Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

Muhammad Wasimuddin,Omar Abuzaghleh,Khaled Elleithy,Abdel-Shakour Abuzneid,Miad Faezipour

doi:10.1109/access.2020.3026968

Muhammad Wasimuddin, Omar Abuzaghleh + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.3026968

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Abstract

Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this article. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined.

Highlights

CONTRIBUTIONS This study aims to contribute to the growing area of research for the detection of heart conditions and different arrhythmias
Diagnosis of myocardial infarction (MI) can save lives and is a challenging task, but with computer-aided design (CAD) and machine learning techniques, automated diagnosis of MI can be achieved with ECG analysis and classification
This article presented a comprehensive review of different traditional and machine learning methods used in every stage of ECG signal analysis, for the ECG classification task

Summary

Introduction

A. BACKGROUND AND MOTIVATION Heart diseases, called Cardiovascular Diseases (CVDs), are the main causes of high mortality rates. BACKGROUND AND MOTIVATION Heart diseases, called Cardiovascular Diseases (CVDs), are the main causes of high mortality rates They arise with a lack of blood in the coronary artery that supplies blood to the heart itself. Fast and accurate identification of arrhythmia from the ECG wavegraph can potentially save many lives and much in terms of health care costs worldwide [1]. This motivated us to perform a detailed review of ECG analysis and present it in the form of a stages-based process model to further clarify and categorize the flow and significance of each phase of ECG signal analysis. With the enormous impact that effective ECG signal analysis offers on public health and economy, giving a perspective of hardware and software tools along with real-time monitoring using portable and wearable devices to analyze an ECG signal in the form of stages-based process is another motivation that led us to conduct this study

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