#🔒 ML PROJECT HELP

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dense pine
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12 Lead ECG diagnosis project. (I am stuck and I do not know how to finish it)

The code: https://paste.pythondiscord.com/2VYQ

This is my university project. The objective is to use this dataset and build a model that can predict heart diseases.

Dataset: A large dataset of ECG recordings in .mat files.

Preprocessing: I have done some preprocessing. Baseline correction, artifact removal, High-pass filtering, Low-pass filtering, Bandpass filtering.

I've implemented a peak detection algorithm based on the Pan-Tompkins method to detect R peaks in the ECG signal. The peak detection algorithm involves bandpass filtering, derivative calculation, squaring, moving-window integration, and peak detection based on fiducial marks.

I do not know what to do next. It is just my bachelor's degree and I am no expert. The signal processing is hard and the code is hard. I feel very frustrated because I am doing it on my own and I only have 8 days left until the presentation.

If anyone has any experience in such projects, please give me some guidance. Thank you!

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static sedgeBOT
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@dense pine

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twin wagon
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I don't have first-hand experience with this, but often the way you translate a graph like this (or any image) into an input for your model is to turn the graphs/images into numbers. For example, if you were working with cancer, you could extract the area, volume, circumference, smoothness of the edge, signal variability within the tumour etc. which gives you a bunch of numbers that you can feed into the model and then train your model. In your case it would be a good idea to look up a paper that implements an ML model with ECG data and see what sort of parameters they've extracted from the data. Perhaps average peak height, peak separation, peak variability, essentially what is it that doctors normally look for when diagnosing heart diseases from ECG graphs, turn that into numbers and then label the datasets by whether the person has a heart disease and that's your prediction model. I assume your ECG data is accompanied by whether that data corresponds to a healthy person or someone with a heart disease

dense pine
twin wagon
dense pine
static sedgeBOT
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