Shannon entropy Morlet wavelet Transform (SEMWT) and Kernel Weight Convolutional Neural Network (KWCNN) classifier for arrhythmia in electrocardiogram recordings. (September 2022)

• Record Type: Journal Article
• Authors: Thirrunavukkarasu, R.R.; Meera Devi, T.
• Is Part Of: Biomedical signal processing and control. Volume 78(2022)
• Publication Date: 2022-09
• Subjects: Electrocardiogram (ECG) morphology -- Denoising -- Fuzzy Weight Beetle Swarm Optimization (FWBSO) -- Feature extraction -- Shannon Entropy Morlet Wavelet Transform (SEMWT) -- Kullback-Leibler Divergence Kernel Principal Component Analysis (KLDKPCA) -- Classification -- Kernel Weight Convolutional Neural Network (KWCNN); 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.2022.103992
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The Kernel Weight Convolutional Neural Network (KWCNN) works based on weight calculation from kernel function using CNN and to employ to classify ECG arrhythmia. Trivial features from a large number of features can be extracted using KLDKPCA. Instantaneous frequency, amplitude envelope, along with energy of the signal-generating system can be calculated using Teager Energy Operator(TEO). Abstract: The automatic detection and classification of life-threatening arrhythmia is life-threatening in treatment of a variety of cardiac diseases. Cardiac arrhythmias are irregular heartbeats that are either too fast (tachycardia) or too slow (bradycardia). Minor alteration in the morphology or dynamics of the Electrocardiogram (ECG) can induce severe arrhythmia events, that can decrease the heart's ability to pump blood and cause breathing difficulties, chest pain, tiredness, and loss of consciousness. A unique deep learning technique for classification of distinct types of arrhythmia utilizing feature extraction is provided in this research. To acquire morphological features, the Shannon Entropy Morlet Wavelet Transform (SEMWT) is done to every heart beat. This work uses Empirical Mode Decompositions (EMDs) with Fuzzy Weight Beetle Swarm Optimization (FWBSO) is introduced for signal noise removal. Then, utilising Kullback-Leibler Divergence Kernel Principal Component Analysis (KLDKPCA) and Dynamic Time Wrapping ECG segments are selected, whereas morphological features from P-QRS-T waves are extracted using SEMWT. SEMWT improves the time and frequency resolution of an ECG signal, making it easier to decode critical information. ECG signal was divided into low-frequency approximation and high-frequency detail components after applying SEMWT. Then, with high accuracy, a Kernel Weight Convolutional Neural Network (KWCNN)-based automated arrhythmia classification is constructed. This work's resulted are evaluated with performance metrics of Sensitivity (SEN), F-measure, Positive Predictivity (PP) and Accuracy (ACC). Over the whole MIT-BIH Arrhythmias Database, the suggested approach was tested. The suggested classification approach was first tested in MATLAB, with the results compared to those of other methods.