Fuzz-ClustNet: Coupled fuzzy clustering and deep neural networks for Arrhythmia detection from ECG signals. (February 2023)

• Record Type: Journal Article
• Authors: Kumar, Sanjay; Mallik, Abhishek; Kumar, Akshi; Ser, Javier Del; Yang, Guang
• Is Part Of: Computers in biology and medicine. Volume 153(2023)
• Publication Date: 2023-02
• Subjects: Arrhythmia detection -- Convolutional Neural Network -- Deep learning -- Electrocardiogram (ECG) -- Feature extraction -- Fuzzy clustering; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106511
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Electrocardiogram (ECG) is a widely used technique to diagnose cardiovascular diseases. It is a non-invasive technique that represents the cyclic contraction and relaxation of heart muscles. ECG can be used to detect abnormal heart motions, heart attacks, heart diseases, or enlarged hearts by measuring the heart's electrical activity. Over the past few years, various works have been done in the field of studying and analyzing the ECG signals to detect heart diseases. In this work, we propose a deep learning and fuzzy clustering (Fuzz-ClustNet) based approach for Arrhythmia detection from ECG signals. We started by denoising the collected ECG signals to remove errors like baseline drift, power line interference, motion noise, etc. The denoised ECG signals are then segmented to have an increased focus on the ECG signals. We then perform data augmentation on the segmented images to counter the effects of the class imbalance. The augmented images are then passed through a CNN feature extractor. The extracted features are then passed to a fuzzy clustering algorithm to classify the ECG signals for their respective cardio diseases. We ran intensive simulations on two benchmarked datasets and evaluated various performance metrics. The performance of our proposed algorithm was compared with several recently proposed algorithms for heart disease detection from ECG signals. The obtained results demonstrate the efficacy of our proposed approach as compared to other contemporary algorithms. Highlights: We propose deep learning and fuzzy clustering for arrhythmia detection from ECG. We ran intensive simulations on two benchmarked datasets using various metrics. The performance was compared with several SOTA of heart disease detection from ECG. The obtained results demonstrate the efficacy of our proposed approach.