AFib Classifier from ECG signals using Spectrogram and Feature Based Approaches

The project consists of a different multiclassifier built following two approaches. The first one, the spectrogram approach, transforms signals into a spectrogram and predicts its class like it was an image. The second one relies on features of Ecgs.

How to run

Feature based approaches

• Feature signal preprocessing: python AFib.py features preprocessing

• Feature RNN training with default epochs: python AFib.py features train rnn

• Feature RNN training with given epochs: python AFib.py features train rnn 30

• Feature RNN evaluation with default weights: python AFib.py features evaluate rnn

• Feature RNN evaluation with default weights: python AFib.py features evaluate rnn ./weights.h5

• Feature CRNN training with default epochs: python AFib.py features train crnn

• Feature CRNN training with given epochs: python AFib.py features train crnn 30

• Feature CRNN evaluation with default weights: python AFib.py features evaluate crnn

• Feature CRNN evaluation with given weights: python AFib.py features evaluate crnn ./weights.h5

Spectrogram based approaches

• Spectrogram signal preprocessing: python AFib.py spectrogram preprocessing

• Spectrogram CNN training with default epochs: python AFib.py spectrogram train cnn

• Spectrogram CNN training with given epochs: python AFib.py spectrogram train cnn 30

• Spectrogram CNN evaluation with default weights: python AFib.py spectrogram evaluate cnn

• Spectrogram CNN evaluation with default weights: python AFib.py spectrogram evaluate cnn ./weights.h5

• Spectrogram CRNN training with default epochs: python AFib.py spectrogram train crnn

• Spectrogram CRNN training with given epochs: python AFib.py spectrogram train crnn 30

• Spectrogram CRNN evaluation with default weights: python AFib.py spectrogram evaluate crnn

• Spectrogram CRNN evaluation with given weights: python AFib.py spectrogram evaluate crnn ./weights.h5