title: Application of higher order statistics/spectra in biomedical signals--a review.
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.
authors.affiliation: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 535 Clementi Road, Singapore 599489, Singapore. ckc@np.edu.sg
authors.forename: Kuang Chua
authors.initials: KC
authors.lastname: Chua
authors.forename: Vinod
authors.initials: V
authors.lastname: Chandran
authors.forename: U Rajendra
authors.initials: UR
authors.lastname: Acharya
authors.forename: Choo Min
authors.initials: CM
authors.lastname: Lim
mesh_terms: D000818:Animals; D004562:Electrocardiography; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted