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Repository Details
This project aims at predicting if a heart beat from a ECG signal has an arrhythmia for each 0.4 second window centered on the peak of the heart beat. In this context, different classifiers including Random Forest, Logistic Regression, K Nearest Neighbors, Neural Networks and Decision Tree are used to detect abnormal beats. We use the MIH-BIH Arrythmia dataset from https://physionet.org/content/mitdb/1.0.0/ which is made available under the ODC Attribution License. This is a dataset with 48 half-hour two-channel ECG recordings measured at 360 Hz from the 1970s.
