This is the implementation for the BeatGAN model architecture described in the paper: "BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series".
In this paper, we propose an unsupervised anomaly detection algorithm for time series data. BeatGAN has the following advantages: 1) Unsupervised: it is applicable even when labels are unavailable; 2) Effectiveness: It outperforms baselines in both accuracy and inference speed, achieving accuracy of nearly 0.95 AUC on ECG data and very fast inference (2.6 ms per beat); 3) Explainability: It pinpoints the time ticks involved in the anomalous patterns, providing interpretable output for visualization and attention routing; 4) Generality: BeatGAN also successfully detects unusual moions in multivariate motion-capture database.
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For full MIT-BIH dataset, download from
https://www.dropbox.com/sh/b17k2pb83obbrkn/AADzJigiIrottyTOyvAEU1LOa?dl=0 （contain preprocessed data） and place them in experiments/ecg/dataset/preprocessed/ -
For motion capture dataset, in experiments/mocap/dataset
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For ecg full experiemnt (need to download full dataset)
sh run_ecg.sh -
For ecg demo (there are demo data in experiments/ecg/dataset/demo, the output dir is in experiments/ecg/output/beatgan/ecg/demo )
sh run_ecg_demo.sh -
For motion experiment
sh run_mocap.sh
- Python 3
- PyTorch (1.0.0)
- scikit-learn (0.20.0)
- biosppy (0.6.1) # For data preprocess
- tqdm (4.28.1)
- matplotlib (3.0.2)
If you find this code useful in your research, please, consider citing our paper:
@inproceedings{zhou2019beatgan,
title={BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series},
author={Zhou, Bin and Liu, Shenghua and Bryan Hooi and Cheng, Xueqi and Ye, Jing },
booktitle={International Joint Conference on Artificial Intelligence},
year={2019},
}