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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Review Article

Comparison of Soft Computing and Optimization Techniques in Classification of ECG Signal

Author(s): Prerak Mathur, Pooja Sharma and Karan Veer*

Volume 16, Issue 2, 2023

Published on: 28 September, 2022

Article ID: e040822207294 Pages: 7

DOI: 10.2174/2666255816666220804161549

Price: $65

Abstract

Electrocardiogram (ECG) is a visual representation of the heartbeat that can be used to detect cardiac problems. It helps in detection of a normal or abnormal state of heart diseases. Therefore, it is difficult to detect the cardiological status with naked eyes. So, features extraction from ECG signal is crucial to recognise heart disorders. After selecting significant features, classification can be done by Machine Learning (ML), and Deep Learning (DL). Most of the methods utilised to classify the electrocardiogram are based on 1-D electrocardiogram data. These methods focus on extracting the attributes wavelength and time of each waveform as input but but their behavior remains different during classification. Various ECG construal algorithms based on signal processing approaches have been planned in recent years. Few studies show how optimisation techniques are helpful for feature selection and classification with ML and DL. This work compares the studies based on ML and DL. It also depicts how optimisation methods increase the accuracy, sensitivity, and specificity of data.

Keywords: ECG signal, Deep learning, Machine Learning, Feature Extraction, Classification, Optimization Techniques.

Graphical Abstract

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