This repo contains the code used in our ICSMD paper. The published version will be updated soon.
Our work is trying to make "machine doctors" act as the real doctors who not only make diagnosis, but also describe patients' symptoms. Our proposal algorithms will generate the saliency map to explain which parts of our input features contribute to the algorithms' decisions.
This folder contains the features and labels in a clean format, which will be used as the input data for the main notebook: DEFTCNN.
This folder contains codes for data preprocessing, feature extraction and the implementation of DEFTCNN.
Sample the cwru data.
Based on the sampled data, extract different domain features from the time series data. And the features extractions are applied on time windows.