Deep Learning-Based Signal Quality Assessment for Wearable ECGs
Xiangyu Zhang, Jianqing Li, Zhipeng Cai, Lina Zhao, Chengyu Liu
Abstract
Nowadays, use of the dynamic electrocardiogram (ECG) has developed rapidly because of the wide application of wearable devices [1]–[3]. Most ECG-based diagnostic algorithms require that the ECG signal have a clear waveform and accurate feature points. However, the collected wearable ECG signal usually contains a certain amount of noise and causes many false alarms in the ECG analysis system [4], [5]. Thus, signal quality assessment (SQA) plays a prominent role in ruling out the ECG segments with poor signal quality [6]. Compared with traditional static ECG signals, dynamic wearable ECGs contain more noise, which brings greater challenges to disease detection algorithms [7]–[9]. These artifacts and noises in dynamic ECG signals can seriously affect the R-peaks detection, ECG beat extraction, ECG morphological feature extraction and the detection of noise peaks, resulting in frequent false alarms [10]. In 2008, Li et al. [11] proposed the bSQI signal quality indexes: comparison of two beat detectors on a single ECG lead. Liu et al. [12] generalized the two QRS wave complex (QRS) detectors-based bSQI to multiple QRS detectors-based bSQI (GbSQI) to improve the SQA performance. Liu et al. [8] proposed an efficient real-time SQA method for healthy subjects.