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 matplotlib matplotlib-pyplot numpy pandas pyplot python random-forest scipy
Last synced: 3 months ago
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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 (7 months ago)
- Default Branch: main
- Last Pushed: 2024-12-12T22:53:38.000Z (7 months ago)
- Last Synced: 2025-02-08T01:51:29.861Z (5 months ago)
- Topics: csv, csv-files, data-science, decision-trees, ecg, ecg-classification, ecg-signal, jupyter, jupyter-notebook, knearest-neighbors, knn, machine-learning, matplotlib, matplotlib-pyplot, numpy, pandas, pyplot, python, random-forest, scipy
- Language: Jupyter Notebook
- Homepage:
- Size: 2.91 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 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.