A prediction model for the number of intrinsic mode functions in biomedical signals: The case of electrocardiogram

Karagiannis, A; Constantinou, P

dc.contributor.author	Karagiannis, A	en
dc.contributor.author	Constantinou, P	en
dc.date.accessioned	2014-03-01T02:47:15Z
dc.date.available	2014-03-01T02:47:15Z
dc.date.issued	2011	en
dc.identifier.issn	1746-8094	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33030
dc.subject	Biomedical signals	en
dc.subject	Electrocardiogram	en
dc.subject	Empirical mode decomposition	en
dc.subject	Extrema	en
dc.subject	Intrinsic mode functions	en
dc.subject	Kurtosis	en
dc.subject	Prediction model	en
dc.subject.other	Biomedical signal	en
dc.subject.other	Electrocardiogram	en
dc.subject.other	Empirical Mode Decomposition	en
dc.subject.other	Extrema	en
dc.subject.other	Intrinsic Mode functions	en
dc.subject.other	Kurtosis	en
dc.subject.other	Prediction model	en
dc.subject.other	Bioelectric phenomena	en
dc.subject.other	Electrocardiography	en
dc.subject.other	Electrochromic devices	en
dc.subject.other	Forecasting	en
dc.subject.other	Mathematical models	en
dc.subject.other	Time series	en
dc.subject.other	Computer simulation	en
dc.subject.other	computer simulation	en
dc.subject.other	conference paper	en
dc.subject.other	electrocardiogram	en
dc.subject.other	empirical mode decomposition	en
dc.subject.other	intrinsic mode function	en
dc.subject.other	mathematical model	en
dc.subject.other	prediction	en
dc.subject.other	priority journal	en
dc.subject.other	signal processing	en
dc.subject.other	validation study	en
dc.title	A prediction model for the number of intrinsic mode functions in biomedical signals: The case of electrocardiogram	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1016/j.bspc.2011.02.005	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.bspc.2011.02.005	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	In this paper, the open issue of prediction of the number of intrinsic mode functions (IMF) extracted from a time series after the application of empirical mode decomposition (EMD) is addressed and a methodology is presented directing towards an a priori prediction model. Parameters related with the time series are measured or calculated and used by the model in order to define a closed set in which the actual total number of IMFs is expected to be included after the application of EMD. The prediction model is verified by a large number of tests on simulated electrocardiogram (ECG) time series and after refinement it is validated using real ECG time series from Physionet MIT-BIH database. (C) 2011 Elsevier Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	Biomedical Signal Processing and Control	en
dc.identifier.doi	10.1016/j.bspc.2011.02.005	en
dc.identifier.isi	ISI:000293480100004	en
dc.identifier.volume	6	en
dc.identifier.issue	3	en
dc.identifier.spage	231	en
dc.identifier.epage	243	en