Noise components identification in biomedical signals based on empirical mode decomposition

Karagiannis, A; Constantinou, Ph

dc.contributor.author	Karagiannis, A	en
dc.contributor.author	Constantinou, Ph	en
dc.date.accessioned	2014-03-01T02:46:15Z
dc.date.available	2014-03-01T02:46:15Z
dc.date.issued	2009	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32626
dc.subject	Biomedical signal	en
dc.subject	Empirical mode decomposition	en
dc.subject	Hilbert huang transform	en
dc.subject	IMF	en
dc.subject	Noise components	en
dc.subject	Partial signal reconstruction	en
dc.subject	Statistical significance	en
dc.subject.other	Biomedical signal	en
dc.subject.other	Empirical Mode Decomposition	en
dc.subject.other	Hilbert Huang transforms	en
dc.subject.other	IMF	en
dc.subject.other	Noise components	en
dc.subject.other	Statistical significance	en
dc.subject.other	Acoustic signal processing	en
dc.subject.other	Bioelectric phenomena	en
dc.subject.other	Information technology	en
dc.subject.other	Repair	en
dc.subject.other	Signal analysis	en
dc.subject.other	Signal reconstruction	en
dc.subject.other	Spectrum analysis	en
dc.subject.other	Spectrum analyzers	en
dc.subject.other	Structures (built objects)	en
dc.subject.other	Vibration measurement	en
dc.subject.other	Mathematical transformations	en
dc.title	Noise components identification in biomedical signals based on empirical mode decomposition	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ITAB.2009.5394300	en
heal.identifier.secondary	5394300	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ITAB.2009.5394300	en
heal.publicationDate	2009	en
heal.abstract	Hilbert-Huang Transform (HHT) is composed of the Empirical Mode Decomposition (EMD) as the first step of the procedure and Hilbert Spectral analysis (HSA) as the second step. It is a recent tool in the analysis of signals originating from nonlinear processes as well as nonstationary signals. Empirical Mode Decomposition produces a set of Intrinsic Mode Functions and the core idea is based on the assumption that any data consists of different simple intrinsic modes of oscillations. Statistical significance of the Intrinsic Mode Functions and partial signal reconstruction are investigated in this paper. Application of Hilbert-Huang Transform on biomedical signals such as ECG from MIT-BIH database and experimental respiratory signals acquired by means of accelerometers, reveal the adaptive nature of the method. ©2009 IEEE.	en
heal.journalName	Final Program and Abstract Book - 9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009	en
dc.identifier.doi	10.1109/ITAB.2009.5394300	en