QRS complex detection using novel deep learning neural networks

Abstract

Objective: Accurate QRS complex detection is essential for electrocardiography (ECG) diagnosis. Many proposed algorithms don't perform satisfactorily on noisy and arrhythmia ECGs. The purpose of this study is to develop a noise resistant and generalizable method to detect QRS complexes accurately. Methods: Two deep learning models based on multi-dilated convolutional blocks are proposed. One model (CNN) is mainly composed of convolutional blocks and Squeeze-and-Excitation networks (SENet). The other model (CRNN) contains a hybrid convolutional and recurrent neural network. With 5-fold cross-validation approach the models are trained and tested on four open-access ECG databases: the China Physiological Signal Challenge (2019) database (CPSCDB), the MIT-BIH Noise Stress Test Database (NSTDB), the MIT-BIH Arrhythmia Database (MITDB) and the QT Database (QTDB). Results: The F1 score of CNN model on CPSCDB, NSTDB, MITDB and QTDB are 0.9929, 0.9892, 0.9994 and 0.9998 respectively. The F1 score of CRNN model on these four databases are 0.9947, 0.9953, 0.9995 and 0.9998 respectively. The ensemble of both models scored the first place in the China Physiological Signal Challenge (2019). Conclusion: The proposed models achieve state-of-the-art performance in QRS complex detection and show good generalization on different databases. This work might help make better ECG diagnosis.