Cover
Vol. 20 No. 1 (2024)

Published: June 30, 2024

Pages: 95-103

Original Article

Recognition of Cardiac Arrhythmia using ECG Signals and Bio-inspired AWPSO Algorithms

Jyothirmai Digumarthi 1 V. M. Gayathri 2 R. Pitchai 3
1Department of Networking and Communications , Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India
2Department of Networking and Communications, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India
3Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Telagana, India
History
  • Received: May 20, 2023
  • Revised: July 12, 2023
  • Accepted: July 15, 2023
  • Available Online: November 25, 2023
DOI

https://doi.org/10.37917/ijeee.20.1.10

Abstract

Studies indicate cardiac arrhythmia is one of the leading causes of death in the world. The risk of a stroke may be reduced when an irregular and fast heart rate is diagnosed. Since it is non-invasive, electrocardiograms are often used to detect arrhythmias. Human data input may be error-prone and time-consuming because of these limitations. For early detection of heart rhythm problems, it is best to use deep learning models. In this paper, a hybrid bio-inspired algorithm has been proposed by combining whale optimization (WOA) with adaptive particle swarm optimization (APSO). The WOA is a recently developed meta-heuristic algorithm. APSO is used to increase convergence speed. When compared to conventional optimization methods, the two techniques work better together. MIT-BIH dataset has been utilized for training, testing and validating this model. The recall, accuracy, and specificity are used to measure efficiency of the proposed method. The efficiency of the proposed method is compared with state-of-art methods and produced 98.25 % of accuracy.

Keywords

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