ECG Signals Segmentation Using Deep Spatiotemporal Feature Fusion U-Net for QRS Complexes and R-Peak Detection

To detect and identify QRS complexes and R-peak is one of the crucial steps in the field of electrocardiogram (ECG) signals research, and their detection accuracy directly affects the performance of the subsequent ECG signal processing and analysis. However, the task involving the detection and identification of the ECG features becomes so complex for the traditional threshold-based detection methods. At present, researchers turn to deep learning (DL) approaches, but little work has been done on the detection of spatiotemporal features of the QRS complexes. In this article, for better QRS complexes and R-peak detection, we propose a novel method based on the improved U-net model, called ST-Res U-net. This uniquely designed component of the improved U-net model is made up of four levels of ST block (the block extracting spatial-temporal features) and Res Path (the residual path). The entire framework contains three steps: data preprocessing dealing with denoising of the raw ECG signals, the key component ST-Res U-net dealing with the spatiotemporal feature extraction, and a threshold screening algorithm for locating the R-peaks. Our experiments are purposely designed to test various combination structures of the ST blocks. The training and testing are from the MIT-BIH arrhythmia database (MITDB) and the CPSC2019 database. We adopt a commonly used set of evaluation criteria with the following experimental results: 99.76% and 90.01% for sensitivity, 99.87% and 93.5% for positive predictivity rate, 99.81% and 91.75% for F1 value, and 0.37% and 15.24% for detection error rate (DER). The former numbers are for MITDB and the latter CPSC2019. Furthermore, we test the proposed method on the PTB-XL database (without labeled R-peak positions), and the method can accurately locate QRS complexes and R-peaks The experimental results demonstrate that the proposed model and method is quite effective for the automatic classification and annotation of ECG signals and thus greatly improves the accuracy of diagnosis of arrhythmia diseases.