 or sound check. Do I have sound? Do I have sound? That should be fine. I don't need to keep monitoring. As long as I don't mess around with the USB connections. Should be fine. Should be losing my microphone or anything. That's the chat as well just in case someone joins in. Let's seem to work. So finished this web application yesterday. CgSyntheticSignalGenerate.go. Check it out. It's on the website. This page. I'll share it in the in the notes. In the comments. And that's working fine. There's no refresh button, but you can just refresh the page. And the other thing we would like to do is to... Yeah, so that's our IntelliQzG. What we would like to do is have a feature extraction from it. It's essentially something that labels the PQRS, PQRST waves. It actually puts them in, labels them in separate colors. That would be nice. Let's see how well. We had something previously working. We have the description for it. And a video as well. To look like that. Yes, it will label... This is my 30 second signal. Yeah, it will label ECG as such. It was working nicely. Unfortunately, due to technical issues. To reboot the whole, restart the whole server so we don't have that backend. Therefore, this doesn't work anymore. But it's okay. Because we have JGPD to make a similar tool for us. So let's just polish this. What was that T7 coming from? To remember what the range, minimum maximum is this? No, that's okay. I think we need the range. At least not yet. I don't know why would we need one? So we have those as outputs. And we'll have a ECG shot as per usual with different waves in different colors. We use American, American English. None of that. You, rubbish. PUF, QRS, DWAVE, ECG. Then we also have measurements for heart rate, mean, PR, interval, time, interval, mean, cure, ISO, level, mean. I can use... So we could use Neurokit, which actually allows simulating ECG waveforms. Flask, so it's available for you to play with. And numpy, this relevant paper. We have a scroll, do we need a scroll option? Just get away with window size. So this will be, oops, another input, outputs, Python packages, that paper might be not so relevant. We have this description that we wrote a while back. We already punched something into just checking my mouse. I realized all my previous streams didn't have the mouse cursor visible. That's not cool. I'm checking that I actually see the mouse cursor all the time. I seem to be able to see it okay. Please let me know if I stuff something up like that. I would like to improve your feedback would be very valuable. Congratulations. What else do we have? Hitch extraction, clean ECG signal, maybe type classification. It's not really about type classification. But let's read our own requirements. We'll not really sweep this quickly. The ECG feature extractor is a prototype tool designed to analyze and label different components of an electrocardiogram ECG waveform with distinct colors, enhancing visual clarity and facilitating accurate interpretation. By splitting the ECG wave into T wave, QRS complex, NP wave, the extractor effectively segments and highlights each component, allowing clinicians and researchers to easily identify and analyze specific features. The color labeling provides a clear representation of the different ECG components aiding in the identification of abnormalities and regularities. Yet that description is okay. In addition to segment labeling, the ECG feature extractor provides valuable quantitative measurements for several important parameters. It calculates the mean values for crucial metrics including heart rate, PR interval, QRS time, QT interval, and isoelectric level. These measurements offer valuable insights into cardiac activity and provide a central information for diagnosing and monitoring heart conditions. By automating the extraction and analysis of ECG features, this tool could streamline the interpretation process, save time, and help help their professionals making important decisions based on accurate and standardized measurements. I am right. We have the file structure. Can we put all that north order? It is. We have the folder structure as inputs. Essentially, it will be something similar to this, but this will have each wave labeled with a different color. It is probably done with this. We don't want to display this anymore. I have a folder. Let's do this again. The tool complies with the relevant medical and ethical guidelines and regulations. Well, this is a good reason why this is synthetic ECG. Based on the information you provided, you are looking to create a Flask web application using Python that can process and analyze ECG signals. That's right. The users can input ECG signals and select a window size in the ECG signal type. Yep. The application will then output an analyzed ECG chart with different wave components color coded, along with other statistical metrics like heart rate mean, PR interval mean, etc. Yeah, that's right. We have the requirements file. Copy pasting is a skill, isn't it? Requirements. Yeah, we should have a New York hit NumPy Flask pandas modplotlib. We should have all of that already. This is mainly for reference. We'll deal with the Flask settings once we have something working. Yeah, New York hit. What was the sample application structure? Yes. So, it's the main Flask code. Handles the routes and the main functionality of the application. It will import the required libraries, load the ECG data, process it and send the output to the front end. That's nice. That's the script. Yes. Handles any client-side JavaScript for the application, for example, to make asynchronous requests to update the ECG chart dynamically. Bigger. Yeah, we know what CSS does. And the index HTML. Yeah, we would not have any. Let's close this for a sec in CSS. Let me actually copy this CSS. Should we copy everything from the previous project templates. My app, the WSGI file. We don't need that. Copy in features, extraction. So the data should be empty for now. It will remain empty. That's the old HTML file. This one doesn't work anymore. It's the one we are changing. We have JavaScript, CSS and the main app application. So we can use, we can start off this app application in Python code. Essentially, it will be a more advanced version of this. So this one, it's this tool here. So just generating synthetic signal. It's not doing any labeling. We want to do labeling. By the way, if you have any comments, suggestions, improvement, please do let me know. Just by the way, my YouTube analytics, if anyone is interested, I can talk more about that. You have provided a Flask application that receives ECG parameters from the client-side, generates a simulated ECG signal using the Narokid 2 library, and returns the generated ECG signal as a response. The parameters include BPM, window size, various types of noise, and ISO actor voltage. Here, I'll provide a brief review of your code and suggest any necessary improvements. I didn't mean to just ignore what I was asking about before. I didn't upload it. I did upload it. Let's do this again. Requirements. I should give it the JavaScript first. Let's do this with the JavaScript first. Your JavaScript code is for a web page that allows users to input parameters to generate a synthetic ECG signal using the Flask back-end. When users change any input field in the form, it makes a post-request to the Flask back-end, retrieves the generated ECG data, and uses Plotly to plot the ECG data on the web page. Give me some example improvement. Is that all about? Here's an example of how you can add a loading spinner to provide feedback to the user when the ECG data is being fetched. Assume you have a less than div8 equals loader, greater than less than div greater than in your HTML that contains a loading spinner, which is hidden by default. Now that's okay. I'll do it later. Mainly working on different applications. Given the provided information and requirements, I will focus on enhancing the app.py and script. JS files to include features for analyzing and labeling different components of the ECG waveform with distinct colors and to calculate mean values for crucial metrics. That's right. It's modifying the Python code. We will modify the class gap to process the ECG signals and extract features such as P-Wave, QRS complex, and T-Wave, then send these features to the front-end to be plotted. Doing everything in the same function. Okay, let's close all for a sec. We need to modify the JavaScript code to receive the extracted ECG features and plot them with different colors. Right. So using the same libraries, we have generate ECG as we had before. The code remains the same. We have the actual ECG generation over here. Okay, let's see. So it uncommented this. Didn't actually mention anything about it. Okay, it's extracting here, but it's not actually saying how it's done. I think this will actually work. I'm turning into all this into JSON file. I think this will work. Okay, the JavaScript should have update plot there already. Added all this stuff in constant data. Copy the whole constant data and you plot the CG plot data layout. This remains the same. ECG analysis. Ensure you have thoroughly understood the Narokid 2 library and how it processes and analyzes ECG signals. The extraction of P-Waves, QRS complexes, and T-Waves should be validated with actual ECG data to interact with ECG. Getting 500 error. Why? Failing to load resources. Failing to load. I really understood what's different in these errors here. Load them open. If six errors, this one says some problems, no longer generate consumers, it just puts service as an issue. Okay, obviously having trouble with the HTML in a template might well pop the styling. Let's put this in. Expect some variables to have different names or something like that. The HTML code you provided is a template for a web page that uses a form to gather parameters for generating an ECG waveform. It then passes these parameters to a backend service which returns the ECG data to be plotted on the page using Clotli. The form includes various controls for manipulating the ECG waveform including sliders for BPM, window size, and different types of noise. Your HTML is well-structured and you've made effective use of semantic HTML elements like less than header greater than, less than nav greater than, and less than form greater than. The inclusion of open graph meta tags at the top of your document is a nice touch for enhancing the shareability of your page. Your web page is well-designed with interactive elements for generating custom ECG waveforms. Enhancements in code cleanliness, accessibility, CO, and user feedback can make it more user-friendly, accessible, and discoverable online. Yeah, that's fine. That's not what we are worrying about. Yeah, the demo length is too small. Right, I've seen this error before. What's the problem? Can you solve this one for us? Please? Generating two responses. I want the one that I can read out loud. It's pretty much the same, pretty much the difference. Suggesting logging stuff. Unlike when it says final thoughts as if it's finished working. Pretty sure I have all the packages installed. I think it is the syntax error. Pretty sure we are printing the error. Oops, sprint data. The length is too small. 25 seconds seem to do the processing. Still get 500 for the generate ECG. That's not cool. Yeah, I'm pretty sure we don't actually have the ECGP wave. At least not yet. I'm not sure why it's even trying to plot it. I'm not sure if Neurokit can label individual waveforms. I don't think so. The main question. Does it even have an option to extract B wave? Pretty sure it doesn't. It says it does. This is a general approach for using Neurokit. ECG processing. Process the ECG signals using nk.ac underscore process which will return a DAF frame containing various process signals and a dictionary containing useful ECG features. Extracting waves. You can extract individual waves from the signals DAF frame which contains various processed and filtered versions of the ECG signal including the RPX. Visualizing the ECG signal you can use nk.ac underscore plot to visualize the process ECG signal which will display the RPX and if processed appropriately other waveforms as well. It's a nice code. This actually works. Oops. It seems I made a mistake in the previous example. The nk.ac underscore plot function does not take a sampling underscore rate argument. You should be able to plot the ECG signals and RPX just by passing the signals DAF frame dead. This poses some problems with the pandas library. This error is likely occurring because of changes in the pandas library where multidimensional indexing is no longer supported. It usually arises when you are trying to index a pandas DAF frame or series in a way that would return a multidimensional object. You know the source. It appears the error is emanating from the internal workings of the Neurokit to library particularly when it tries to plot the ECG signals and is not something directly related to your code. This could likely be due to a version mismatch or some changes in pandas or other dependencies that the Neurokit to relies on. This is not a recommended approach as it involves changing the library's source code which could lead to more issues online. However, if you're in a hurry and need a quick fix, you can trace back to the file and line number indicated in the error message and make necessary adjustments such as converting the pandas object to an mp array before indexing. But this should be a last resort and done with caution. Wait for a touch. Okay, so be sure a Neurokit doesn't have an option to extract features. So I have to do it in a different way. If the error occurs during the plotting step and your main goal is to extract ECG features, you can bypass the planning for now and focus on extracting the features you need from the signals and info objects returned by nk.ac underscore process. Pre-show Neurokit doesn't have an option to extract individual waves. We might have to use a different approach. Apologies for the confusion. Yes, you might need to resort to a more manual approach to extract the individual waves if the library doesn't provide a straightforward way. You can do this by utilizing the indices of the rpx, ppx, tpx, etc, which can be computed from Neurokit to and then slice the ECG signal accordingly. Here's a basic example of how this could be achieved. Note that this approach involves estimating where the pnt waves are relative to the rpx, which might not be extremely accurate and may need further tuning or a more sophisticated approach depending on your specific needs. Right, so this is more manual. Yeah, Neurokit wouldn't have automated any functions extracting individual waveforms. So I have to do it with ECG, ECG simulation. I mean, we still want to keep this essentially what we want. What do we want? We have a ECG signal generator. What do we want? We want to label a different parts of the signal. When do we want it? We'll deal in 10 minutes. That's how long we have in this stream. Okay, so it's ignored. Everything I said in the beginning is actually not a flask application, just a still testing mode. See if this works. It's surprising it does. Okay, how did you do it? That was quick. Okay, there's a problem. Just extracting the qrss. You ideally want something similar to this video that we had. Where is the quality low? This is better. Yeah, one day p qrss t in separate colors. This is something else. It's only in this example, only qrss peak is being labeled in different colors. Instead, can we use all colors and label p wave separately q notch separately r peak in a different color r in a different color and s peak wave in a different color at a deal with the message text. Yes, certainly. You can achieve this by specifically segmenting each part of the ECG waveform p wave, qrss complex and t wave and plotting them in different colors. However, you would need the precise indices of these components and that might involve more sophisticated signal processing or machine learning methods to accurately identify each component. Here's a basic example of how this could be achieved with simplified assumptions. Yes, I'm just a basic illustrative example. And for real world applications, you would need more accurate methods to determine the exact boundaries of each wave component. That's why explanation of the code. We're using the rocket to get the r peaks. We then use these r peaks to estimate the locations of the p waves and t waves. Yeah, this is a rough estimate and should be refined for real world data. We plug these separate segments in different colors. The by underscore label dictionary is used to eliminate duplicate labels and legend. Remember, the accuracy of this approach depends highly on the precision of the p q r s and t wave detection and this code provides rough estimations. You may need more advanced algorithms or models for precise and reliable wave delineation, especially for real world noisy ECG data. This is more like get the labeling off the labeling is a bit off. I understand your concern. The labeling of ECG components can be refined by identifying specific points for p q r s and t waves individually. However, this would require a more advanced approach than the simple estimation used in the previous example. Here's a more detailed example using artificial indices to label each component individually. Please note that these are still estimates and in a real world application, you would need to employ advanced signal processing techniques or machine learning models to accurately identify these components. In this revised code, I've adjusted the indices for segmenting the ECG into p q r s and t waves individually, and each of them is colored differently. I've increased the total window around each our peak to accommodate individual labeling of all five components. I've made sure to make the indices relative to each our peak so dynamically adjusts based on the position of each our peak. Remember to replace these estimated indices with the actual indices of each wave component for precise labeling. This can be achieved using more advanced signal processing methods or machine learning models trained for ECG signal delineation. This last labeling is a bit messy. Can we assume that so we're finding our peaks? Can we then just assume that the peak beforehand is q and the peak after that is s and I json to the our peak, you have q and s. It's by the way not quite right, but certainly we can simplify the labeling with these assumptions. Here's the updated code to reflect those changes. Okay, we have like four minutes to turn it into a flask application, publish. Can we do that? In this version, the segment just before each our peak is assumed to be the q wave and the segment just after is assumed to be the s wave. The p wave is assumed to be before the q wave and the t wave is assumed to be after the s wave. Please keep in mind, these are simplifications and for a real world application, you'd likely need a more sophisticated approach to identify these components accurately, especially in the presence of noise or arrhythmias. Also, the current code will plot the s wave and the r wave with the same color because they are in the same range, you may need to adjust the indices accordingly based on your specific requirements. Yeah, the previous one was better wasn't the previous code was producing better results. Can we keep the r peak label as a dot? Once we have that, can we assume that the negative peaks on the left and right are q and s? Can we also assume that before q, we have a positive t wave? Can we assume that after t negative peak, we have a positive t wave? Certainly. We can improve the labeling by marking our peaks with dots and making more important assumptions about the positions of p, q, s and t waves. We will search for negative peaks around the r peaks to identify q and s waves and assume the positive peaks before q and after s waves are added as p and t waves respectively. Here's an improved version of the code. In this improved code, r peaks are marked with red dots. q and s waves are identified as the negative peaks before and after the r peaks, respectively, within a certain window. p waves are assumed to be the positive peaks before the q waves. t waves are assumed to be the positive peaks after the s waves. The labels are added as dots to make the graph less cluttered. This approach should give you more accurate labeling of the ECG components. Make sure to adjust the window sizes and thresholds according to the specific characteristics of the ECG signals you are working with. Apologies for the confusion. It looks like the distance underscore min parameter isn't available in the version of Narokid 2 you are using. We can still proceed by just using the height underscore min parameter to filter out the smaller peaks or we can implement an alternative method to find the peaks. Here's the corrected code. I removed the distance underscore min parameter and kept the height underscore min parameter to filter out the peaks based on their heights. This should help to avoid smaller peaks that are too close to the r peaks while still identifying the q, s, p and t waves effectively. e wave and t wave are mislabeled. They should be local maximans before q and after s. Can you fix this? Certainly. I apologize for the oversight. I'll fix the code to correctly identify the p and t waves as the local maximans before q and after s respectively. Here's the revised code. In this revised code, I've added a check to ensure that the p and t waves are positive peaks. The p wave is identified as the highest positive peak before the q wave within a given window. The t wave is identified as the highest positive peak after the s wave within a given window. These modifications should result in more accurate labeling of the p and t waves in the ECG signal. Adjust the window sizes and thresholds as needed for your specific ECG signals. Not messing it up. Why? This number is potentially too small. Okay, let me know if there's any suggestions or anything I can improve. What am I doing wrong? Over one hour, there's nothing in the chat. Take a break and see you in a bit.