Assessment of the classification capability of prediction and approximation methods for HRV analysis

Manis, G; Nikolopoulos, S; Alexandridi, A; Davos, C

dc.contributor.author	Manis, G	en
dc.contributor.author	Nikolopoulos, S	en
dc.contributor.author	Alexandridi, A	en
dc.contributor.author	Davos, C	en
dc.date.accessioned	2014-03-01T01:25:57Z
dc.date.available	2014-03-01T01:25:57Z
dc.date.issued	2007	en
dc.identifier.issn	0010-4825	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17841
dc.subject	Approximation	en
dc.subject	Cardiogram classification	en
dc.subject	ECG	en
dc.subject	Heart rate variability	en
dc.subject	Mean error	en
dc.subject	Prediction	en
dc.subject.classification	Biology	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Biomedical	en
dc.subject.classification	Mathematical & Computational Biology	en
dc.subject.other	Bioelectric potentials	en
dc.subject.other	Electrocardiography	en
dc.subject.other	Least squares approximations	en
dc.subject.other	Medical computing	en
dc.subject.other	Neural networks	en
dc.subject.other	Patient monitoring	en
dc.subject.other	Wavelet transforms	en
dc.subject.other	Approximation methods	en
dc.subject.other	Cardiogram classification	en
dc.subject.other	Heart rate variability (HRV)	en
dc.subject.other	Mean error	en
dc.subject.other	Cardiovascular system	en
dc.subject.other	adult	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	controlled study	en
dc.subject.other	female	en
dc.subject.other	heart rate variability	en
dc.subject.other	human	en
dc.subject.other	male	en
dc.subject.other	mathematical computing	en
dc.subject.other	normal human	en
dc.subject.other	prediction	en
dc.subject.other	priority journal	en
dc.subject.other	waveform	en
dc.subject.other	Adult	en
dc.subject.other	Age Factors	en
dc.subject.other	Aged	en
dc.subject.other	Coronary Disease	en
dc.subject.other	Electrocardiography	en
dc.subject.other	Electrocardiography, Ambulatory	en
dc.subject.other	Female	en
dc.subject.other	Forecasting	en
dc.subject.other	Fourier Analysis	en
dc.subject.other	Heart Failure, Congestive	en
dc.subject.other	Heart Rate	en
dc.subject.other	Humans	en
dc.subject.other	Least-Squares Analysis	en
dc.subject.other	Linear Models	en
dc.subject.other	Male	en
dc.subject.other	Middle Aged	en
dc.subject.other	Neural Networks (Computer)	en
dc.subject.other	Time Factors	en
dc.title	Assessment of the classification capability of prediction and approximation methods for HRV analysis	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.compbiomed.2006.06.008	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.compbiomed.2006.06.008	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	The goal of this paper is to examine the classification capabilities of various prediction and approximation methods and suggest which are most likely to be suitable for the clinical setting. Various prediction and approximation methods are applied in order to detect and extract those which provide the better differentiation between control and patient data, as well as members of different age groups. The prediction methods are local linear prediction, local exponential prediction, the delay times method, autoregressive prediction and neural networks. Approximation is computed with local linear approximation, least squares approximation, neural networks and the wavelet transform. These methods are chosen since each has a different physical basis and thus extracts and uses time series information in a different way. (c) 2006 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Computers in Biology and Medicine	en
dc.identifier.doi	10.1016/j.compbiomed.2006.06.008	en
dc.identifier.isi	ISI:000246166500007	en
dc.identifier.volume	37	en
dc.identifier.issue	5	en
dc.identifier.spage	642	en
dc.identifier.epage	654	en