https://github.com/winterwind/ecg_signal_classification

Two-part project that involves detecting the R-peaks in an ECG signal to extract the individual ECG beats and making a machine learning model to classify them
https://github.com/winterwind/ecg_signal_classification

csv csv-files data-science decision-trees ecg ecg-classification ecg-signal jupyter jupyter-notebook knearest-neighbors knn machine-learning python random-forest

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Two-part project that involves detecting the R-peaks in an ECG signal to extract the individual ECG beats and making a machine learning model to classify them

• Host: GitHub
• URL: https://github.com/winterwind/ecg_signal_classification
• Owner: Winterwind
• Created: 2024-12-12T22:38:32.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-12T22:53:38.000Z (about 2 months ago)
• Last Synced: 2024-12-12T23:29:55.237Z (about 2 months ago)
• Topics: csv, csv-files, data-science, decision-trees, ecg, ecg-classification, ecg-signal, jupyter, jupyter-notebook, knearest-neighbors, knn, machine-learning, python, random-forest
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# ECG_Signal_Classification

Two-part project that involves detecting the R-peaks in an ECG signal to extract the individual ECG beats and making a machine learning model to classify them. Both extract data from .csv files and can be found in their respective folders.

## Part 1

Here, I parse data from a .csv file, discard incomplete rows with missing information, plot the data, and isolate the R-peaks

## Part 2

Here, I parse data from multiple .csv files containing different dimensions of the data. I then apply three classifiers: KNearestNeighbors, DecisionTree, and RandomForest; and then compare and contrast the results.