https://github.com/drawcodeboy/srl-ae
Sparse Residual LSTM Autoencoder | Robust Autoencoder for Anomaly Detection in ECG | 2024 대한전자공학회 추계학술대회 | Autumn Annual Conference of IEIE, 2024 | OMS 2
https://github.com/drawcodeboy/srl-ae
anomaly-detection autoencoder ecg lstm-autoencoder robust robust-autoencoder srl-ae
Last synced: 11 months ago
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Sparse Residual LSTM Autoencoder | Robust Autoencoder for Anomaly Detection in ECG | 2024 대한전자공학회 추계학술대회 | Autumn Annual Conference of IEIE, 2024 | OMS 2
- Host: GitHub
- URL: https://github.com/drawcodeboy/srl-ae
- Owner: drawcodeboy
- Created: 2024-07-17T11:14:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-30T08:01:20.000Z (about 1 year ago)
- Last Synced: 2024-12-30T08:34:07.434Z (about 1 year ago)
- Topics: anomaly-detection, autoencoder, ecg, lstm-autoencoder, robust, robust-autoencoder, srl-ae
- Language: Python
- Homepage:
- Size: 3.85 MB
- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# SRL-AE
* SRL-AE (Sparse Residual LSTM Autoencoder)
* This study proposes a hybrid model, the Sparse Residual LSTM Autoencoder (SRL-AE), combining a Sparse Autoencoder and Residual LSTM to improve anomaly detection in electrocardiogram (ECG) data.
## 📝 Paper & Description
* Accepted, but not published yet. (Paper Link will be updated soon.)
* 📌 Paper Link
* 📌 Doby's Lab (Blog Description)
## 📝 Setting
```
# Clone this Repository
git clone https://github.com/drawcodeboy/SRL-AE.git
# Virtual Environment
python -m venv .venv
.venv\Scripts\activate # Window commands
# Install Packages
pip install -r requirements.txt
# You need to download the dataset(ECG5000), and place it under the data directory.
# <>
# train LSTM-AE (CPU), if you want train on GPU, use argument "--use-cuda"
python train.py --model=LSTM-AE
# test LSTM-AE (CPU)
python test.py --model=LSTM-AE --weights-filename=LSTM-AE_{epochs}.pth
# train SRL-AE (CPU)
python train.py --model=SRL-AE
# test SRL-AE (CPU)
python test.py --model=SRL-AE --weights-filename=SRL-AE_{epochs}.pth
```
## 📁 Dataset
* ECG5000 Dataset
## Experiment 1 (Residual LSTM)
* It is interpreted that the decoder intentionally makes reconstruction difficult through residual connections, so normal data can be easily reconstructed, while abnormal data becomes difficult to reconstruct.
| | Accuracy | F1-Score | Normal Loss Mean | Loss Gap |
| :---: | :---: | :---: | :---: | :---: |
| Both | 0.983 | 0.972 | 5.208 | 16.003 |
| Encoder | 0.954 | 0.923 | 6.980 | 15.914 |
| Decoder | 0.986 | 0.977 | 4.828 | 17.028 |
## Experiment 2 (Sparse Autoencoder)
* A Sparse Autoencoder was used in the encoder to effectively extract simple patterns from normal data through sparsity constraints. This was demonstrated by conducting a quantitative evaluation using t-SNE.
 | 
--- | --- |
## Experiments 3 (SRL-AE)
* To demonstrate the robustness of the SRL-AE model, experiments were conducted with four models. All models were trained using the same method, and their performance was evaluated. Compared to the conventional LSTM Autoencoder, the SRL-AE model showed slight differences in accuracy and F1-Score but demonstrated significant improvements in robustness metrics, thereby enhancing the reliability of the model's inference results.
| | Accuracy | F1-Score | Normal Loss Mean | Loss Gap |
| :---: | :---: | :---: | :---: | :---: |
| LSTM-AE | 0.986 | 0.978 | 5.240 | 15.096 |
| Residual LSTM-AE | 0.986 | 0.977 | 4.828 | 17.028 |
| Sparse LSTM-AE | 0.987 | 0.979 | 5.010 | 16.280 |
| SRL-AE (Ours) | 0.986 | 0.977 | 4.332 | 17.320 |
 | 
--- | --- |
 | 
## References
1. Hou, Borui, et al. "LSTM-based auto-encoder model for ECG arrhythmias classification." IEEE Transactions on Instrumentation and Measurement 69.4 (2019): 1232-1240.
2. Farady, Isack, et al. "ECG Anomaly Detection with LSTM-Autoencoder for Heartbeat Analy
sis." 2024 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2024.
3. Dutta, Koustav, et al. "MED-NET: a novel approach to ECG anomaly detection using LSTM auto-encoders." International Journal of Computer Applications in Technology 65.4 (2021): 343-357.
4. Matias, Pedro, et al. "Robust Anomaly Detection in Time Series through Variational AutoEncoders and a Local Similarity Score." Biosignals. 2021.
5. Alamr, Abrar, and Abdelmonim Artoli. "Unsu pervised transformer-based anomaly detection in ECG signals." Algorithms 16.3 (2023): 152.
6. ECG5000-Dataset, “http://timeseriesclassification.com/description.php?Dataset=ECG5000,“ Access Date: 2024/08/23.
7. Wei, Yuanyuan, et al. LSTM-autoencoder-based anomaly detection for indoor air quality time-ser ies data." IEEE Sensors Journal 23.4 (2023): 3787-3800.
8. Kim, Jaeyoung, Mostafa El-Khamy, and Jung won Lee. "Residual LSTM: Design of a deep recurrent architecture for distant speech recognition." arXiv preprint arXiv:1701.03360 (2017).
9. Ng, Andrew. "Sparse autoencoder." CS294A Lecture notes 72.2011 (2011): 1-19.