 Yes, we were adjusting the membership functions for the phasological algorithm to work better in terms of detecting abnormal ECG waveforms currently doing the Kary quest port and the port is Flask application We obviously have the HTML code We had to javascript javascript stream code. So all the waveforms actually Generate that in the front end because again have the game logic and the main thing that we are currently working on is this flask application that is working in the back and Look currently getting an error the membership functions names Problem with the names. So we modified last time names the names the names Actually The board Why are there only four five rules? I don't know This one actually not looking at the amplitude Was an error thing anyway, I Thought it's working now in a sense that they can come back Next there's something in continuous and chat doesn't seem to be the case Does not seem to be the case and I'll start sharing the code The view I cannot take the whole code at once. I don't think it's a good idea. All right Let's break down your HTML code. Okay. Okay. Okay. Okay. We don't need explanation of the code Don't break it out. I said there will be four files Respondent the end but make sure you read the whole code. So you have access to it Generate waveform function randomly selects and generates either a normal or an abnormal wave form the use of mathematical functions to simulate ECG waveforms is quite clever It adds a realistic touch to your game. Make sure that the waveform generation aligns with real-world I said the quick response is Why aren't you transcribing? Here is the game logic code and after that there will be the Flask python application script in a separate The prompt game variables sets up variables for score game speed and waveform types Drawing functions includes functions to draw the waveform score and clear the camera score management Handles scoring with hits false alarms great rejections and misses Adjusts score based on player actions Click event handling detects player clicks on the canvas and updates the score accordingly game loop Manages the main gameplay loop updating the game state moving the waveform and drawing elements on the canvas Waveform analysis and display sends waveform data to a back end for analysis Presumably a Flask app and displays the results on the web page key points The game dynamically adjusts speed based on the score adding complexity as the player progresses The scoring system is well thought out rewarding correct identification of waveforms and penalizing mistakes Integration with a back end for waveform analysis adds an educational and interactive element to the game Overall it looks like a solid script for a game that combines entertainment with educational aspects of ecg waveform analysis Currently getting an error for this code That we would like to correct. It should be a simple one Here is the output Of the Flask terminal So would you recommend using Would you recommend would you be generating roms? For me to use in github co-pilot or are you going to be generating the code? Yourself and delete the previous rules that didn't work very well Don't quite understand how co-pilot works in terms of providing specific Advice to fix. Okay. Now it seems to be more specific to our needs So we're having problems Frequency a membership Frequency membership low medium high So instead of low medium high we should have Low normal high This should be called normal Can we fix this instead of changing the membership function? But in theory it should change the medium to use normal Maybe we should have low normal and high That's something wrong. There's a comma missing. Right. So it's bad bad Control f5 it Yeah, we have the thing running again Which is great. We need to Check how well it's working. In case another normality is At point one It's normal. So it shows point one That's normal again Normality point five. Okay, that makes sense And that's not good. That's not good as well And I wonder how much Corp pilot knows I think the problem might be with positive px normal. Okay, can we change this Be it so that normal is three But anything below three or above three is Too few or too many Now any values above three should be Too many And any values below three considered too few So just stick to three three and then This goes to three four five Okay, let's check it out But we have a normal I like the point one better The multi point nine a Okay, this is not good Can I have it three three three? No Okay, this will Look super weird Okay, we can go half So we have zero one two point five Get the 24 to double check 3.5 because I think there's a problem. They meant to be Overlapped say somehow if this is 2.5. This should be Whatever two But then Try this out Okay, a compound doesn't say there's any problem with this Hey, how changing To changing the membership functions for the positive underscore peaks fuzzy variable as you suggested is a good idea It adjusts the ranges for what constitutes too few normal and too many positive peaks Here's the updated code snippet based on your suggestion This change will effectively shift the definitions for the categories of positive peaks in your fuzzy logic system The too few category now covers a broader range. The normal category is more narrowly defined and too many categories starts at a higher value It's important to ensure that these new ranges align well with the real world data and the behavior you expect in the system Yes, so when we have a few too few So instead too few Used positive peaks too few Not using it. Are we We are This rule doesn't seem to Take effect Check the rules. Yes, if they have positive peaks too few of them or too many Have the month is likely that makes sense, but it doesn't seem to Behave as expected explainable AI It's hard to explain I need to cover All these rules where we can start the commenting them out one by one Is that a good idea? So if we comment to That we just yeah, we just get an error So I have likely likely possible unlikely Unlikely likely likely It's that simple way of Getting rid of the rules I mean I can just do this Comment that out Just do rule one this stuff We only looking at positive peaks in rule one Let's just do this. Yes, that that rule doesn't work. Yeah, we need to check All the rules. So ideally we will have some sort of graphical interface for designing this We do not have one. Yeah, because this becomes rather Complicated Just bring it back to where It works. Yes. I don't like the abnormality of point five. I don't need to go zero to six Hips of peaks Three it's a good one So the abnormalities probability of the signal being abnormal actually wait now it's working better So I still write so I get point one. This one is missing a negative peak. So I getting a point four for abnormality probability and that's same point three When I'm missing the third positive peak Point one for normal. It's actually pretty good Point three when I miss the first positive peak Point five. Yeah for this weird thing the qrs is not quite right That trouble calculating normalized amplitude over there Yeah, that's another weird thing. I'm getting point five So now the numbers kind of go from zero one to point five Which is okay, but they do not cover the entire range. It's a not ideal And that's wrong why the point once was missing a negative peak and and A really important thing that this meant to be A scalable not scalable what's called when they It can be applied onto another data set and will still work. What's the name for it? The point one for normal. It's good. It's a low Might have something that prevents the same signal appearing twice Or at least when we are testing I think there's some sort of in the game logic. That's some sort of probability for I think 40 60 For normal and abnormal But then it could be another rule of not repeating itself. This works works pretty well Can you explain so we'll explain the rules Right the description Yeah, because not just the rules. It's the It's you can you can still play the game, right? Yes So that should be normal. So I should not be clicking on it. I'll be getting why I'm not getting my points Corrects rejections going up. Ah because I had 20 It's what It went like super negative. It's actually not showing the correct score there. They had 29 misses Um, right. So the interesting thing would be you have your score and the robot score And yes, someone will say the robot is cheating but the robot is also just looking at the waveform. It doesn't have anything else Doesn't have any data on how the waveforms were generated And then you have this score A of the robot on the other side. I don't like this point one And let's check the negative Hey, can we quickly check the negative peaks as well the rules for it Can you look up the code and explain what the current rules are and the membership functions? I'll have a look at it myself as well Sure, let's review the negative peaks membership functions and rules from your flask applications fuzzy logic code Too few the fine for values between zero and zero point five peaking at zero point five This represents scenarios where there are very few negative peaks Normal range is between zero point five to two with the peak at one This category is for a normal count of negative peaks Too many covers the range from two to four peaking at three It indicates an abnormally high count of negative peaks rule two If there are too few negative peaks and the positive peaks are not normal It's likely to be an abnormality Rule three if there are too many negative peaks it's possible to be an abnormality Rule four if both positive and negative peaks are normal the abnormality is unlikely These rules integrate the negative peaks count into the fuzzy logic system to assess ecg waveform abnormalities The categories and rules are designed to capture different scenarios that might indicate an abnormal heart rhythm Adjustments to these functions and rules should be made based on how well they align with actual ecg data and the specific requirements of your analysis At this line the codifying fuzzy rule, which is a key component Multi-valued logic Yes actually This case the rule rule two is the fine force if negative peaks Is too few and positive peaks are not normal abnormality Is likely Okay, so another big question that yes, you will need a cardiologist to answer this is Do we have likely possible and unlikely? We can have more A higher range Can Can you draw this can plot those membership functions? a plot on one graph One work wouldn't because we don't have the plot of a view. Okay, actually my work Where would it be displayed? Yes starting Give me a warning The plot is nowhere to be seen It is doing the Plotting, but it won't work any suggestions You can ignore the warning That's nice It's trying to plot the membership functions But it's not Able to do so This might be a problem Yeah, I would like to view this scene On the front panel because this will plot it using mudplotlib You would do have this plot I have trouble with rule three Is there a way to graphically review how the rules are interacting with one another? Here's a sample code. Okay. Okay. This sounds great. I would like to do it But can we plot it on the front end instead of in python? Yeah, so it seems like it generated Will generate something. So we have this game. It's on bionicchaos.com That you can play it looks essentially exactly like this It's that is so I have to The rules of the game Is to not click on ecg electrocardiogram waveform that looks normal and yes click On waveforms that look abnormal We can see it's There's only in this game. There's only like five Of them. So that's normal and once you get to 50 points it starts scrolling faster So the idea is that you have to keep the greens Going up the reds staying low or zero So I'm getting all this normal and it's uh So if I did not click on normal, it's a hit I don't know why there's so many normals keep coming through If this is abnormal if I Click on it. It's also a hit Again, now it's becoming uh Becoming harder That's obviously an abnormal. So I have to click on it. That's normal. So I do not click on it Do not click Do not click That's abnormal because it was missing something. That's obviously that's abnormal That's uh, normal abnormal. Okay, now it will become harder. That's normal It becomes faster As you go up and score. I don't know that why there's so many normals in the row. That was abnormal. It was missing something Uh, that's abnormal. Obviously this looks all skewed up A abnormal abnormal so you can go try this game On the side It's free. No registration is required And it's becomes crazy hard It's a bunch of normals, normals abnormal I think earlier I got to like a thousand points. There's no little. Oh, I missed one. Okay First miss. So it was an abnormal waveform and I did not click on it. Okay, this is crazy fast now Okay, I think you get the point See if I can keep explaining what I'm trying to do now While still still playing uh Okay, so now what we're trying to do so this game is already running with oops means that It's on the website And now what we are doing is training a board to play the game and then It's not it cannot cheat because The board is only operating in the back end Okay, super. It's crazy now. Um, so those are the results of the analysis What is the goal for this feature selection? So features so we're looking currently as multiple features. Oops That's a false alarm. I clicked on the normal Okay, now it's just super crazy. I can't do it anymore Pretty sure there was a video where I reached like thousand points Maybe I'm uh, not focused at the moment. Anyway, the if I stop clicking the score will go down so the features I'm looking at Someone is asking. What's your goals for this? What are the features selection? So the features I'm currently looking is Frequency Role a positive number of peaks negative number of peaks amplitude and That's it at the moment later. We'll be also looking at kurtosis kurtosis and other things as well They're like standard things and ecg Analysis if you look at the literature or ask gpt gpt Can do it now What are the current features I'm looking at and what feature frequency? It doesn't seem to to actually be very useful In my current. Ah, so the main thing is for This algorithm to be Generalizable, right? So if you load different data set, it should still work. So in my case if I keep loading more a waveform examples So the waveform examples are all generated in javascript on the front end All the game logic is in the front end but all the Currently we're using fuzzy logic, which is like explainable ai explainable machine learning and that's all happening in the back end And they so they don't know of one another, right? So the back end only gets the waveform doesn't get anything else It doesn't know how the waveforms were actually generated So it doesn't have those names For example, so it can tell if it's normal or abnormal Or the ratio of them Appearing on the screen is also unknown to the back end flask The application no, sorry this this whole thing is a flask application But the python code this function the fuzzy logic analysis doesn't know that later We'll also planning to add a neural network doing the same task and then you can see Essentially ideally on the screen you will have your score Currently at 640 already had a few bunch of misses and false alarms. So false alarms is obviously when you Click on a normal ECG and misses when you miss an abnormal when you do not click on abnormal one It's a super crazy fast. So I can't do it anymore See if you can reach a higher score Currently the Machine learning algorithm is speeding Speeding out an abnormality score This abnormality score Yeah, github core pilot it's gonna be all plus Mainly relying on gpt4 to do this Okay, this kept the turning my speech into Text Which is fine Okay, kids. So can you review what we did so far quickly? If you want to be funny, you can be funny But definitely no emojis I want to see any emojis In the responses. I am keep it short. See what it says. All right. Let's do a quick recap. No emojis straight to the point Front end you've got a javascript based system generating ECG waveforms with all the game logic handled client side Need for interactivity and user engagement back end flask application. This is where the magic happens Your python code analyzes the waveforms using fuzzy logic It's like a black box. It doesn't know how the waveforms are generated or their labels normal or abnormal from the front end Fuzzy logic. This is your AI's brain making sense of the waveforms It gives you an abnormality score based on the input waveform Machine learning you're planning to introduce a neural network to tackle the same task This will add another layer of analysis to your setup Game mechanics the player clicks on ECG waveforms aiming to identify abnormalities Misses not clicking an abnormal waveform and false alarms clicking a normal waveform affect the score Scalability and generalization your goal is to make the system adaptable to different datasets The back end should reliably analyze any waveform regardless of its origin Future plans visualizing the fuzzy logic analysis on the front end and comparing it with the neural network's output That's going to be an interesting comparison So you're blending gaming education and advanced AI in a really cool way The fuzzy logic provides explainability and adding a neural network will boost the analysis power The key challenge will be ensuring that both systems generalize well across various ECG patterns Keep up the awesome work. Rocketman scientist woman scientist Isn't I safe to not use any emojis? Uh me as I was asking what do you think about training model for voice classification? Right Uh pronunciation wrong pronunciation. I think I was saying it before I think When people learn English From now on they'll probably be optimizing for the Robot to understand them. So they will be talking like this Hopefully when they start pronouncing words in a better fashion The transcript will be better as well. Yeah, I was doing it for my Videos and everything so the live streams on youtube get transcribed then I get that text out Uh, I do still have to correct that so there are obvious things like eg ecg That it doesn't get At least eg entirely Yeah, eg it's always Writing eg so yes, there would be some sort of I wish not wish could be easily done Actually had a tool like that A speech rate and volume analysis. So this one is also on the side. It's uh Open it another link. See if it's still working Uh, some people are complaining. It doesn't work for them. We actually have to read the instructions It's only working in chromium based Uh, only working in chromium based browser browsers And this one will work differently and yes, it will do a volume and speech Analysis as you can see here, but uh, yeah, it might need more more work Some problems if the side I'm pretty sure there will be some errors or whatever The point of this one. It's all javascript Where is it? network Why is it not sure why it's not recording? Well, anyway, it's all javascript And it's currently just looking at the speech rate and volume And yes, hopefully you can train this one to better recognize the individual voices and stuff so that Could be something We'll be adding here later on. It's already working on the website. You can look it up. It's using a common javascript Javascript module. So isn't this one doing any better? I think so Fixer no, I think so. Yeah Yeah, if you talk normally It's getting confused. Yeah, if you talk like to 5 year old Then that may get things correctly. So anyway, this is on the site as well. You can go check it out If there are any ads popping up, I apologize in advance. Hopefully they're actually relevant to to you I don't know how well Google is placing ads these days Sometimes it's better than others Right, so if I don't touch the can don't click on anything the score will Start I think it will start going down because there is a There's a 60 40 a ratio for normal and abnormal For 60 percent abnormal and 40 normal So if you don't click on it that will keep Oops East coast to the relevant ad. I don't know doesn't look like So this video brought to you by no, it's it's totally irrelevant Current as a You can essentially go and like clear those out. Hopefully if you go on the side You'll actually get some relevant ads whatever this you're into a python development and stuff But yeah, it's not a given So currently we fine tuning at this game So it gives them normality score. Yeah, that one is wrong Don't get point five. That's a good That's good point three, okay Point one for normal But then we also get this one problem point three for this one's okay Then we get point three is fine Point one for this is it's for normal. It should be point one or zero. That's fine the only problem is We'll probably have to add another fuzzy logic rule Is that we also get point one even if we don't have that negative peak It's not cool. Yeah, this one That should have shown a negative peak of zero I hope it's actually did In theory, there should be two negative peaks There should be two negative peaks one before and one after the qrs complex and still transcribing can you I mean that we can just look it up on a Wikipedia what the Normal ECG should look like But okay the current problem. Can we go back to the rules? And there's a problem. Can we double check? Or add a rule That makes sure makes sure When we have Low number. Yeah, when we have too few Negative peaks so essentially zero below point five, whatever it is that the score For abnormality goes up Okay, so it's suggesting to add a rule we have seven rules already And this is to do negative peaks Yeah, we can combine this to The ball should be likely Can we get I don't know how to do it Can you combine these two rules into one? with Or condition Let's see Because in theory kopa should be gpt4 as well, right? So It should be rule three Oops for three Negative peaks too many to few abnormality likely Sounds good Sounds very good Let's test it So normality 0.9 0.9 0.5 What why? And it's problem To few too many negative peaks negative peaks Negative peaks so if we have less than one is a problem I think this thing should Overlap So we have that and we have that rule three By the way, let me know if you have done fuzzy logic before or are you using it as machine learning? I Really do not know why everyone is using neural networks Considering they're like a black box I was thinking doing like a neural network and then use a fuzzy logic to try and explain what is it the neural network doing So let me know what you think about that But currently back to the membership functions and rule number three Is it the problem that the Membership functions do not overlap? Can you plot This one and tell me how to improve the numbers there The rules generally should be that If we don't have any negative peaks The Abnormality is highly likely if we have between One and two negative peaks. It's normal and above two is again highly Likely to be abnormal ECG You have to check this Text to speech thing. This should be peaks not peaks It's highly likely If we have between one and two It's a normal CG and above two is highly likely to be abnormal a CG Do I need to specify rules with unlikely abnormality? Wouldn't it contradict the other rules potentially Obviously mispronouncing rules Obviously Specifying rules for different scenarios including those that lead to outcomes like unlikely abnormality is crucial for several reasons Completeness including rules for various scenarios ensures your system can handle a wide range of input This includes not only clear cut cases of abnormality But also cases where the data suggests a normal condition by explicitly defining what constitutes normal in your system You provide a more complete and nuanced analysis Balance and contrast rules that lead to unlikely abnormality provide a necessary balance and contrast to those that indicate a high likelihood of abnormality This helps prevent the system from being overly biased towards diagnosing abnormalities It's important in medical applications where false positives can be as significant as false negatives Avoiding contradictions in fuzzy logic rules don't necessarily contradict each other. Instead, they offer different perspectives on the data Each rule contributes to the final output based on its conditions The system then aggregates these contributions to reduce a nuanced result By having rules for both likely abnormality and unlikely abnormality You ensure that the system can differentiate between different states in a nuanced way Defusification in the defusification step where the fuzzy logic results are converted into a single crisp output Having a range of outputs from likely to unlikely abnormality allows for a more accurate and meaningful final value This is especially important when the outputs of the fuzzy system are used for decision making In your case having a rule that explicitly states when an ecg is likely normal or unlikely abnormality is important for cases Where the waveform does not exhibit clear signs of abnormality This helps to avoid overdiagnosing abnormalities and provides a clearer understanding of what the system considers normal Remember the goal of a fuzzy logic system is not just identify when something is wrong You also have to recognize when it's right Okay, we combine this to a control I Combine these two rules Okay, we're currently actually not looking at the amplitude And it's doing github What that's a new error that should work now, right? It's working again. I don't know what happened just threw me out All right, we wanted to review this bit of the code With this Error This is because We are currently we currently don't have any rules for amplitude Okay, so we added three more rules that are to do the amplitude feature Amplitude feature We're not currently using them. We have to update this Just go select all that control i Update and job 10 rules What's up It's super long. What's that rule 11? Okay, do I really need rule number 10? Isn't it already covered by Rule rules one two three or four? Yeah, let's get rid of it Yeah, probably amplitude Something like this meant to be done by a cardiologist or ecg specialist technician A ecg waveform reviewer I'm only doing it because I do not have access to one someone wants to Once if someone has experience in this field, please do let me know these rules just Should apply common sense Just worry with the Amplitude So we have low medium high And it's actually think those they used for exporting so we can plot in In the front and Yeah, otherwise, I'm not sure why Why is it done? Don't remember why the way you return All the membership functions What is returned by the fuzzy logic analysis function? It's asking me to provide a code that's all So I'm selecting it Might be too much how many currently we have like three 300 lines It's kind of getting out of hand Don't think we need this kind of stuff Just about this code. Can you remind me? Why are we returning All the membership functions? Don't remember is it so We can plot them On the front end Also the amplitude Rules the low medium and high there's a bit of a concern there because It might be that Currently high is actually Normal ecg Well, because we don't have any examples with Extremely large amplitudes So the normal ecg waveform is What we have for A normal waveform So we have this Why this goes just going up? All right, because that's a normal I forget how the game works Because that's a normal ecg that I didn't click on That's okay So the row amplitude is currently 300. It's going all the way up to 349 Yeah, there I still for normal ecg I get the abnormality score of 0.5. That's not right Before I was getting 0.1, which was better. Okay. Anyway, we need to keep What's actually training it adjusting those rules abnormality Value for normal waveform Is 0.5 Before it used to be 0.1, which is More Likely Which was better It seems like you're observing unexpected behavior in your fuzzy logic system related to the classification of normal ecg waveforms Specifically the abnormality values for what you expect to be normal ecg readings are higher than anticipated Let's break down the potential issues and consider some solutions Raw amplitude if the raw amplitude for normal ecg is consistently around three, but your system scales are down to 0.5 This could be an issue with how amplitude normalization is being done abnormality score for normal waveforms previously normal waveforms had lower abnormality scores around 0.1 Which aligned better with your expectations Now you're seeing higher scores around 0.5 indicating a potential misalignment in the system's interpretation Normalization of amplitude review the normalization process for amplitude It should accurately reflect the range of amplitudes found in normal ecg data If three is a typical amplitude for a normal ecg the normalization process should consider this Membership function adjustments revisit the membership functions for amplitude They might need adjustment to better capture the range of normal and abnormal amplitudes based on your dataset Rule calibration the rules involving amplitude might need recalibration If a normal ecg amplitude is leading to a higher abnormality score It suggests that the rules or the membership functions are not accurately capturing the nature of the data Collect more data more data, especially with known classifications can help in recalibrating the membership functions and rules Real-world ecg data, particularly with expert annotations can be invaluable here Expert consultation consult with medical experts to understand typical amplitude ranges in ecgs This expert knowledge can guide the adjustment of your membership functions and rules Testing and iteration continuously test the system with different scenarios and iteratively adjust the rules and membership functions This process helps in fine-tuning the system to respond accurately to various waveform characteristics Incremental adjustments make small incremental changes to the membership functions and rules then test the system's response This approach helps isolate the impact of each change Logging and analysis implement detailed logging of the system's input and output values Analyze these logs to understand how different inputs are leading to the current outputs By methodically adjusting and testing your system, you can align the abnormality scores more closely with what is expected for normal versus abnormal ecg waveforms This process might require several iterations to get right, but it's essential for ensuring the reliability and accuracy of your diagnostic tool Providing code suggestions or prompts for github copilot to modify the actual script Understood, I'll provide both code suggestions and prompts for github copilot to help you modify the script Adjusting amplitude normalization, s Suggestion, review and modify the normalization function for amplitude to ensure it reflects the real amplitude range in normal ecg's tuning membership functions Suggestion, modify the amplitude membership functions to align with real-world ecg amplitude data Code snippet, python copy code amplitude, low equals fuzz, trimf, amplitude, universe, zero, zero, one, hash adjust these values based on data Amplitude, medium, equals fuzz, trimf, amplitude, universe, 0.5, 2, 3.5, hash adjust these values Amplitude, high, equals fuzz, trimf, amplitude, universe, 3, 4, 5, hash adjust these values Suggestion, re-examine and adjust the rules that use amplitude to better classify normal and abnormal ecg's Good snippet, python copy code rule 8 equals control, rule, amplitude, medium, abnormality, unlikely, hash adjust rule as per new definitions For amplitude normalization, write, write a python function to normalize ecg amplitude values based on a maximum normal amplitude threshold for tuning membership functions Create python code using psychic fuzzy to define membership functions for ecg amplitude with updated ranges to accurately reflect normal and abnormal readings Refining fuzzy logic rules Generate python code to adjust fuzzy logic rules in a medical diagnostic system where ecg amplitude is a key factor Ensuring accurate classification of normal and abnormal readings Remember, when using github copilot or any iBase code generation tool, it's important to review and test the generated code thoroughly These tools can provide a great starting point, but they may not always fully grasp the nuances of your specific application or data Amplitude didn't change So we have low, medium, high Um Okay, real world, but the way those waveforms are Generated, I don't know how real world they are, but it meant to be normalized. I don't know what you're talking about So obviously You're welcome Can you check the code again? The amplitude membership function should be using Normalized values from zero to one Generally, I thought The actual amplitude of the ecg waveform is not Crucial like we're not looking at the absolute numbers, but the ratios between different peaks For example Yep You're right in the context of fuzzy logic for ecg waveform analysis The absolute amplitude values are less important than their relative proportions or normalized values The goal is to interpret the ecg waveform characteristics in a manner that reflects the clinical significance Often focusing on ratios or relative changes rather than absolute measures Given that the amplitude values are normalized between zero and one the membership functions should be defined accordingly Here's how you might adjust them Low amplitude considered low if it's significantly below the normal range Medium amplitude represents the typical amplitude range for a normal ecg High amplitude significantly above what is typically observed in a normal ecg If the relative ratios between different peaks e.g. The ratio of the heights of the p-wave q rs complex and t-wave in an ecg Are more clinically relevant? You should ensure your system captures and analyzes these relationships. This might involve calculating ratios developer method to calculate the relative ratio They're going places now. Okay, so we happy to And let's check the signal. I mean, yeah, this is cheating Because we're actually looking at the signal and adjusting the fuzzy logic parameters. The fuzzy logic parameters ideally should be generic Because then we can apply them for any ecg waveform But currently the amplitude So if I have a value of three four nine, the normalized amplitude is one And three four nine We get some problem with that code And we need some example with like a large amplitude noise So Yeah, the other thing with the way The normalization works is uh I think it's taking from memory. It's it's actually storing The maximum value and updating it So if the current value is larger than what we previously had it will Make the current value The absolute Maximum I think it's a global variable or something So we might need to change that And also consider an option like with the current small dataset is that the the highest amplitude is of a Normal ecg waveform dynamic maximum amplitude your system updates the maximum amplitude based on the highest value seen so far This approach could be problematic because if an anomalously high amplitude Possibly due to noise or a rare event is encountered excuse the normalization for all subsequent data Implications such a method might not be ideal for a generic system intended to be applied to any ecg waveform Could lead to misinterpretation of normal amplitude values if the global maximum is set too high fixed maximum amplitude See sitting using a fixed maximum amplitude value based on clinical knowledge of ecg waveforms This fixed value would represent the upper limit of what's considered a normal amplitude in a typical ecg Use a statistical approach to determine a reasonable maximum amplitude This could involve analyzing a large dataset of ecgs to find a value above which a certain percentage eg 95 percent of all amplitude readings fall this method would be more robust against outliers and would adapt to dataset used Implement outlier detection in your system to identify and handle anomalously high amplitude readings that might skew your normalization This could involve techniques like setting thresholds using standard deviations or more complex statistical methods Consult with medical professionals to understand the typical range of amplitudes in ecg waveforms and use this information to set your Normalization parameters adjusting the normalization function mo Handling outliers Implement a method to detect and manage outliers in your amplitude data Adapting your system to use a more stable and clinically relevant method for amplitude normalization will likely improve its generalizability and accuracy This change combined with outlier management should make your system more robust and applicable to a broader range of ecg waveforms Remember continuous testing and consultation with medical professionals are key to find tuning such systems If you are a medical professional do let me know What you think but As well as the students or anyone else can Provide the feedback Sorry, just isn't easy fixed just removed a rule nine and Simplify and rule eight to just include the low A amplitude So when they have one rule with amplitudes that when it's low the abnormality is likely Normalized amplitude is to be fixed It's like it's stopped working Yeah, three four nine It's why I'm like 37 times amplitude is being mentioned Calculate amplitude Yeah, it's not working correctly Just date What? No Definitely not It's not what we meant Can we review how the row amplitude Is being normalized? It doesn't seem to be correct To begin with does that make sense because we don't have the global variables yet? The global variables Yes said to infinity and to clean the code we're not normalizing Yeah, we're not normalizing the number of peaks by the minute to do this Certainly Let's review how the raw amplitude is being normalized in your code and address the issue with the global variables and their initialization Based on your code snippet. Here's how the amplitude normalization seems to work Amplitude calculation the amplitude of each waveform is calculated as the difference between its maximum and minimum values Normalization function the normalization of the amplitude appears to be done through the normalized function Which scales the amplitude relative to a global minimum and maximum global variables initialization the global variables for amplitude Global underscore min underscore amp and global underscore max underscore amp are initially set to infinity and negative infinity respectively This setup is typical for establishing a running minimum or maximum really relies on updating these values correctly during the processing of each waveform Updating global variables It looks like you might be missing a step where you update the global underscore min underscore amp and global underscore max underscore amp With the current waveforms amplitude. This step is critical for the normalization process to work correctly Update global amplitude ranges you need you need to adjust the global minimum and maximum amplitude values each time you process A new waveform similar to what you're doing with peaks normalize the amplitude based on these updated global values Here's how you might modify the code initial extreme values The initial extreme values for the global variables are standard for capturing the range of data However, ensure that these values are updated correctly during processing normalization method review whether the normalization method Subtracting the min and dividing by the range suits your applications needs and correctly reflects the variations in amplitude You expect to see in normal and abnormal ecg waveforms Robustness against outliers consider how outliers might affect your global maximum and minimum values Depending on your data, you might need a more robust method to handle extreme values or noise By adjusting how you update the global amplitude range and apply normalization You can ensure that your system more accurately reflects the characteristics of the ecg waveforms that analyze Yeah, how about adding other features extracted from the ecg waveform? Can you make any recommendations? a kurtosis and other stuff certainly Adding additional features extracted from ecg waveforms can enhance the accuracy and robustness of your analysis Here are some key ecg features commonly used in waveform analysis heart rate hr asterisk d Description the number of heartbeats per minute Extraction calculate the time interval between successive qrs complexes r intervals and convert it into beads per minute qrs complex duration description the duration of the qrs complex which represents ventricular depolarization Extraction measure the width of the qrs complex in milliseconds Clinical relevance prolonged or shortened qrs duration can indicate various cardiac conditions including bundle branch block or ventricular hypertrophy Description the interval from the start of the q wave to the end of the t wave Extraction measure the time from the beginning of the qrs complex to the end of the t wave Clinical relevance abnormal quart intervals can indicate electrolyte imbalances medication effects or genetic conditions like long quart syndrome p-wave analysis Description examination of the p-wave representing atrial depolarization Extraction analyze the amplitude and duration of the p-wave clinical relevance Abnormal p-waves may indicate atrial enlargement or atrial arrhythmias st segment analysis Description the segment between the qrs complex and the t wave Extraction evaluate the elevation or depression of the st segment relative to the baseline Clinical relevance st segment changes can be indicative of myocardial ischemia or infarction p-wave analysis Description analysis of the t-wave which represents ventricular repolarization Extraction assess the amplitude and symmetry of the t-wave Clinical relevance abnormal p-waves can indicate electrolyte disturbances ischemia or ventricular hypertrophy Description the variability in the rr interval Extraction calculate the differences in the rr interval durations over time Clinical relevance increased rr variability can be a sign of autonomic nervous system dysfunction To implement these feature extractions you'll need algorithms capable of accurately identifying ecg waveform components p-wave qrs complex t-wave etc This often involves signal processing techniques like filtering peak detection and waveform segmentation Once extracted these features can be integrated into your fuzzy logic system or used as inputs to a neural network They provide a richer data set that can lead to more accurate and clinically relevant analyses The extraction of these features from raw ecg data can be complex and requires a good understanding of both signal processing and the clinical aspects Of ecg interpretation collaboration with medical professionals is advised to ensure the clinical validity of your feature extraction methods It is now a relevant tool and the ecg synthetic signal generator so we can use it to generate more A waveform examples it has a noise to it as well noise option to it as well That's the default Ideally we also have a drop down That pre-selects this value To show Different types of ecg waveform abnormalities And yes, this is easier than dealing with eeg that is More random might finish here Go check out bodykills.com Watch some ads sometimes they don't actually load some hopefully get some relevant ads like A university or not Yeah, I shouldn't say anything brought to you by a university I'll see you next time. 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