Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering

Authors:
J. L. RodríGuez-Sotelo;D. Peluffo-OrdoñEz;D. Cuesta-Frau;G. Castellanos-DomíNguez
Affiliations:
Universidad Autónoma de Manizales, Grupo Automática, D. Ing. Electrónica y Automatización, Antigua estación de ferrocarril, Manizales, Colombia;Universidad Nacional de Colombia, D. Ing. Eléctrica, Electrónica y Computación, Km. 9, Vía al aeropuerto, Campus la Nubia, Manizales, Colombia;Technological Institute of Informatics, Polytechnic University of Valencia, Campus Alcoi, Plaza Ferrándiz y Carbonell, 2, 03801 Alcoi, Spain;Universidad Nacional de Colombia, D. Ing. Eléctrica, Electrónica y Computación, Km. 9, Vía al aeropuerto, Campus la Nubia, Manizales, Colombia
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

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Abstract

The computer-assisted analysis of biomedical records has become an essential tool in clinical settings. However, current devices provide a growing amount of data that often exceeds the processing capacity of normal computers. As this amount of information rises, new demands for more efficient data extracting methods appear. This paper addresses the task of data mining in physiological records using a feature selection scheme. An unsupervised method based on relevance analysis is described. This scheme uses a least-squares optimization of the input feature matrix in a single iteration. The output of the algorithm is a feature weighting vector. The performance of the method was assessed using a heartbeat clustering test on real ECG records. The quantitative cluster validity measures yielded a correctly classified heartbeat rate of 98.69% (specificity), 85.88% (sensitivity) and 95.04% (general clustering performance), which is even higher than the performance achieved by other similar ECG clustering studies. The number of features was reduced on average from 100 to 18, and the temporal cost was a 43% lower than in previous ECG clustering schemes.