Dengyong Zhang^1,2, Minzhi Yuan^1,2, Feng Li^1,2, Lebing Zhang^3,*, Yanqiang Sun⁴, Yiming Ling⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2809-2824, 2024, DOI:10.32604/cmes.2023.029181 - 15 December 2023

Abstract Electrocardiogram (ECG) signal is one of the noninvasive physiological measurement techniques commonly used in cardiac diagnosis. However, in real scenarios, the ECG signal is susceptible to various noise erosion, which affects the subsequent pathological analysis. Therefore, the effective removal of the noise from ECG signals has become a top priority in cardiac diagnostic research. Aiming at the problem of incomplete signal shape retention and low signal-to-noise ratio (SNR) after denoising, a novel ECG denoising network, named attention-based residual dense shrinkage network (ARDSN), is proposed in this paper. Firstly, the shallow ECG characteristics are extracted by More >

Muhammad Aleem Raza¹, Muhammad Anwar², Kashif Nisar³, Ag. Asri Ag. Ibrahim^3,*, Usman Ahmed Raza¹, Sadiq Ali Khan⁴, Fahad Ahmad⁵

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3817-3834, 2023, DOI:10.32604/cmc.2023.032275 - 26 December 2023

Abstract With the help of computer-aided diagnostic systems, cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease. However, the early diagnosis of cardiac arrhythmia is one of the most challenging tasks. The manual analysis of electrocardiogram (ECG) data with the help of the Holter monitor is challenging. Currently, the Convolutional Neural Network (CNN) is receiving considerable attention from researchers for automatically identifying ECG signals. This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute More >

Nimmala Mangathayaru^1,*, Padmaja Rani², Vinjamuri Janaki³, Kalyanapu Srinivas⁴, B. Mathura Bai¹, G. Sai Mohan¹, B. Lalith Bharadwaj¹

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2425-2443, 2021, DOI:10.32604/cmc.2021.016534 - 21 July 2021

Abstract Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine. Detecting arrhythmia from ECG signals is considered a standard approach and hence, automating this process would aid the diagnosis by providing fast, cost-efficient, and accurate solutions at scale. This is executed by extracting the definite properties from the individual patterns collected from Electrocardiography (ECG) signals causing arrhythmia. In this era of applied intelligence, automated detection and diagnostic solutions are widely used for their spontaneous and robust solutions. In this research, our contributions are two-fold. Firstly, the Dual-Tree Complex Wavelet… More >