Short-time detection of QRS complexes using dual channels 1 based on U-Net and bidirectional long short-term memory

TL;DR

This paper introduces a novel dual-channel deep learning algorithm combining U-Net and bidirectional LSTM for accurate short-time QRS complex detection in ECG signals, demonstrating superior performance on MIT-BIH data.

Contribution

It presents a new algorithm integrating U-Net and bidirectional LSTM for improved short-time QRS detection in noisy ECG signals.

Findings

Achieved 99.56% sensitivity in QRS detection.

Attained 99.72% positive predictivity.

Reached 99.28% overall accuracy.

Abstract

Cardiovascular disease is associated with high rates of morbidity and mortality, and can be reflected by 19 abnormal features of electrocardiogram (ECG). Detecting changes in the QRS complexes in ECG 20 signals is regarded as a straightforward, noninvasive, inexpensive, and preliminary diagnosis approach 21 for evaluating the cardiac health of patients. Therefore, detecting QRS complexes in ECG signals must 22 be accurate over short times. However, the reliability of automatic QRS detection is restricted by all 23 kinds of noise and complex signal morphologies. In this study, we proposed a new algorithm for 24 short-time detection of QRS complexes using dual channels based on U-Net and bidirectional long 25 short-term memory. First, a proposed preprocessor with mean filtering and discrete wavelet transform 26 was initially applied to remove different types of noise. Next the signal was transformed and 27 annotations were relabeled. Finally, a method combining U-Net and bidirectional long short-term 28 memory with dual channels was used for the short-time detection of QRS complexes. The proposed 29 algorithm was trained and tested using 44 ECG records from the MIT-BIH arrhythmia database. It 30 achieved, on average, high values for detection sensitivity (99.56%), positive predictivity (99.72%), 31 and accuracy (99.28%) on the test set, indicating an improvement compared to algorithms reported in 32 the literature.