JavaScript. We have three levels to it, but obviously for a yes, no decision. So it's essentially yes, no, maybe. Well, but the membership function abnormality, it's mentioned 38 times. That's the main thing. Yes, we have abnormality. It's very convoluted language. It's like a double negative or something. As we're saying, it should be normal, not like normality. And if it's, if it's likely, then it's normal. Then it's, if it's unlikely, it's abnormal. And in this case, we have the measure for abnormality. So if it's likely or possible, essentially suggest that the signal, the ECG waveform is not normal. And those are the thresholds, except they would not work for the current code. Because the lowest it goes to is that magic number of 0.365. That's not cool, is it? So do you think the port can solve this for us? Yes, so we don't want to do it at the front end. We want the backend to return the abnormality score, but also essentially return the decision, make a decision, normal or abnormal. And then we calculate the score. Currently, we use the magic number threshold, which is okay, but not ideal. Especially if we're trying to use fuzzy logic correctly, we should be, everything should be in the form of this membership functions. You can go look them up. There should be a lot of content on explaining fuzzy logic, the basics of it. So to fully integrate this new system, you will want to make a decision in backend. That's what we are doing already. So in the three items that you suggested, a couple of things. Yes, we do want to make the decision in the backend, but we don't want to use a threshold, we want to use the membership function. Now the second item, yes, we want to modify, we want to send the decision as well as the abnormality score to the front end. And we want the decision, the yes, no, normal slash abnormal to be used to calculate the score. Okay, so you have all the code that we have so far. Are you going to modify the script or generate prompts for GitHub co-pilot to use? Another problem is the current membership functions for abnormality has a 0.3 threshold for abnormality to be unlikely. The fuzzy logical rhythm returns 0.31 for normal ECG waveform. And let's double check that. So it returns, okay, 0.56 what? No, it's doing 0.56 all the time. So 0.9 for this one. So it's highly abnormal. Yeah, there's something wrong. Right, 0.37 and 0.36. Yeah, 0.36. Yeah, 0.37 for this like essentially slightly abnormal. 0.37 for this one, that's okay. And 0.36049A4 normal, a waveform, fuzzy logic rhythm, no fuzzy logic algorithm. That's what I'm struggling with the transcription thing. I can never get what I say correctly unless I speak like this, but slowly, but even then a certain terminology, it gets incorrect. As late as those live streams get transcribed by YouTube using the Google API, I would imagine. I have to go and manually correct the text, which is not fun. And the decision is normal if highest membership equal unlikely else abnormal. Okay, that's fine. Okay, this is so we displaying this season on the front panel, normal or abnormal. Now, in JavaScript, we haven't gotten read off this line. And in Python code, we need to adjust the membership function so that the magic number is within the normal range. Okay, let's actually try to make something ourselves without the pot. No, we will use the pot. We want to generate this function and it should be regenerate. Okay, it's actually getting it. Let's see. And now we'll get up co-op artists and no good. What if we select the whole code when they had 201 lines of code regenerate the update fuzz score? Nope. Yeah. Didn't have the context was it for now? This does sound correct. Instead of relying on that function, we want to rely on normal abnormal. Just comment out for a sec a copied again. Now we also write in your Python app.py file. You need to treat the abnormality membership functions so that a score like 0.36043 falls under the normal category. Here's an example of how you could adjust the membership functions. Yeah, there's a problem with that is that the first abnormal, like the lowest abnormality score for abnormal is very close. The lowest score for an abnormal ECG waveform is just above this number. Which is a problem because 0.5 will be too high. So 0.5 is way too high. This might be better. There will be overlapping like the, yeah, because it's a triangular shape. We're not plotting it in one, but probably should that will require more work. Don't want it. We might later do like a general. So we'll have a new tool. It will be the ECG bot, the Kaliquest bot. I'll give it another name. If you have any better name suggestions, please do let me know. And then later we might do a more simple fuzzy logic tool. Scores up to 0.36 are considered unlikely normal. Scores just above 0.36 start entering possible category and scores from around 0.44 onwards are firmly in the likely abnormal category. This adjustment should align the fuzzy logic analysis more closely with your expectations for classifying normal and abnormal ECG waveforms. Remember, these values are just a starting point. You may need to fine tune them further based on your test results and specific requirements. Yeah, so in the beginning it's still adjusting its thresholds. So it's making mistakes. So it's going to, yeah, minus 60 score. And the human one said not to go below minus 50 might change it. It can go negative is fine. I think how the fuzzy logic score is calculated. Yeah, we need to adjust this score. So normal is still generating 0.4 now. So why? It's like getting better over time. There's some thresholds that need adjustment that are adjusting as, like as we go. Okay, so need to adjust how the score is being calculated. Score go from HTML quickly. I know it's actually in, yeah, you can select it. It's not actually text. It's being overlaid there. It's called fuzzy score. It gets based on the decision and waveform type. There's something wrong here. I expect this should be reversed. It seemed to be the fuzzy score. It's going to be going in reverse. Can you double check the code you generated? The decision abnormal and waveform type. So 0 is normal. 1 is abnormal. Yes. Okay, why the score is not going anywhere now? Score is not going anywhere. There's some silly mistake. Okay, this should stick. Okay, why? Doesn't it? My GitHub co-pilot is not working. It's not completing the code. If taking... Oops. That's why GitHub co-pilot is funny like that. Taking the decision. Yes, that was wrong. It's false alarm. It's abnormal. It's essentially saying yes to anything. Clicking on anything. Better over time. So for normal ECG, currently it gives a 0.4. What's actually later goes down to 0.36. Continuously chatting to oneself or to an i-model like GitHub co-pilot is a bit unconventional. I understand the context here is to brainstorm or debug an issue with the code. Regarding your problem where a normal ECG waveform is generating different abnormalities scores each time, there are a few areas we can look into. If the input ECG waveform data varies even slightly each time, it could lead to different fuzzy logic analysis results. Ensure that the ECG data being passed for analysis is consistent for what you define as a normal waveform. The use of global min and max values for frequency, peaks, and amplitude might introduce variability. These values are used to normalize the input data, and if they change due to new max or min values being encountered in the data, this can affect the scaled values fed into the fuzzy logic system leading to different abnormality scores. To address this, you might consider using fixed thresholds for normalization, based on the expected range of values in a standard ECG, rather than dynamically updating them. The way the membership functions are defined can significantly impact the classification. Given your example data, the scale frequency of 0.6 is classified as normal in your system, but the combination of other parameters, positive peaks, negative peaks, normalized amplitude, might be pushing the final abnormality score into the abnormal range. It might be beneficial to review and possibly adjust the membership functions or the rules in your fuzzy logic system to better align with what you expect for a normal ECG waveform. Ensure that the fuzzy logic system is stable and reduces consistent outputs for the same inputs. This can be tested by feeding the same data multiple times and checking if the output abnormality score remains consistent. Rigorous testing with a variety of ECG waveform data, both normal and abnormal, is crucial. Log the inputs and outputs to understand how the system is behaving and where it might be deviating from expected results. One approach could be to recalibrate your normalization process and review your fuzzy logic rules. Here's a pseudo code outline to guide this process. This approach focuses on reducing variability introduced by dynamic thresholds and aligning the fuzzy logic system more closely with clinical standards. Remember, fine-tuning a system like this can be iterative and may require several adjustments based on testing results.