Abstract:
:For many decades correlation and power spectrum have been primary tools for digital signal processing applications in the biomedical area. The information contained in the power spectrum is essentially that of the autocorrelation sequence; which is sufficient for complete statistical descriptions of Gaussian signals of known means. However, there are practical situations where one needs to look beyond autocorrelation of a signal to extract information regarding deviation from Gaussianity and the presence of phase relations. Higher order spectra, also known as polyspectra, are spectral representations of higher order statistics, i.e. moments and cumulants of third order and beyond. HOS (higher order statistics or higher order spectra) can detect deviations from linearity, stationarity or Gaussianity in the signal. Most of the biomedical signals are non-linear, non-stationary and non-Gaussian in nature and therefore it can be more advantageous to analyze them with HOS compared to the use of second-order correlations and power spectra. In this paper we have discussed the application of HOS for different bio-signals. HOS methods of analysis are explained using a typical heart rate variability (HRV) signal and applications to other signals are reviewed.
journal_name
Med Eng Physjournal_title
Medical engineering & physicsauthors
Chua KC,Chandran V,Acharya UR,Lim CMdoi
10.1016/j.medengphy.2010.04.009subject
Has Abstractpub_date
2010-09-01 00:00:00pages
679-89issue
7eissn
1350-4533issn
1873-4030pii
S1350-4533(10)00083-4journal_volume
32pub_type
杂志文章,评审abstract::Recent research has indicated that consumers of upper limb prostheses desire lighter-weight, anthropomorphic devices. The potential of dielectric elastomer (DE) actuators to better meet the design priorities of prosthesis users is explored. Current challenges are critically reviewed with respect to (1) durability, (2)...
journal_title:Medical engineering & physics
pub_type: 杂志文章,评审
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pub_type: 杂志文章
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