Automated System for the Detection of Heart Anomalies Using Phonocardiograms: A Systematic Review

Anjan Gudigar; U. Raghavendra; M. Maithri; Jyothi Samanth; Mahesh Anil Inamdar; V. Vidhya; Jahmunah Vicnesh; Mukund A. Prabhu; Ru-San Tan; Chai Hong Yeong; Filippo Molinari; U. R. Acharya

doi:10.1109/ACCESS.2024.3465511

IEEE Access (Jan 2024)

Automated System for the Detection of Heart Anomalies Using Phonocardiograms: A Systematic Review

Anjan Gudigar,
U. Raghavendra,
M. Maithri,
Jyothi Samanth,
Mahesh Anil Inamdar,
V. Vidhya,
Jahmunah Vicnesh,
Mukund A. Prabhu,
Ru-San Tan,
Chai Hong Yeong,
Filippo Molinari,
U. R. Acharya

Affiliations

Anjan Gudigar: ORCiD; Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
U. Raghavendra: ORCiD; Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
M. Maithri: ORCiD; Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
Jyothi Samanth: ORCiD; Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, India
Mahesh Anil Inamdar: Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
V. Vidhya: ORCiD; Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
Jahmunah Vicnesh: School of Engineering, Nanyang Polytechnic, Jurong West, Singapore
Mukund A. Prabhu: ORCiD; Department of Cardiology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India
Ru-San Tan: ORCiD; National Heart Centre, Jurong West, Singapore
Chai Hong Yeong: School of Medicine, Faculty of Health and Medical Sciences, and Digital Health and Medical Advancement Impact Laboratory, Taylor’s University, Subang Jaya, Malaysia
Filippo Molinari: ORCiD; Department of Electronics and Telecommunications, Biolab, PolitoBIOMedLab, Politecnico di Torino, Turin, Italy
U. R. Acharya: ORCiD; School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, Australia

DOI: https://doi.org/10.1109/ACCESS.2024.3465511
Journal volume & issue: Vol. 12
pp. 138399 – 138428

Abstract

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Phonocardiogram (PCG) signals generated by the heart contain information about heart conditions. This review examines how PCG analysis identifies and diagnoses heart issues. We studied traditional signal processing and artificial intelligence techniques and provided a complete picture of the current state of this field. Adhering to the systematic review guidelines, our comprehensive review covers 103 studies from reputed journals. It includes Machine Learning (ML) and Deep Learning (DL) techniques used to develop the computer-aided diagnostic tools using PCG signals. This review evaluates the strengths and weaknesses of various ML and DL methods, emphasizing their effectiveness in diagnosing several abnormalities. Additionally, we examine the obstacles and challenges limiting the widespread adoption of PCG-based diagnostic systems in clinical settings. We outline a plan for future research to develop improved versions of PCG analysis models. These models will be more robust, precise, and user-friendly. They will improve cardiovascular care by enabling machines to screen for problems automatically and intelligently.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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