DAEAC

This is an implementation of paper Inter-patient ECG Arrhythmia Heartbeat Classification Based on Unsupervised Domain Adaptation.

Train & Test

In this implementation, we use .yaml files for configuration. Run the following codes for training and testing:

CUDA_VISIBLE_DEVICES=$1 python train.py --config=${CONFIG_FILE}

CUDA_VISIBLE_DEVICES=$1 python eval.py --config=${CONFIG_FILE}

Data Preprocessing

We use three datasets in this work, including MIT-BIH, INCARTDB and SVDB. The users can download the original datasets in the above websits.

All original records are converted into .mat format with src/data/preprocessing.py, and the indices of heartbeats are saved as .npz files.