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@INPROCEEDINGS{Schiek:127876,
      author       = {Schiek, Michael},
      title        = {{P}hase based {M}ethods for {D}ata {F}usion},
      reportid     = {FZJ-2012-00825},
      pages        = {online},
      year         = {2011},
      abstract     = {Introduction 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},
      month         = {Sep},
      date          = {2011-09-27},
      organization  = {BMT-2011, Freiburg (Germany), 27 Sep
                       2011 - 29 Sep 2011},
      cin          = {ZEL},
      cid          = {I:(DE-Juel1)ZEL-20090406},
      pnm          = {333 - Pathophysiological Mechanisms of Neurological and
                      Psychiatric Diseases (POF2-333)},
      pid          = {G:(DE-HGF)POF2-333},
      typ          = {PUB:(DE-HGF)8},
      url          = {https://juser.fz-juelich.de/record/127876},
}