Noise-assisted data processing with empirical mode decomposition in biomedical signals

Karagiannis, A; Constantinou, P

dc.contributor.author	Karagiannis, A	en
dc.contributor.author	Constantinou, P	en
dc.date.accessioned	2014-03-01T01:36:26Z
dc.date.available	2014-03-01T01:36:26Z
dc.date.issued	2011	en
dc.identifier.issn	1089-7771	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21302
dc.subject	Biomedical signal processing	en
dc.subject	electrocardiography (ECG)	en
dc.subject	empirical mode decomposition (EMD)	en
dc.subject.classification	Computer Science, Information Systems	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Mathematical & Computational Biology	en
dc.subject.classification	Medical Informatics	en
dc.subject.other	Biomedical signal	en
dc.subject.other	Biomedical signal processing	en
dc.subject.other	De-noising	en
dc.subject.other	ECG signals	en
dc.subject.other	Empirical mode decomposition	en
dc.subject.other	Intrinsic mode functions	en
dc.subject.other	Noise components	en
dc.subject.other	Processing Time	en
dc.subject.other	Statistical significance test	en
dc.subject.other	White Gaussian Noise	en
dc.subject.other	Bioelectric phenomena	en
dc.subject.other	Data handling	en
dc.subject.other	Electrocardiography	en
dc.subject.other	Electrochromic devices	en
dc.subject.other	Gaussian noise (electronic)	en
dc.subject.other	Time series	en
dc.subject.other	Signal processing	en
dc.subject.other	algorithm	en
dc.subject.other	article	en
dc.subject.other	computer simulation	en
dc.subject.other	electrocardiography	en
dc.subject.other	human	en
dc.subject.other	methodology	en
dc.subject.other	signal processing	en
dc.subject.other	Algorithms	en
dc.subject.other	Computer Simulation	en
dc.subject.other	Electrocardiography	en
dc.subject.other	Humans	en
dc.subject.other	Signal Processing, Computer-Assisted	en
dc.title	Noise-assisted data processing with empirical mode decomposition in biomedical signals	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TITB.2010.2091648	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TITB.2010.2091648	en
heal.identifier.secondary	5629368	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	In this paper, a methodology is described in order to investigate the performance of empirical mode decomposition (EMD) in biomedical signals, and especially in the case of electrocardiogram (ECG). Synthetic ECG signals corrupted with white Gaussian noise are employed and time series of various lengths are processed with EMD in order to extract the intrinsic mode functions (IMFs). A statistical significance test is implemented for the identification of IMFs with high-level noise components and their exclusion from denoising procedures. Simulation campaign results reveal that a decrease of processing time is accomplished with the introduction of preprocessing stage, prior to the application of EMD in biomedical time series. Furthermore, the variation in the number of IMFs according to the type of the preprocessing stage is studied as a function of SNR and time-series length. The application of the methodology in MITBIH ECG records is also presented in order to verify the findings in real ECG signals. © 2006 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Information Technology in Biomedicine	en
dc.identifier.doi	10.1109/TITB.2010.2091648	en
dc.identifier.isi	ISI:000286009500002	en
dc.identifier.volume	15	en
dc.identifier.issue	1	en
dc.identifier.spage	11	en
dc.identifier.epage	18	en