Comprehensive analysis on intelligent deep learning based approaches for heart beat detection

SAAD ALMUTAIRI; MANIMURUGAN S; MAJED MOHAMMED ABOROKBAH; NARMATHA C; SUBRAMANIAM GANESAN; RIYADH A ALZAHEB; HANI ALMOAMARI

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Comprehensive analysis on intelligent deep learning based approaches for heart beat detection

Author(s)

SAAD ALMUTAIRI Industrial Innovation & Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk City, Kingdom of Saudi Arabia.

MANIMURUGAN S Industrial Innovation & Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk City, Kingdom of Saudi Arabia.

MAJED MOHAMMED ABOROKBAH Industrial Innovation & Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk City, Kingdom of Saudi Arabia.

NARMATHA C Industrial Innovation & Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk City, Kingdom of Saudi Arabia.

SUBRAMANIAM GANESAN Department of Electrical and Computer Engineering, Oakland university, Rochester, United State.

RIYADH A ALZAHEB Faculty of Applied Medical Sciences, University of Tabuk, Tabuk City, Kingdom of Saudi Arabia.

HANI ALMOAMARI Faculty of Computer and Information Systems, Islamic University of Madinah, Kingdom of Saudi Arabia.

Abstract

In recent years, the research study on heartbeat detection has been increased, which is more essential in medical and sports-related applications. These analyses help to find most heart disorders by examining the electrical signal of the heartbeat that produced with distinct unique cardiac tissues located in the heart of the body. Recently, numerous works have been developed to generate class labels based on automatic heartbeat classification techniques. More importantly, Deep Learning (DL) approaches used in recent times to optimize the functionality of traditional heartbeat methodologies. With this motivation, this study analyses the DL methods of ECG-based automatic heartbeat abnormalities detection through analyzing the ECG signal pre-processing to improve the quality, heartbeat segmentation techniques to identify the target region, feature extraction methods to reduce complexity of classifier by reducing the number of resources, and different DL based classification algorithms to generate class label for identifying the heartbeat. Finally, this analysis focus on the difficulties that DL models encounter and suggest some potential future directions. The results observed from various studies clearly show that classification performance improves even when using datasets with limited sample size. This study suggests that further attention should be paid to enhancing the generalizability of DL models used to analyse ECG signals, particularly by extracting more significant sample datasets.

Publication Details

Page(s) 5317-5335

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 14, Year: 2022

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Keywords

Cardiovascular Diseases and Deep Learning, -Heartbeat Measurement, Electrocardiogram Signals

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