000127876 001__ 127876
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000127876 037__ $$aFZJ-2012-00825
000127876 041__ $$aEnglish
000127876 1001_ $$0P:(DE-Juel1)133935$$aSchiek, Michael$$b0$$eCorresponding author
000127876 1112_ $$aBMT-2011$$cFreiburg$$d2011-09-27 - 2011-09-29$$gBMT-2011$$wGermany
000127876 245__ $$aPhase based Methods for Data Fusion
000127876 260__ $$c2011
000127876 300__ $$aonline
000127876 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1358952452_24571
000127876 3367_ $$033$$2EndNote$$aConference Paper
000127876 3367_ $$2ORCID$$aCONFERENCE_PAPER
000127876 3367_ $$2DataCite$$aOutput Types/Conference Paper
000127876 3367_ $$2DRIVER$$aconferenceObject
000127876 3367_ $$2BibTeX$$aINPROCEEDINGS
000127876 500__ $$3POF3_Assignment on 2016-02-29
000127876 520__ $$aIntroduction Data fusion is the method to merge signals from different sources or sensors in order to, e.g., study the relationship among events or objects, reconstruct the laws of motion of a dynamical system, identify specific events or states of a dynamical system or even suppress noise and artifacts. Phase based methods are proposed for data fusion in physiological signals. Methods In physiology the far most activities obey a cyclic dynamic; therefore data fusion based on phase synchronization analysis is a promising approach in order to reveal the mutual interaction between the different processes, e.g. cardiac and respiratory activity. Phase calculation requires proper band pass filtering as a prerequisite, the cut-off frequencies being defined by the power spectral densities of the different signals. Applying this method the data fusion results e.g. in a histogram of phase differences whose deviation from a uniform distribution can be used to quantify the degree of phase synchronization. Another approach uses recurring events within one signal to mark trial epochs within the other signal in order to apply Cross Trial Phase Statistics (CTPS) [1]. Deviations from uniform distribution sharply indicate intermittent interactions between the signals also in those cases where amplitude based methods fail to reveal any cross correlation at all. In long term recordings during daily activity the occurrence of movement artifacts is very likely. In order to preserve the information of the undisturbed epochs accelerometer based movement detection can be used for artifact identification. The former mentioned CTPS allows for doing this in an automated manner. Results and Conclusion Phase analysis based methods are well suited for data fusion in physiological long term recordings. The analysis of phase difference histograms or Cross Trial Phase Statistics (CTPS) result in quantitative measures for synchronization or intermittent interaction of the merged signal and therefore reduces the huge amount of raw data to single numbers. This information reduction is an essential issue in long term recordings. CTPS also allows for automated artifact identification which is an important demand in home monitoring. [1] Dammers J, et al., (2008). IEEE transactions on bio-medical engineering 55:2353-62
000127876 536__ $$0G:(DE-HGF)POF2-333$$a333 - Pathophysiological Mechanisms of Neurological and Psychiatric Diseases (POF2-333)$$cPOF2-333$$fPOF II$$x0
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000127876 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)133935$$aForschungszentrum Jülich GmbH$$b0$$kFZJ
000127876 9132_ $$0G:(DE-HGF)POF3-579H$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vAddenda$$x0
000127876 9131_ $$0G:(DE-HGF)POF2-333$$1G:(DE-HGF)POF2-330$$2G:(DE-HGF)POF2-300$$3G:(DE-HGF)POF2$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lFunktion und Dysfunktion des Nervensystems$$vPathophysiological Mechanisms of Neurological and Psychiatric Diseases$$x0
000127876 9141_ $$y2012
000127876 920__ $$lyes
000127876 9201_ $$0I:(DE-Juel1)ZEL-20090406$$kZEL$$lZentralinstitut für Elektronik$$x0
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000127876 980__ $$aI:(DE-Juel1)ZEA-2-20090406
000127876 981__ $$aI:(DE-Juel1)PGI-4-20110106
000127876 981__ $$aI:(DE-Juel1)ZEA-2-20090406
000127876 981__ $$aI:(DE-Juel1)ZEL-20090406