Self-Supervised Atrial Fibrillation Anomaly Detection Method Guided by Electrocardiogram Features
Electrocardiogram(ECG)anomaly detection aims to find abnormal patterns in ECG data,such as atrial fibrilla-tion features or invalid interference signal features.A simple and effective self-supervised anomaly detection method has been proposed for atrial fibrillation ECG disease,called electrocardiogram feature induced atrial fibrillation detector(EFAFD).In this method,the P-wave mask and heart rate variability(HRV)index are introduced through a multi-task learning strategy to guide the model in learning the medical characteristics such as P-wave disappearance and irregularity of RR intervals,so as to improve the model's ability to distinguish abnormal ECG patterns of atrial fibrillation.Specifically,the ECG data with P-wave masking are reconstructed using an autoencoder to capture the characteristic of P-wave disap-pearance in atrial fibrillation.Simultaneously,the prediction task of HRV indices is integrated into the autoencoder frame-work to learn the rhythmic features of atrial fibrillation electrocardiogram with absolute irregularity of RR intervals.Finally,atrial fibrillation ECG is detected by measuring the reconstruction error of the ECG.The proposed method is evaluated on real dynamic ECG datasets,including the CPSC2021 dataset and the Icentia11k dataset.The AUC of EFAFD reaches 81.85%on the CPSC2021 dataset and 92.46%on the Icentia11k dataset,and experimental results demonstrate its superiority over existing methods in atrial fibrillation anomaly detection.
万方数据
维普
