Previous [ 1] [ 2] [ 3] [ 4] [ 5] [ 6] [ 7] [ 8] [ 9] [ 10] [ 11] [ 12] [ 13] [ 14] [ 15] [ 16] [ 17] [ 18] [ 19] [ 20]

@

Journal of Information Science and Engineering, Vol. 30 No. 4, pp. 1655-1668 (September 2014)


Unsupervised Clustering of Heart Sound Recordings for Cardiac Auscultation Database Indexing


WEI-HO TSAI1 AND SUNG-HOW SUE2
1Department of Electronic Engineering
Graduate Institute of Computer and Communication Engineering
National Taipei University of Technology
Taipei, 106 Taiwan
2Pojen General Hospital
Taipei, 105 Taiwan

This study proposes an unsupervised framework for classifying heart sound data. Its goal is to cluster unknown heart sound recordings, such that each cluster contains sound recordings belonging to the same heart diseases or normal heart beat category. The proposed framework is more flexible than the conventional supervised classification of heart sounds by the case when heart sound data belong to undefined categories or when there is no prior template data for building a heart sound classifier. The proposed system includes four components, heart sound feature extraction, similarity computation, cluster generation, and estimation of the optimal number of clusters. Our experiments show that the resulting clusters based on our system are roughly consistent with the heart beat categories defined by human labeling, which indicates the feasibility of the unsupervised classification framework.

Keywords: clustering, heart sound, unsupervised classification, cardiac auscultation, rand index

Full Text () Retrieve PDF document (201409_20.pdf)

Received October 17, 2012; revised December 16, 2012; accepted February 18, 2013.
Communicated by Hsin-Min Wang.