SAPTSTA-AnoECG: a PatchTST-based ECG anomaly detection method with subtractive attention and data augmentation

Abstract

An electrocardiogram (ECG) is a crucial noninvasive medical diagnostic method that enables real-time monitoring of the electrical activity of the heart. ECGs hold a significant position in the rapid diagnosis and routine monitoring of cardiac diseases due to their user-friendly operation, prompt detection, broad range of diagnosable problems, and cost-effectiveness. However, thorough comprehension of ECG readings requires a high level of medical expertise due to the complex variations in ECG patterns, substantial interindividual differences, and numerous interfering factors. Consequently, current ECG machines and ECG Holters typically provide simplistic indications of ECG anomalies. Nonetheless, current ECG anomaly detection (EAD) algorithms lack precision; therefore, these medical devices cannot accurately report the specific types of diseases reflected in ECG results. In response to these challenges, this paper proposes enhancing the accuracy of electrocardiogram detection by improving algorithms. Therefore, we propose SAPTSTA-AnoECG, a PatchTST-based ECG anomaly detection method with subtractive attention and data augmentation. This method introduces a subtractive attention mechanism to make the Transformer architecture more suitable for time series data. We also use data augmentation to increase the robustness of the model. In addition, a patch-based approach is employed to reduce the algorithm’s computational complexity of the model. Furthermore, we introduce a new publicly available ECG dataset named HCE in this paper and conduct comparative experiments using this dataset along with the PTB-XL and CPSC 2018 datasets. The experimental results demonstrate the effectiveness of this method.