Document Type : Full Research Paper
Authors
1 Faculty of Biomedical Engineering, Sahand Univercsity of Technology, Tabriz, Iran
2 Faculty of Biomedical Engineering/,Sahand University of Technology, Tabriz, Iran
Abstract
In recent years, model-based ECG processing algorithms have been successfully developed in various fileds of ECG processing. The calculation of ECG dynamic model (EDM) is a crucial step for these methods. The EDM parameters can be calculated using optimization algorithms. One of the popular optimization methods in this field is an offline nonlinear method in which users have to manually select points on ECG signal in order to calculate EDM parameters. The objective function used in this algorithm is a complex function which is hard to optimize. In this paper an automatic optimization algorithm is proposed which uses meta-heuristic optimization algorithms to calculate EDM parameters. In this algorithm, we don’t need to select points manually. In addition, the objective function in this algorithm is broken in to several simple objective functions which makes the optimization more accurate. Meta-heuristic optimization algorithms may perform successfully on some optimization problems while failing on others. As a result, a specific algorithm cannot be considered the best optimizer for all optimization problems. For this reason, in this paper, the performances of nine popular meta-heuristic algorithms such as particle swarm optimization, artificial bee colony, cucko search, etc are investigated. In this paper, 200 ECG segments from different records of the MIT-BIH Normal Sinus Rhythm Database (NSRDB) have been selected for evaluation. The duration of each segment was 30 seconds. The EDM parameters for each segment were calculated using the aforemetinoned optimization algorithms. For evaluation, the similarities between the original signals and the synthetic ECG signals were inspected for each optimization algorithm. These synthetic signals were created using the calculated EDM parameters. The similarity results showed that the water evaporation optimization (WEO), teaching learning-based optimization (TLBO), and cucko’s search (CS) algorithms achived better results compared with other methods.
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