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Proceedings Paper

Segment clustering methodology for unsupervised Holter recordings analysis
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Paper Abstract

Cardiac arrhythmia analysis on Holter recordings is an important issue in clinical settings, however such issue implicitly involves attending other problems related to the large amount of unlabelled data which means a high computational cost. In this work an unsupervised methodology based in a segment framework is presented, which consists of dividing the raw data into a balanced number of segments in order to identify fiducial points, characterize and cluster the heartbeats in each segment separately. The resulting clusters are merged or split according to an assumed criterion of homogeneity. This framework compensates the high computational cost employed in Holter analysis, being possible its implementation for further real time applications. The performance of the method is measure over the records from the MIT/BIH arrhythmia database and achieves high values of sensibility and specificity, taking advantage of database labels, for a broad kind of heartbeats types recommended by the AAMI.

Paper Details

Date Published: 28 January 2015
PDF: 6 pages
Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 92870M (28 January 2015); doi: 10.1117/12.2073882
Show Author Affiliations
Jose Luis Rodríguez-Sotelo, Univ. Autónoma de Manizales (Colombia)
Diego Peluffo-Ordoñez, Univ. Cooperativa de Colombia (Colombia)
German Castellanos Dominguez, Univ. Nacional de Colombia (Colombia)

Published in SPIE Proceedings Vol. 9287:
10th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore, Editor(s)

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