A modular approach to computer-aided auscultation: Analysis and parametric characterization of murmur acoustic qualities

Authors:
Chia-Hsuan Shen;Fred K. Choy;Yuerong Chen;Shengyong Wang
Affiliations:
Department of Mechanical Engineering, Chang Gung University, Guishan Taoyuan 333, Taiwan, ROC;Department of Mechanical Engineering, University of Akron, Akron, OH44325-3903, United States;Department of Mechanical Engineering, University of Akron, Akron, OH44325-3903, United States;Department of Mechanical Engineering, University of Akron, Akron, OH44325-3903, United States
Venue:
Computers in Biology and Medicine
Year:
2013

Citing 6
Cited 0

Classification of heart sounds using an artificial neural network

Pattern Recognition Letters
An introduction to ROC analysis

Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Heart sound classification using wavelet transform and incremental self-organizing map

Digital Signal Processing
Evaluation of ensemble methods for diagnosing of valvular heart disease

Expert Systems with Applications: An International Journal
Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier

Expert Systems with Applications: An International Journal
Automatic phonocardiograph signal analysis for detecting heart valve disorders

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the present work, a modularized approach to computer-aided auscultation based on the traditional cardiac auscultation of murmur is proposed. Under such an approach, the present paper concerns the task of evaluating murmur acoustic quality character. The murmurs were analyzed in their time-series representation, frequency representation as well as time-frequency representation, allowing extraction of interpretable features based on their signal structural and spectral characters. The features were evaluated using scatter plots, receiver operating characteristic curves (ROC), and numerical experiments using a KNN classifier. The possible physiological and hemodynamical associations with the feature set are made. The implication and advantage of the modular approach are discussed.