Alzheimer disease diagnostics from EEG via Wishart distribution of fractional processes

Exact estimation of Hurst exponent from a signal is a complex task that determines the fractional character of the investigated sample. In this work, we propose a maximum likelihood technique using Wishart distribution and autocorrelation structure of the investigated time series. Unlike conventiona...

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Bibliographic Details
Published inSignal, image and video processing Vol. 15; no. 7; pp. 1435 - 1442
Main Authors Dlask, Martin, Kukal, Jaromir
Format Journal Article
LanguageEnglish
Published London Springer London 01.10.2021
Springer Nature B.V
Subjects
Alzheimer's disease
Brownian motion
Computer Imaging
Computer Science
Confidence intervals
Image Processing and Computer Vision
Multimedia Information Systems
Original Paper
Pattern Recognition and Graphics
Segmentation
Signal processing
Signal,Image and Speech Processing
Time series
Vision
fBm
Hurst exponent
Maximum likelihood method
Wishart distribution
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Summary:Exact estimation of Hurst exponent from a signal is a complex task that determines the fractional character of the investigated sample. In this work, we propose a maximum likelihood technique using Wishart distribution and autocorrelation structure of the investigated time series. Unlike conventional methods, we perform signal segmentation and use the aggregated data to obtain an unbiased estimate of Hurst exponent. The efficiency of the estimation is validated by four different methods of fractional Brownian motion generation. The resulting estimates have very tiny confidence intervals as well as small mean square error. Additionally, the proposed methodology has been applied to 19-channel EEG time series and their Hurst exponent estimation related to the diagnostics of Alzheimer’s disease.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-021-01875-9