A novel P-QRS-T wave localization method in ECG signals based on hybrid neural networks. (November 2022)

• Record Type: Journal Article
• Authors: Liu, Jinlei; Jin, Yanrui; Liu, Yunqing; Li, Zhiyuan; Qin, Chengjin; Chen, Xiaojun; Zhao, Liqun; Liu, Chengliang
• Is Part Of: Computers in biology and medicine. Volume 150(2022)
• Publication Date: 2022-11
• Subjects: Electrocardiogram -- P-QRS-T wave localization -- Residual neural network -- Long short-term memory; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106110
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: As the number of people suffering from cardiovascular diseases increases every year, it becomes essential to have an accurate automatic electrocardiogram (ECG) diagnosis system. Researchers have adopted different methods, such as deep learning, to investigate arrhythmias classification. However, the importance of ECG waveform features is generally ignored when deep learning approaches are applied to classification tasks. P-wave, QRS-wave, and T-wave, containing plenty of physiological information, are three critical waves in the ECG heartbeat. The accurate localization of these critical ECG wave components is a prerequisite for ECG classification and diagnosis. In this study, a novel P-QRS-T wave localization method based on hybrid neural networks is proposed. The raw ECG signal is preprocessed sequentially by filtering, heartbeat extraction, and data standardization. The hybrid neural network is constructed by combining the residual neural network (ResNet) and the Long Short-Term Memory (LSTM). It predicts the relative positions of the P-peak, QRS-peak, and T-peak for each heartbeat. The proposed algorithm was validated on four ECG databases with input noise of different signal-to-noise ratio (SNR) levels. The results show that the proposed method can accurately predict the positions of the three key waves. The proposed P-QRS-T localization approach can improve the efficiency of ECG delineation. Integrated with cardiac disease classification methods, it can contribute to the development of advanced automatic ECG diagnosis systems. Highlights: Without complex feature extraction or feature selection stages, the proposed algorithm can be embedded in portable devices. The proposed method can be used as the initial step of ECG heartbeat classification and combined with other classifiers. This wave localization algorithm can also be applied to the 12-lead ECG, including abnormal waveforms. The method based on ResNet and LSTM is robust against noise and has achieved excellent performance in four ECG databases.