Abstract

Most heart diseases are associated with and reflected by the sounds that the heart produces. Heart auscultation, defined as listening to the heart sound, has been a very important method for the early diagnosis of cardiac dysfunction. Traditional auscultation requires substantial clinical experience and good listening skills. The emergence of the electronic stethoscope has paved the way for a new field of computer-aided auscultation. This article provides an in-depth study of (1) the electronic stethoscope technology, and (2) the methodology for diagnosis of cardiac disorders based on computer-aided auscultation. The paper is based on a comprehensive review of (1) literature articles, (2) market (state-of-the-art) products, and (3) smartphone stethoscope apps. It covers in depth every key component of the computer-aided system with electronic stethoscope, from sensor design, front-end circuitry, denoising algorithm, heart sound segmentation, to the final machine learning techniques. Our intent is to provide an informative and illustrative presentation of the electronic stethoscope, which is valuable and beneficial to academics, researchers and engineers in the technical field, as well as to medical professionals to facilitate its use clinically. The paper provides the technological and medical basis for the development and commercialization of a real-time integrated heart sound detection, acquisition and quantification system.

Highlights

  • Heart disease is the leading cause of death in most countries in the world

  • This paper provides a comprehensive review of literature articles, market products, and smartphone stethoscope apps covering every key component of computeraided cardiac dysfunction detection system using the electronic stethoscope

  • We have found that the study for the automated detection of various heart pathological conditions and diseases from heart sound (HS) signal mainly focuses on three stages: (1) HS acquisition system and sensor design (2) denoising and segmentation of HS signals, and (3) appropriate feature extraction and automatic interpretation of HS

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Summary

Background

Heart disease is the leading cause of death in most countries in the world. In 2012, cardiovascular diseases killed 17.5 million people, i.e., three in every ten deaths [1]. We have found that the study for the automated detection of various heart pathological conditions and diseases from HS signal mainly focuses on three stages: (1) HS acquisition system and sensor design (2) denoising and segmentation of HS signals, and (3) appropriate feature extraction and automatic interpretation of HS. Some of the most popular of such adaptive algorithms are least mean square (LMS) algorithm, normalized LMS (NLMS) algorithm,

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