 we will also have to fine-tune the fuzzy logic algorithm at the moment. It's not doing very great. It's always detecting every waveform as ab normal, so generating only hits and false alarms and the score goes negative. So we'll need to modify the memory functions and the fuzzy logic rules. Can you give an overview and suggest specific code changes? On the topic of dating the membership functions and the fuzzy rules, can we go over each membership function and each rule? Let's get to it to adjust all these parameters. So if you were an ECG specialist, how would you adjust the membership functions and the fuzzy rules? Okay, we do not currently have easy access to cardiologists or any other ECG specialist, so we will have to fine-tune the parameters selves. Now I'm sharing a couple of images of how the waveforms look like. Can you rewrite the fuzzy logic analysis function? We've updated membership functions and fuzzy rules include all the comments as to what changes were made. So go over each membership function and each fuzzy rule. So we have quite a few rules. We have a default rule and rules one to six and a rule for abnormal intervals. Okay, it doesn't want to generate anymore, which is gave up on me. Why did you give up on me? I have to go over them in the same way. We reload the page. Can we continue generating the same analysis? We've suggested changes for all the rules. I have to go over these membership functions again. So we have amplitude. I haven't changed that. Six ones haven't changed that. So I don't know why it didn't generate that code considering there was no changes made. Okay, rid of it. Now amplitude, we have low, medium and high. It's going normal. That's actually a better name, but then it will start planning. So we'll take the problem with the current amplitude is that it's really became slow because we're like at minus 150 points and the points go low. It comes much slow. Yeah, ideally, we would not be looking at the signals that we have. I actually want to add much more as well. But the problem with the amplitude, so we don't want to end up fine-tuning each parameter. We don't want essentially to use the magic numbers. You might change the name to medium. Comment that out for a sec and see essentially how good of a cardiologist the GPT-4 can be. So we have positive peaks, rated positive peaks. Just make sure that the names are the same. We have too few, normal too many. Yeah, two, three peaks can be normal. Accounting for P and T more than three peaks might be a normal. One positive peak is typical. Okay, so the thing is, it later also important like once we go over the rules, it makes a huge difference because we're essentially giving a weight for each situation. A big sharpness, a sharpness. It's also changed the values. We keep them to zero and one, and this one might be a bit finicky. So your sharper ones are more typical. Broad range for normal up-peak sharpness. Their sharp peaks may indicate abnormality. They're sharp. Yeah, we have low normal high. Yeah, let's try that. So we have both binary normal and normal, but we also have the likelihood which is abnormality is likely possible or unlikely. And it's suggesting, I really have that line. That's not antecedent. Yeah, so the range for abnormality is from zero to 1.1 with steps of 0.1. We have rule one for normal SG considering all factors. Okay, it's a lot. It might actually keep the rules proper names. This one looking up should post the peaks. They're saying essentially if everything is normal, abnormality is unlikely. Okay, so we actually need to make sure this is working for a dataset. In our dataset, we only have one normal VCG type. The other ones are abnormal. Yeah, I actually don't expect it to work with the GPT-4 suggestions. So I'm not sure if it means it's not a great cardiologist or just in our case, the sample data is different. So I'll have to tweak things to suit the waveforms that we are generating. It's suggesting so there is like an edge cases of normal CG. So when it's always normal, when it's clearly abnormal, rule for potentially abnormal, interestingly enough, it's not even looking at RPIC sharpness for being potentially abnormal. Rule for potential abnormality based on RPIC sharpness. Okay, so I take my comment back. It actually has a separate rule for RPIC sharpness. And rule five, incorporating RPIC sharpness being normal, which is normal, positive peaks normal. How's it? Rule five different from rule one. That's rule one doesn't have RPIC sharpness. Now it does seem to be rule one has everything as the and operators. Rule five has and and then or which can make sense, but then the description is not right. That's also decided to rewrite the analyze the CG function, which we've been actually asked for. We didn't have it. We have something, right? Well, currently it's in the same function. I think it's suggesting to split. Yeah, because currently, the fuzzy logic analysis is like way too long. So it refracted it into two parts. Do we have a calculate membership? Yeah, as I said, it's a pseudo pseudo code. So it's not quite relevant to the code we provided. Suggesting breaking down that we currently have would not even work. Yes, something broke that code too many doesn't exist. So I have low, medium, high for amplitude. We do have too few normal too many positive peaks. It's not too many. We had too many before and it used to work. It's wrong, isn't it? Didn't do a mistake? Yes, it did. Now I don't trust that many mistake there as well. I think that number might be six instead of five because make it five over there. Well, yeah, that one should have been labeled as normal. Well, that's because of the noise. We have like 29 positive peaks. That's not right. So let's reduce the noise to zero. That's abnormal. That's correct. Let's just wait for a normal. That's abnormal. Correct 0.7. That's abnormal. Correct 0.6. What do we get for a normal one? If we're normal, we get 0.59. Yeah, 0.65, 0.59 is the normal one. Yeah, there's not much difference between them. That's the problem. And we get 0.59 for another the abnormal anyways. That's no good. You have to modify the rules quite a bit. Yeah, I guess an error like that means that for some conditions, there were like no rules. No rules were met at all, even though we have a default rule. So I have to check what that error is all about. Yeah, the other way is those rules and proper names. Yeah, I like it. Wait, before we have more rules, just a second ago. All right, yeah, we do. So we have five rules. Instead of the seven that we have, we are happy to try them out. We have five rules. And why do we need this analyze? ECG is not being used. It's just suggesting to split the function. It can make sense, because it's too long. It's the abnormality analysis. Yeah, the decision is not good. That shouldn't be a result below a certain number. That's what we have the membership functions for. It doesn't tell you for which. I remember which one wasn't and was this medium for amplitude. For a sec. Yeah, we should have the interval. What's the problem? I don't know where this error is coming from. I don't know. It might be going in circles now. Anyway, we need to fix those membership functions. I'll see you next time. Bye. Okay, it started happening. The error started happening after changing the membership functions, after changing the positive logic analysis. Can we again go over each membership function one by one, suggest the default values, and then we go over the fuzzy rules as well one by one of what we currently have. So if we can act as a cardiologist, that would be great. So you should be agnostic to what the waveforms actually look like. And we want to generate general membership functions and activation rules for the fuzzy logic. Sure, let's tackle the fuzzy logic membership functions and rules as if I were a cardiologist, setting default values and rules based on general knowledge of ECG characteristics. Amplitude, asterisk. Low, normally ECG amplitude is not very low unless there is poor electrode contact or the patient has a low voltage ECG, which is rare. We will set this to cover a very small portion of the range. Medium, this should cover the typical range for an ECG, which is what we expect in most healthy adults. Hi, a high amplitude might indicate ventricular hypertrophy or other conditions, so we'll set a range that captures significantly higher than normal values. Positive peaks, asterisk. Too few, in a typical ECG, there should be one r wave per cardiac cycle. If there are less, it could indicate a serious condition. Normal, one positive peak per cycle is normal, which is the r wave. Too many, more than one r wave may indicate conditions like ventricular tachycardia. Negative peaks. Too few, negative peaks are less common and their absence might not always indicate an abnormality. Normal, normally, we don't expect to see many negative peaks, so we'll set a conservative range. Too many, multiple negative peaks could indicate an inverted T wave or an abnormal Q wave. R peak sharpness, asterisk. Low, an r wave that is too broad might indicate a conduction delay. Normal, a normal r wave will have a certain sharpness that corresponds to a healthy heart's conduction speed. High, a very sharp r wave could indicate a hypercute myocardial infarction. Rule for normal ECG, i If the amplitude is medium, positive peaks are normal, negative peaks are normal, and r peak sharpness is normal, then the ECG is likely normal. Rule for clearly abnormal ECG, if If amplitude is high or low, or if there are too many positive or negative peaks, then the ECG is likely abnormal. Rule for potentially abnormal ECG, if If there are too few positive or negative peaks or the r peak sharpness is abnormal, then the ECG might be abnormal. Let's define each membership function and corresponding fuzzy rule, assuming a general understanding of ECG characteristics with our specific waveform visuals. The goal is to create a fuzzy logic system that can generalize well over typical ECG features. Amplitude, asterisk. Low, uncommon in healthy ECGs and could indicate technical issues or specific medical conditions. Medium, represents the amplitude range typically observed in a healthy population. High, significantly above the normal range, possibly indicating cardiac hypertrophy or other abnormalities. Positive peaks, e.g., r waves, asterisk. Too few, less than one positive peak might be due to a rarer arrhythmia or an error in ECG recording. Normal, typically, one positive r wave per cardiac cycle is expected. Too many, more than one positive peak could indicate extra cislies or other forms of arrhythmia. Negative peaks, asterisk. Too few, maybe normal as negative peaks are less common, e.g., q waves. Normal, typically, zero to one negative peak is expected per cycle. Too many, more negative peaks can suggest ischemia or infarction. R-peak sharpness, asterisk. Low, indicates a less sharp r wave, which could be due to a conduction delay. Normal, a sharp r wave is typical of a normal ECG. High, extremely sharp r waves could be pathological. Normal ECG rule, if, if the amplitude is medium and both positive and negative peaks are within normal ranges, then the r wave sharpness is normal, the ECG is likely normal. Abnormal ECG rule, if, if the amplitude is very high or very low, or if there are too many positive or negative peaks, the ECG is likely abnormal. Potentially abnormal ECG rule, if, if there are too few positive or negative peaks, or if the r wave sharpness is high or low, the ECG might be abnormal. These rules and membership functions should create a balanced fuzzy logic system that can distinguish between normal and abnormal ECG patterns with reasonable accuracy. Remember, the exact ranges for, we're suggesting a set of rules that can make educated guesses about the normality or potential abnormality of an ECG pattern based on a general understanding of ECG characteristics. Rule for normal ECG, rule underscore normal, if, if the amplitude is either too high or too low, or if there are too many positive or negative peaks, then we assume the ECG is likely abnormal. There are too few positive or negative peaks, or if the r peak sharpness is not normal, either too high or too low, then we consider the ECG possibly abnormal. We can certainly define additional rules if needed, especially if there are specific combinations of these features that are known to be indicative of certain conditions. However, the three rules above provide a basic structure for the fuzzy logic analysis. No, I think the PR and RT interval should be between zero and one, but yes, we might want to double check that. And also for certain waveforms, I get them both as undefined. Alright, if the PR and RT intervals should be between zero and one, then the membership functions for the PR and RT intervals need to be adjusted to reflect this range. This also means that when you get a value of undefined for these intervals, the system should handle it by assigning a default value that falls within this range. That's how you can adjust the PR underscore interval and RT underscore interval definitions and handle undefined values. In the fuzzy logic analysis function, you need to handle undefined values for PR and RT intervals. A common approach is to treat undefined as the midpoint of the normal range or as a neutral value. It's important to normalize the actual PR and RT interval values to be between zero and one before setting them to the fuzzy control system. If the intervals are measured in seconds or any other unit, you need to find a way to convert them to a zero to one scale that matches the membership functions. If an interval is undefined, you assign the default underscore normal underscore value which is a neutral value within the normal range. This way, the fuzzy system can handle all cases consistently. Let's expand the fuzzy rules to include all the variables, amplitude, positive peaks, negative peaks, r-peak sharpness, PR interval, and RT interval. Integrating all these variables will make the fuzzy logic system more comprehensive and potentially more accurate in its analysis. Rule for normal ECG, if the amplitude is very high or very low, or there are too many positive or negative peaks, the ECG is likely abnormal. If the r-peak sharpness is either too high or too low, the ECG might be abnormal. If either PR or RT interval is too short or too long, the ECG is likely abnormal. Rule for mixed indicators, combining various factors. If there are multiple indicators of abnormality, e.g., abnormal amplitude with abnormal peak counts, or abnormal intervals with abnormal peak sharpness, the ECG is likely abnormal. Let's implement these rules. These rules consider various combinations of ECG features to provide a comprehensive analysis. The rule underscore normal is designed to capture cases where all ECG features fall within the normal ranges. The other rules address different combinations of abnormalities, thereby covering a broad spectrum of potential ECG patterns. This setup should improve the ability of the system to classify ECG patterns accurately. If the PR and RT intervals show ways output and number, but you're facing issues when there aren't enough peaks to calculate these intervals, which provides the calculate underscore intervals function. This function needs to handle cases where there are not enough peaks to calculate PR and RT intervals reliably. Given that we're analyzing individual PQRS complexes and not a continuous waveform, the approach for calculating PR and RT intervals needs to account for different scenarios, like when certain peaks are missing. Handle missing peaks, if the necessary peaks for calculating PR or RT intervals are missing, calculate these intervals based on available data or default to a certain value that indicates an incomplete waveform. Fallback values, when specific peaks are not found, use fallback values that make sense in the context of an ECG analysis. For example, if the RP is missing, we might assume a default position based on the length of the waveform. If there are two peaks, we assume they are RT and estimate the P peak. If there are fewer than two peaks, we use a default value. This approach ensures that the function always returns numerical values, preventing errors due to undefined values. Remember, these are approximations. In real-world applications, it's crucial to validate such assumptions and fallback strategies with domain experts. Wearing my virtual cardiologist hat here, let's dive into how this application can be a game changer in the medical field, particularly in training medical students, while also keeping an eye on necessary disclaimers. Practical understanding of an ECG analysis, this tool is like a digital sandbox. It allows students to explore ECG waveforms in a controlled environment. They can input different waveforms and see how changes in peaks, intervals, and amplitude affect the fuzzy logic analysis. It's hands-on learning without the risk. Demonstrating the role of AI in medicine, the app showcases how I can assist in medical diagnostics. It's not just about learning to read an ECG, it's about understanding how technology can augment human skills. This is crucial in an era where tech and medicine are increasingly intertwined. Developing critical thinking, by observing how the fuzzy logic rules apply to various waveforms, students learn to think critically about ECG interpretations. Yet to see that diagnosis is not always black and white. Sometimes, it's fuzzy, pun intended. Research and improvement, for those budding Einstein's in the medical class, this tool can be a research platform. They can experiment with different membership functions and rules, seeing firsthand how tweaks can improve or impair diagnostic accuracy. Disclaimers, not a replacement for professional advice, big flashing lights here, this tool doesn't replace a seasoned cardiologist's expertise. It's an educational aid, not a diagnostic oracle. Always consult real-life experts for clinical decisions. Approximations and assumptions, the app makes certain assumptions, like estimating missing peaks. These are educated guesses, not gospel truths. In the real world, each ECG is as unique as a fingerprint and needs human interpretation. Results may vary, the fuzzy logic is based on predefined rules and thresholds, which might not go all the wild, wonderful variations of human heart rhythms. It's good, but it's not invaluable. Recently evolving, medicine and technology are fast-paced fields. What's cutting edge today might be old news tomorrow. This tool is a snapshot of current knowledge and technology, not the final frontier. Remember, using this tool doesn't automatically grant you the title, cardiologist. You still need to endure countless coffee-filled nights and survive the wrath of attending physicians. This app is like training wheels for ECG analysis. It helps you stay upright, but eventually, you'll have to pedal the complex world of cardiology on your own. While the app can crunch numbers and analyze waveforms, it can't empathize with patients or laugh at your jokes, that's your job. In summary, this application is a fantastic educational tool that brings a taste of ours potential in medicine to the classroom. It's about blending technology with traditional learning, encouraging students to think critically and creatively. Just remember, it's a stepping stone, not the entire journey. I see you've uploaded several ECG trace images with corresponding scores and analysis details, including PR and RT intervals. Here's what might be going on with the PR and RT interval calculations. PR interval calculation, the PR interval is measured from the start of the P wave to the beginning of the QRS complex. The application is only detecting peaks and not differentiating between P waves, QRS complexes, and T waves. It might inaccurately calculate the interval. For example, if the first peak is assumed to be a P wave when it's actually part of the QRS complex, this would result in an incorrect PR interval. RT interval calculation. Similarly, the RT interval calculation requires identifying the R wave and the subsequent T wave. If the peak detection algorithm is not accurately identifying these specific waves, the RT interval will be off. Peak detection sensitivity. The algorithm might not be sensitive enough to detect all peaks, especially if they're small or if the ECG is noisy. This could lead to missing P or T waves, resulting in undefined intervals. Resolution and scaling. If the ECG data is not properly scaled to the actual time units, the intervals calculated could be inaccurate. Make sure that the data's time resolution, samples per second, matches the scaling used in interval calculation. Algorithm robustness. The algorithm might not be robust against noise or artifacts in ECG traits. This could lead to full speaks or miss peaks, skewing the PR and RT intervals. Here are some steps to potentially improve the PR and RT interval calculations. Implement a more sophisticated peak detection algorithm that can differentiate between P waves, QIS complexes, and T waves. Increase the algorithm's sensitivity to low amplitude waves while filtering out noise. Ensure that the data scaling in terms of time is accurate. Validate the algorithm against known ECG traces with annotated P, QIS, and T waves to fine-tune the detection parameters. The application's diagnostic conclusions should always be reviewed by a qualified health care professional, and this software should only be used as a supplementary tool, not a definitive diagnostic system. When training medical students, emphasize the importance of understanding ECG waveform anatomy and not solely relying on automated tools. Additionally, make clear that while the tool can aid in education and pattern recognition, it does not replace the expertise of a cardiologist. Let's get into it now. To address the issues with the PR and RT interval calculations, we need to ensure that. The peaks detected correspond to the correct P, QIS, and T waves. The intervals are calculated only when the necessary waves are identified. The undefined output is handled correctly. Here's an approach to refine the calculation of PR and RT intervals. Based on your description, it looks like the PR and RT intervals are being normalized twice, which would indeed make the values smaller than they should be. The normalization should only occur once. It seems we're normalizing the intervals both inside the process underscore data function and again in the calculate underscore intervals function. Let's fix the code to normalize only once and ensure the intervals are properly calculated. I'll update the calculate underscore intervals function to only calculate the intervals without normalizing, and we'll remove the normalization step from process underscore data. This way, we prevent double normalization. Here's the corrected calculate underscore intervals and process underscore data functions. Now, process underscore data will return none for the PR and RT intervals if they can't be calculated, which is fine for the fuzzy logic analysis as long as it expects to handle none values. Let's clarify, normalization should be performed once, but it should be done in the right place. If the PR and RT intervals are used in the fuzzy logic analysis and need to be normalized, which is typical, as you wonder your input variables to be in the same scale, this should be done just before they're fed into the fuzzy system. The change I suggested was to remove the normalization from the calculate underscore intervals function, because it seemed like they were being normalized again in the process underscore data function. However, if they're not normalized in process underscore data, they should indeed keep the normalization step in calculate underscore intervals. Let's correct that. We'll calculate the PR and RT intervals in calculate underscore intervals, normalize them, and return the normalized values. The process underscore data function then just passes these values through, so it doesn't need to normalize them again. PR and RT interval calculation, the PR and RT intervals are ratios that represent time intervals in an ECG waveform. You've normalized these intervals by the length of the waveform, which is a standard approach to make the interval scale invariant. However, if you're seeing values that are too small or not making sense, it could be due to incorrect peak detection or improper handling of cases where the expected number of peaks is not found. The membership functions are the ranges for, normal, but not be appropriately defined. The rules may not be comprehensive or correctly prioritized to cover all normal cases effectively. There might be an issue with the inputs being provided to the fuzzy logic system, perhaps due to incorrect data processing or normalization. To correct the code and improve the algorithm's accuracy, consider the following steps. Review peak detection logic, ensure that the peak detection is robust and can accurately identify P, QIS, and T peaks in various conditions. Investigate any assumptions made, such as the order of the peaks, and consider more sophisticated ECG feature detection algorithms if necessary. Adjust membership functions. If your membership functions for, normal, intervals are too restrictive, they might not cover all variations of a normal ECG. Re-evaluate these functions and adjust the universe of discourse and the shapes accordingly. Examine fuzzy rules. If the rules are too simplistic or not correctly weighted, the system might not differentiate between normal and abnormal effectively. You may need to add more rules or adjust the existing ones to account for a broader range of scenarios. Double-check normalization. If you remove the normalization step, make sure that the raw values you're now using are appropriate for the fuzzy logic system. If normalization is necessary, it should be done consistently and correctly across all data points. Debugging. Implement a debugging mechanism to trace how the inputs are processed through the fuzzy system and which rules are being triggered for a given output. This can help identify where the system is failing. For the ECG waveforms that are misclassified, you would need to look at the values of each fuzzy variable for that waveform and see which rules are being activated. Be that the membership functions for the normal range are not capturing the values correctly or that the rules need to be adjusted to better handle the edge cases. Without a detailed look at the exact values being fed into the fuzzy system and the logic of the rules, it's challenging to pinpoint the exact issue. However, the suggestions above should give you a structured approach to debugging and improving the system. If after these steps the problem persists, you may need to share more specifics about the misclassified cases and the logic of the fuzzy system for a deeper analysis. The abnormality score in a fuzzy logic system is determined by evaluating the input parameters against a set of fuzzy rules and then aggregating the results to produce a final score. The specific score will depend on the rules you've established and the membership functions that define the categories for each input variable. Negative peaks, 1. For each input variable, determine the degree of membership in each of the corresponding fuzzy sets. For example, if positive peaks of 3 falls mostly in the normal fuzzy set, but slightly in the too many set, you will have membership degrees for each of those sets. Apply the fuzzy rules to degrees of membership. For example, if you have a rule that says, if positive peaks is normal and raw amplitude is medium, then abnormality is unlikely, you will take the minimum membership degree of the normal, set for positive peaks and the medium, set for raw amplitude as the strength of the rule. Combine the rule outputs according to their assigned consequent fuzzy sets for the output variable, abnormality. This typically done using methods like max min composition or max product composition. Defuzzify the combined output fuzzy set to yield a single number. The defuzzification method often used is the centroid or center of gravity method, which finds the balance point of the output fuzzy set. For a precise abnormality score, you would follow these steps using your fuzzy logic control systems API or tool. If you need to implement this manually or in another environment, you would need to replicate the functionality of your current fuzzy logic system according to its design. The membership functions for the positive peaks in a fuzzy logic system are used to categorize the number of positive peaks in an ECG signal into linguistic terms such as too few, normal, or too many. These are usually defined using triangular or trapezoidal shapes, but other shapes like Gaussian or sigmoidal can also be used depending on the system's design. Here's how you might define triangular membership functions for positive peaks based on the universe of discourse, the range of possible values for positive peaks, which I'll assume to be between 0 and 6. In this example, we're assuming that too few is defined as anywhere from 0 to 2 peaks. Normal is defined precisely at one peak but with a range that allows some flexibility from 1 to 3 peaks. Too many is defined as more than 2 peaks with the range extending to the maximum expected number of peaks. The exact numbers used for these membership functions should be based on clinical data and expert knowledge. They will determine the sensitivity and specificity of the fuzzy logic system in identifying normal and abnormal ECG patterns. If more precise or nuanced categorization is needed, you can add more linguistic variables or adjust the shapes and ranges of these functions. In defining membership functions for ECG signal analysis, using Gaussian or generalized bell-shaped functions can provide a natural representation of the data. Gaussian functions are characterized by their mean and spread, while generalized bell-shaped functions are flexible and can create a variety of shapes based on their parameters. Sigmoid functions, which are often used in machine learning, could also be considered as they smoothly map the input between 0 and 1 and are defined by their slope at a specific point. However, these functions have a mathematical complexity that might not always be suitable for real-time analysis or systems with computational limitations. Choice of membership functions should balance accuracy in representing the data with the system's ability to process the information efficiently. For detailed guidance on fuzzy membership functions, you can refer to educational resources that explain these concepts in depth. The error message you're getting indicates that there's an issue with the rule set in your fuzzy logic system not covering the input space adequately. This means that there are some combinations of input variables that are not triggering any rules, leading to an inability to calculate a crisp output. Here are a few steps to troubleshoot and potentially resolve this issue. Rule coverage, ensure that your rules cover the entire input space. There should be at least one rule that will fire for any possible combination of inputs. Membership function overlaps, check your membership functions for each antecedent. They should overlap to some extent to avoid gaps in the logic where no rules would fire. Default rule, consider implementing a default rule that will fire if no other rules do. This can be a catch-all rule with less weight that provides a default action in case of sparse inputs. Rule adjustment, adjust the existing rules to be less restrictive. If they're too specific, they might not apply to a wide enough range of input scenarios. Bud validation, validate the input values to ensure they're within the expected range. Possible that inputs are not being scaled or normalized correctly before being fed into the fuzzy logic system. System testing, test your fuzzy system with a wide range of input values to ensure robustness and identify any input combinations that don't trigger a rule. Debugging information, print out which rules are firing for given inputs during testing to understand better why certain inputs are not producing a crisp output. After making these checks, if you're still facing issues, you might want to consider consulting a fuzzy logic expert who can look into your specific implementation in detail. Membership functions define how each point in the input space is mapped to a membership value between 0 and 1 for each fuzzy set. It's crucial that these functions are defined correctly to ensure the system's reliability. Short, define with a trim from 0 to 0.30. This means any PR interval from 0 to 0.30 is considered short. Normal, overlaps with short and long, from 0.30 to 0.99. A value around 0.66 would be considered completely normal. Long, starts overlapping with normal around 0.66 and goes to 1. Short, similar to PR, defined from 0 to 0.30. Normal, overlaps with short and long, from 0.30 to 0.99. Long, starts overlapping with normal around 0.66 and goes to 1. Low, from 0 to 0.3. Normal, overlaps with low and high, from 0.2 to 0.8. High, starts overlapping with normal around 0.7 and goes to 1. For the positive peaks and negative peaks, the membership functions were initially defined with specific ranges that cover from 0 peaks to a maximum number, 5 for positive, 4 for negative. It's important to ensure these ranges reflect realistic counts of peaks that could occur in an ECG waveform. Low, defined from 0 to 0.1. Medium, a broad range from 0.05 to 0.95, covering most of the possible amplitude values. High, from 0.9 to 1, overlapping with medium. Now, let's move on to the rules. Rule normal, this rule states that if everything is classified as normal, the ECG is likely normal. This is a straightforward rule, but it might be too restrictive if all conditions need to be normal for it to fire. Rule abnormal amplitude peaks, this rule says that if the amplitude is too high or low, or if there are too many peaks, the ECG is likely abnormal. This seems to make sense, as extreme values in these categories often indicate abnormalities. Rule potential abnormal sharpness, if the R-peak sharpness is too high or too low, it indicates a possible abnormality. This is a good rule, assuming that extreme sharpness values are uncommon. Rule abnormal intervals, this focuses on the lengths of PR and RT intervals, marking the ECG as likely abnormal if they're too short or too long. It's crucial that these are set correctly according to clinical standards. Rule mixed indicators, this rule appears to catch mixed signals from the inputs. If there are conflicting indicators from different inputs, it marks the ECG as likely abnormal. However, this rule could be too complex, leading to potential issues if it's trying to cover too much. It's important that these rules are not contradicting each other and that they cover all possible scenarios. Each input should have a path through the rules that leads to an output, avoiding the situation where no rules are triggered. All right, to adjust the fuzzy logic system to consider an ECG waveform with the given parameters as normal, we can tweak the membership functions like this. Positive peaks, asterisk, too few, keep current from 0 to around 2. Normal, adjust this range to include 3 as a strong member, so maybe from 2 to 4. Too many, adjust this range to start higher than 4. Negative peaks, too few, keep current from 0 to around 0.5. Normal, adjust this range to include 1 as a strong member, so maybe from 0.5 to 1.5. Too many, adjust this range to start higher than 1.5. Amplitude, low, keep current from 0 to around 0.1. Medium, broaden this include 0.58 as a strong member, so maybe adjust to 0.1 to 0.7. High, adjust to start around 0.7. Low, maybe 0 to 0.3. Normal, broaden this range to strongly include 0.416, so perhaps 0.3 to 0.7. High, adjust to start around 0.7. Short, keep current from 0 to around 0.2. Normal, adjust this to include 0.3 as a strong member, so maybe 0.2 to 0.4. Long, adjust this range to start higher than 0.4. After making these adjustments, you'll want to validate the new setup by testing it with a variety of known ECG waveforms to ensure that it still correctly identifies abnormal patterns when they're present. You should also consider consulting with domain experts to validate the clinical relevance of these ranges. Based on the guidelines we discussed, here is how you could modify the fuzzy logic definitions in your Python code to potentially classify a normal ECG waveform with your specified values as normal. Make sure to test the updated system thoroughly to ensure that it behaves as expected across a range of input values. This is a starting point, and fine tuning may be required to achieve the desired specificity and sensitivity. It actually might be in business. It's now we're not making any mistakes. It did one false alarm, but that's pretty much it. It's not detecting the normal ECG waveform as abnormal, which is good, but we kind of faked it with all the GPT4 to adjust the membership functions to actually make sure that the specific abnormal ECG waveform that we have would not be labeled as abnormal, so it will be always labeled as normal. So it adjusted the membership functions. Obviously the question is how well will it work once we add more waveforms? That's what we want to see. And also, well currently already as it is, we can see how, oops, that's me making, yeah, so I'll start making mistakes. The bot will be winning and this is the no noise scenario. They can start adding some noise, see what happens. The hypothesis obviously will be that the robot will start making mistakes as you increase the noise level. I can't compete with it already. So obviously it does the same task as I am. It gets the raw waveform values. It doesn't have the labels and it's able to classify correctly. This one here or something somewhere. We'll check the log, the terminal in a sec. This is it. I can do it anymore. I think I can push to 1000 a score, but the bot will just keep going correctly forever. No breaks, no food, no pay. It just goes, well, it just pays for electricity, right? But how much humans gobble up electricity? Anyway, I'm done here. I can't do it anymore. The bot will keep going. And obviously it doesn't care about speed as well. It does the decision instantaneously. Oops, and I'm making mistakes already. Yeah, maybe I can really push it. I can get to 1000 and it will be really hard for me to keep it there. That's the other thing. So it's the longer term performance that will suffer if I keep doing the task. Because if I stop clicking, I think the, pretty sure the score will start going down. So I'm making a lot of misses already because it's too fast for me and the bot is going strong. And in our case, in this session, the GPT acted as a cardiologist. Well, fake, obviously fake cardiologist. Yeah, it did provide disclaimers. You can see prior and was able to tune up the parameters for the algorithm to do the classification properly. So I don't know where that one false alarm came from. I'm pretty sure it came from where we had the noise in the signal because the default option when you load the page, there is noise introduced. Yeah, so it will be interesting to see now what will happen once we start introducing noise to the system. So just introduce just a tiny bit. Yeah, another false alarm. Yeah, I'm not sure which one is it not doing correctly. But yes, there's essentially too many positive peaks. So it's messing up the numbers. There's no filter at the moment. It would be probably a simple filter would solve this. I didn't want to filter so that the human and the bot essentially look at the same waveform. So the bot doesn't have any unfair advantage. Yeah, so now it's actually classifying everything as abnormal. It's including the normal waveform. That's why the false alarm rate is going up. What if we have just the tiniest amount of noise? No, it still gets rigged somehow. Why? Yeah, this is where it starts picking up errors. So that's not great. We'll continue next time. See you in a bit. Hey, can you summarize what we did today and say bye bye to the viewers politely? Hey, we've tackled a complex task of fine tuning a fuzzy logic system to analyze ECG waveforms. We adjusted membership functions specifically to ensure a particular normal ECG waveform wouldn't be misclassified as abnormal. This approach helped reduce false alarm significantly in a noise-free environment, which is a great start. We also touched on the potential for the system's performance as we introduce noise and more varied data, hypothesizing that while a human might struggle with increased noise levels, the bot could potentially maintain high accuracy due to its consistent, tireless analytical capability. It's a wrap for today's session. We've made solid progress, and it's exciting to think about the potential of this system as it gets exposed to more data and scenarios. Goodbye for now, and take care.