I'm trying to implement the CNN code from Andreas Werdich: https://github.com/awerdich/physionet
" The goal of this project was to implement a deep-learning algorithm that classifies electrocardiogram (ECG) recordings from a single-channel handheld ECG device into four distinct categories: normal sinus rhythm (N), atrial fibrillation (A), other rhythm (O), or too noisy to be classified (~). "
Executing the code works fine. But now after the model was trained I'm not sure how to predict a different ECG signal. He uses ECG signal stored in hdf5 files.
"For each group of data in the hdf5 file representing a single ECG time series, the following metadata was saved as attribute:
- baseline voltage in uV
- bit depth
- gain
- sampling frequency
- measurement units"
After training I saved the model with
model.save(filepath) I put it on filedropper: http://www.filedropper.com/ecgcnn
And I have an hdf5 file full with ECG signals that I'd like to predict: http://www.filedropper.com/physioval
I tried using the model.predict function, but it didn't work. I'm not quite sure how to pass on the ECG signal, because I need 4 different classifications.
Does anyone know how I can make the prediction work?
Thanks
