QRS Complex Detection Using Novel Deep Learning Neural Networks

Wenjie Cai,Danqin Hu

doi:10.1109/access.2020.2997473

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.

Highlights

Cardiovascular diseases (CVD) are the leading cause of death globally, taking around 17.8 million lives each year [1]
The aim of this study is to propose a noise-resistant deep learning method that reaches cutting edge performance for QRS complex detection and generalizes well in different ECG databases
These spikes are only existed in CPSCDB and the spike removal algorithm makes no change to the recordings in other three databases

Summary

Introduction

Cardiovascular diseases (CVD) are the leading cause of death globally, taking around 17.8 million lives each year [1]. Electrocardiogram (ECG) is the most widely used diagnostic tool for CVD. It is performed, noninvasive and can give immediate information. With the development of wearable devices, more and more ECGs are generated for analysis. Automated diagnostic methods are required to process ECGs generated by wearable devices and to reduce doctors’ workload. Many diagnostic methods are based on accurate QRS complex detection. QRS complexes serve as the beat positions and provide information about rhythm and intraventricular conduction. They are the most prominent parts of the ECG and can be identified by human eyes. A lot of algorithms have been developed to automatically detect QRS complex since several decades ago.

Methods

Results

Conclusion