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Open AccessArticle

Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias

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Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
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ITI/Larsys/Madeira Interactive Technologies Institute, 9020-105 Funchal, Portugal
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Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology, Khulna 9208, Bangladesh
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Department of Electrical and Electronic Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
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School of Engineering, Deakin University, Geelong, VIC 3216, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Manuel Blanco-Velasco
Sensors 2021, 21(5), 1638; https://doi.org/10.3390/s21051638
Received: 12 January 2021 / Revised: 16 February 2021 / Accepted: 18 February 2021 / Published: 26 February 2021
(This article belongs to the Section Biomedical Sensors)
The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and amplitudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients (>0.98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz sampling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used. View Full-Text
Keywords: ECG modeling; ECG generator; Gaussian function; optimization; data compression ECG modeling; ECG generator; Gaussian function; optimization; data compression
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MDPI and ACS Style

Awal, M.A.; Mostafa, S.S.; Ahmad, M.; Alahe, M.A.; Rashid, M.A.; Kouzani, A.Z.; Mahmud, M.A.P. Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias. Sensors 2021, 21, 1638. https://doi.org/10.3390/s21051638

AMA Style

Awal MA, Mostafa SS, Ahmad M, Alahe MA, Rashid MA, Kouzani AZ, Mahmud MAP. Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias. Sensors. 2021; 21(5):1638. https://doi.org/10.3390/s21051638

Chicago/Turabian Style

Awal, Md. A.; Mostafa, Sheikh S.; Ahmad, Mohiuddin; Alahe, Mohammad A.; Rashid, Mohd A.; Kouzani, Abbas Z.; Mahmud, M. A.P. 2021. "Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias" Sensors 21, no. 5: 1638. https://doi.org/10.3390/s21051638

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