Arrhythmia disease classification utilizing ResRNN. (January 2023)

• Record Type: Journal Article
• Authors: Dhyani, Shikha; Kumar, Adesh; Choudhury, Sushabhan
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 2
• Publication Date: 2023-01
• Subjects: RR interval -- Arrhythmia detection -- Atrial fibrillation -- Deep learning -- Heart rate -- Residual neural network; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104160
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: In this article, the CPSC 2018 dataset was used to train a ResRNN model. To classify the diseases into nine categories: normal, Arrhythmia (AF), I-atrioventricular block (AVB), left bundle branch block (LBBB), right bundle branch block (RBBB), premature atrial contraction (PAC), premature ventricular contractions (PVC), ST-segment depression (STD), ST-segment elevation (STSE) /(STE). The ResRNN outperforms in identifying eight types of anomalies in 12-lead ECG data. When level 4 rough coefficients with the Symlet-8 (Sym8) channel were used for organization, the best degree of exactness was obtained. The ResRNN classifier has a normal accuracy of 91 percent when using ECG signals, which is much higher than the deep multiscale fusion neural network (DesNet) at 83 % and inception ResNet V2 at 80 %. Abstract: Automated electrocardiogram (ECG) analysis cannot be employed in clinical practice due to the accuracy of the present models. Deep Neural Networks (DNNs) are models made up of stacking transformations that learn tasks by example. This technology has lately demonstrated remarkable performance in several activities, and its potential to improve clinical practice is highly anticipated. In this article, the China physiological signal challenge (CPSC)- 2018 dataset was used to train a ResRNN model. The ResRNN outperforms in identifying eight types of anomalies in 12-lead ECG data. When level 4 rough coefficients with the Symlet-8 (Sym8) channel were used for organization, the best degree of exactness was obtained. The ResRNN classifier has a normal accuracy of 91% when using ECG signals, which is much higher than the deep multiscale fusion neural network (DesNet) at 83 % and Inception ResNet V2 at 80 %. As a result, the given methodology is far better than others. After preprocessing the signals, the ResRNN model classifies the diseases into nine categories: normal, Atrial fibrillation (AF), first degree-atrioventricular block (AVB), left bundle branch block (LBBB), right bundle branch block (RBBB), premature atrial contraction (PAC), premature ventricular contractions (PVC), ST-segment depression (STD), and ST-segment elevation (STSE) /(STE).