Multimodal Time-Frequency Pseudo Anomalies for Atrial Fibrillation Anomaly Detection.

Atrial fibrillation anomaly detection is increasingly significant today as the incidence of cardiovascular disease continues to rise. However, most of the existing supervised learning based methods for computer-aided diagnosis of atrial fibrillation heavily rely on labeled data, which is not applicable because of the scarcity of atrial fibrillation ECG data. While unsupervised methods training solely with normal samples may result in blurred decision boundaries and inadequate discriminability. In this paper, we propose a method for atrial fibrillation anomaly detection based on multimodal time-frequency pseudo anomalies, which learns pseudo anomalies rectified time-frequency hypersphere under better ECG representations. Specifically, we propose an atrial fibrillation ECG generation method that considers the rhythm and wave characteristics to construct pseudo anomalies ECG signals. These pseudo anomalies signals are then used to optimize the time-frequency hypersphere boundary, which is learned from the features of normal ECG signals in both time and frequency domains, leading to more effective atrial fibrillation anomaly detection. Extensive experiments have been conducted on multiple ECG datasets to validate the effectiveness of the proposed method.