# An Attention-Augmented VAE-BiLSTM Framework for Anomaly Detection in 12-Lead ECG Signals

arXiv: https://arxiv.org/abs/2510.05919 · HTML: https://mlsanity.com/paper/2510.05919/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 42 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2201.07284/) (2022)
- [Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals](https://mlsanity.com/paper/2107.12626/) (2021)
- [Pan-Tompkins++: A Robust Approach to Detect R-peaks in ECG Signals](https://mlsanity.com/paper/2211.03171/) (2022)
- [An Efficient Algorithm Based on Wavelet Transform to Reduce Powerline Noise From Electrocardiograms](https://mlsanity.com/paper/2401.10694/) (2024)
- [Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning](https://mlsanity.com/paper/2404.04935/) (2024)

## Built on by (most cited first, 1 of 1)

- [Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices](https://mlsanity.com/paper/2606.11556/) (2026)

## Abstract (arXiv)

Anomaly detection in 12-lead electrocardiograms (ECGs) is critical for identifying deviations associated with cardiovascular disease. This work presents a comparative analysis of three autoencoder-based architectures: convolutional autoencoder (CAE), variational autoencoder with bidirectional long short-term memory (VAE-BiLSTM), and VAE-BiLSTM with multi-head attention (VAE-BiLSTM-MHA), for unsupervised anomaly detection in ECGs. To the best of our knowledge, this study reports the first application of a VAE-BiLSTM-MHA architecture to ECG anomaly detection. All models are trained on normal ECG samples to reconstruct non-anomalous cardiac morphology and detect deviations indicative of disease. Using a unified preprocessing and evaluation pipeline on the public China Physiological Signal Challenge (CPSC) dataset, the attention-augmented VAE achieves the best performance, with an AUPRC of 0.81 and a recall of 0.85 on the held-out test set, outperforming the other architectures. To support clinical triage, this model is further integrated into an interactive dashboard that visualizes anomaly localization. In addition, a performance comparison with baseline models from the literature is provided.