survey

Computer-aided Arrhythmia Diagnosis with Bio-signal Processing: A Survey of Trends and Techniques

Authors:

Sai Manoj Pudukotai Dinakarrao,

Muhammad ShafiqueAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 52, Issue 2

Article No.: 23, Pages 1 - 37

https://doi.org/10.1145/3297711

Published: 27 March 2019 Publication History

Abstract

Signals obtained from a patient, i.e., bio-signals, are utilized to analyze the health of patient. One such bio-signal of paramount importance is the electrocardiogram (ECG), which represents the functioning of the heart. Any abnormal behavior in the ECG signal is an indicative measure of a malfunctioning of the heart, termed an arrhythmia condition. Due to the involved complexities such as lack of human expertise and high probability to misdiagnose, long-term monitoring based on computer-aided diagnosis (CADiag) is preferred. There exist various CADiag techniques for arrhythmia diagnosis with their own benefits and limitations. In this work, we classify the arrhythmia detection approaches that make use of CADiag based on the utilized technique. A vast number of techniques useful for arrhythmia detection, their performances, the involved complexities, and comparison among different variants of same technique and across different techniques are discussed. The comparison of different techniques in terms of their performance for arrhythmia detection and its suitability for hardware implementation toward body-wearable devices is discussed in this work.

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Efe EYavsan E(2024)AttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signalsMathematical Biosciences and Engineering10.3934/mbe.202425921:4(5863-5880)Online publication date: 2024
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Nguyen HPham VLe H(2024)Blending Ensemble Learning Model for 12-Lead Electrocardiogram-Based Arrhythmia ClassificationComputers10.3390/computers1312031613:12(316)Online publication date: 27-Nov-2024
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Mohammed Fahad RPurohit SBhatnagar SKotambkar DThakre VKapoor K(2024)Development of Deep Learning Model for Cardiac Arrhythmia Detection from ECG2024 IEEE Recent Advances in Intelligent Computational Systems (RAICS)10.1109/RAICS61201.2024.10689988(1-6)Online publication date: 16-May-2024
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Index Terms

Computer-aided Arrhythmia Diagnosis with Bio-signal Processing: A Survey of Trends and Techniques
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection
    2. Machine learning approaches
      1. Classification and regression trees
      2. Neural networks
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Time series analysis

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 52, Issue 2

March 2020

770 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3320149

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering

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Publication History

Published: 27 March 2019

Accepted: 01 November 2018

Revised: 01 June 2018

Received: 01 May 2017

Published in CSUR Volume 52, Issue 2

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https://doi.org/10.3934/mbe.2024259
Nguyen HPham VLe H(2024)Blending Ensemble Learning Model for 12-Lead Electrocardiogram-Based Arrhythmia ClassificationComputers10.3390/computers1312031613:12(316)Online publication date: 27-Nov-2024
https://doi.org/10.3390/computers13120316
Mohammed Fahad RPurohit SBhatnagar SKotambkar DThakre VKapoor K(2024)Development of Deep Learning Model for Cardiac Arrhythmia Detection from ECG2024 IEEE Recent Advances in Intelligent Computational Systems (RAICS)10.1109/RAICS61201.2024.10689988(1-6)Online publication date: 16-May-2024
https://doi.org/10.1109/RAICS61201.2024.10689988
Liu KPeng STsao YLiu CChen ZHan Han ZChen WHsieh PLi YHsu YHsu S(2024)A Cross-Modal Autoencoder for Contactless Electrocardiography Monitoring Using Frequency-Modulated Continuous Wave RadarIEEE Sensors Journal10.1109/JSEN.2024.348615424:24(41462-41473)Online publication date: 15-Dec-2024
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Zhang HLiu WChang SWang HHe JHuang Q(2024)ST-ReGE: A Novel Spatial-Temporal Residual Graph Convolutional Network for CVDIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.332702528:1(216-227)Online publication date: Jan-2024
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Luangphasy TZhao X(2024)Efficient Arrhythmia Detection Using Progressive Resolution Shrinking2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825678(1834-1843)Online publication date: 15-Dec-2024
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Giorgio AGuaragnella CRizzi M(2023)FPGA-Based Decision Support System for ECG AnalysisJournal of Low Power Electronics and Applications10.3390/jlpea1301000613:1(6)Online publication date: 7-Jan-2023
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Ansari YMourad OQaraqe KSerpedin E(2023)Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023Frontiers in Physiology10.3389/fphys.2023.124674614Online publication date: 15-Sep-2023
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