QRS complexes and T waves localization in multi-lead ECG signals based on deep learning and electrophysiology knowledge. (1st August 2022)

• Record Type: Journal Article
• Authors: Han, Chuang; Que, Wenge; Wang, Songwei; Zhang, Jinying; Zhao, Jie; Shi, Li
• Is Part Of: Expert systems with applications. Volume 199(2022)
• Publication Date: 2022-08-01
• Subjects: Localization -- U-Net -- CNN -- LSTM -- Dynamic threshold adaptive adjustment -- Electrophysiology knowledge; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117187
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Deep learning and DTAA method along with electrophysiology knowledge are presented. CNN and LSTM based on U-net can express spatial and temporal correlation features. DTAA method and electrophysiology knowledge reduced missed and false detections. The proposed method is more competitive than most state-of-the-art methods. Abstract: Accurate locations of key feature points for QRS complexes and T waves in electrocardiograms (ECGs) play a vital role in the analysis of cardiovascular diseases, such as arrhythmia and myocardial infarction. Because accuracy is affected by specific morphological changes of QRS waves and ST-T segments, traditional methods of detecting these waves need to be improved, which depend on empirical parameters and lack of robustness. This paper described a novel QRS complex and T wave localization method based on a U-Net framework combining the convolution neural network and long short-term memory with ensemble learning for multi-lead ECG signals. Using multi-lead ECG as an input, the model extracted strong temporal correlation features and fine morphological features in the training process, and its output was the probability value for every sample point. Dynamic threshold adaptive adjustment strategies during decision-making were then used to locate QRS complexes and T waves. Based on electrophysiology knowledge (EK), this method considered each lead information and was used to reduce missed and false detections. The proposed method based on deep learning and EK was also validated by three public databases. The experimental results indicated that the proposed method achieved a comparable or even better performance, when compared with present state-of-art methods. The currently proposed method produced results with high sensitivity and positive predictive values ranging from 99.45% to 100% for the locations of QRS complex waves, and from 99.99% to 97.88% for the locations of T waves from three datasets. Moreover, definitive analyses for missed and false detections of ECG signals were also used. These signals contained specific QRS complexes and T waves, such as higher amplitude T waves and QRS complexes, which contained fine structures, inverted U waves behind T waves, and waves along with atrial fibrillation and ventricular premature events.