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

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 paper. 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. INDEX TERMS ECG analysis, cardiac arrhythmias, QRS and ST detection, ECG classification, deep learning.

Origins of ECG and Evolution of Automated DSP Techniques: A Review

“…Einthoven in 1906 categorized normal and abnormal ECGs that were translated by Cardiologist Henry Blackburn [30]. He discussed the first electrocardiographic tracings of atrial fibrillation 3 , premature ventricular contractions 4 , ventricular bigeminy 5 , atrial flutter 6 . The beginning of ECG related research was also demonstrated in an experimental setup that induced heart block in a dog, as shown in Fig.…”

“…It also discusses the American observations of the ECG, the role of Thomas Lewis and the development of Electrocardiography, ECG and Myocardial Infarction (MI), precordial leads, and augmented limb leads, Vectorcardiogram in clinical physiology and the challenges of Electrocardiography. While these reviews focus on the historical development of the ECG, a recent review [4] focuses on various ECG signal processing research development that has occurred during 2000-2020. In [5], authors discuss several automatic detection methods for Myocardial Infarctions in their review.…”

“…Cardiac anomaly where R-R interval is abnormal and P wave is missing at instances 4. Abnormal heartbeat where contractions begin in the ventricles, instead of Sinoatrial node of heart5 Arrhythmia where there is a pattern of irregular heartbeat and regular heartbeat occurrence 6.…”

Multilabel 12-Lead Electrocardiogram Classification Using Beat to Sequence Autoencoders

“…We are inspired by prior work that uses autoencoders to generate features for signal classification [3,4]. Recent advancements in machine learning and available data have heralded an influx of multi-lead ECG classification algorithms [5,6,7,8,9,2]. We extend our prior work by using neural networks over feature engineering with gradient boosted tree classifiers [2].…”

DeepCEDNet: An Efficient Deep Convolutional Encoder-Decoder Networks for ECG Signal Enhancement

“…Recently, deep learning techniques have been gaining attention due to their powerful capability in learning the characteristics of ECG signal [23]- [27]. The noise suppression techniques based on denoising autoencoder (DAE) have shown the excellent performance than conventional denoising methods.…”