A classification approach for the heart sound signals using hidden markov models

Authors:
Yong-Joo Chung
Affiliations:
Department of Electronics, Keimyung University, Daegu, S. Korea
Venue:
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2006

Citing 1
Cited 2

A classifier based on the artificial neural network approach for cardiologic auscultation in pediatrics

Artificial Intelligence in Medicine

Classification of Continuous Heart Sound Signals Using the Ergodic Hidden Markov Model

IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Using Kullback-Leibler Distance in Determining the Classes for the Heart Sound Signal Classification

IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

Stethoscopic auscultation is still one of the primary tools for the diagnosis of heart diseases due to its easy accessibility and relatively low cost. Recently, many research efforts have been done on the automatic classification of heart sound signals for supporting clinicians to make better heart sound diagnosis. Conventionally, automatic classification methods of the heart sound signals have been usually based on artificial neural networks (ANNs). But, in this paper, we propose to use hidden Markov models (HMMs) as the classification tool for the heart sound signal. In the experiments classifying 10 different kinds of heart sound signals, the proposed method has shown quite successful results compared with ANNs achieving average classification rate about 99%.