Multiscale Laplacian graph kernel features combined with tree deep convolutional neural network for the detection of ECG arrhythmia. (July 2022)

• Record Type: Journal Article
• Authors: Ramkumar, M.; Lakshmi, A.; Pallikonda Rajasekaran, M.; Manjunathan, A.
• Is Part Of: Biomedical signal processing and control. Volume 76(2022)
• Publication Date: 2022-07
• Subjects: Atrial fibrillation -- Multiscale Laplacian graph kernel (MLJK) -- Tree deep convolutional neural network -- MIT-BIH AF database -- MIT-BIH arrhythmia database; 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.2022.103639
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: In this manuscript, MLGK-TDCNN is proposed for the detection of ECG arrhythmia. Here, the input ECG signals are taken from two datasets: (i) AFDB (ii) MBDB. These datasets are balanced using Improved fuzzy c-means method. Moreover, the de-noised ECG signals are given to the MLGK. Then, the MLGK features given to the TDCNN classifier for detecting AF and NSR. Abstract: In this manuscript, Multiscale Laplacian graph kernel features combined with Tree Deep Convolutional Neural Network (MLGK-TDCNN) is proposed for the detection of Electrocardiogram (ECG) arrhythmia. Here, the input ECG signals are taken from two datasets: (i) MIT-BIH AF database (AFDB) (ii) MIT-BIH arrhythmia database (MBDB). These datasets are fully unbalanced dataset, and these datasets are balanced using Improved fuzzy c-means method for unbalanced dataset. Moreover, the de-noised ECG signals are given to the MLGK. The proposed MLGK is to combine the Multiscale kernel features from the Preprocessed ECG signals. The combined Multiscale kernel features given to the TDCNN classifier for the detection of AF with raw normal sinus rhythm (NSR). The proposed approach is activated in MATLAB platform, then the efficiency is analyzed with existing approaches. The experimental outcomes demonstrate that the proposed FFREWT-MLGK-TDCNN approach is compared with two databases. From the analysis, the accuracy of AFDB shows 9.40%, 16.44% and 23.20% better than the existing approaches, the accuracy of MBDB shows 14.67%, 21.42% and 7.54% better than the existing approaches, like novel intelligent approach depending on multi-scale convolution kernel (MCK) and Squeeze-and-Excitation network (SENet) for AF detection, automatic arrhythmia classification strategy using the optimization-based deep convolutional neural network (CNN-BaROA), deep learning method for classifying arrhythmia by using 2-second segments of 2D recurrence plot images of ECG signals (2D-CNN) respectively.