A novel Transformer-based Anomaly Detection approach for ECG Monitoring Healthcare System

Anomaly detection plays a crucial role across various domains, including healthcare, where identifying deviations from normal patterns can help with early intervention and improved outcomes. In healthcare, such as in ECG analysis, detecting abnormal signals is essential for timely diagnosis and treatment, as it can help identify potentially life-threatening conditions that could otherwise go undetected.
In this work, by focusing on ECG anomaly detection as an illustrative healthcare application, we propose to use a transformer-based variational autoencoder network together with a MEWMA-SVDD control chart to achieve anomaly detection. By employing this approach, we can effectively control the false alarm rate to minimize unnecessary alerts. Our proposed framework not only excels in terms of accuracy but also reduces the false alarm rate, making it a favorable choice compared to existing methods.