Dimensional complexity and spectral properties of the human sleep EEG
Dimensional complexity and spectral properties of the human sleep EEG
Objective: The relevance of the dimensional complexity (DC) for the analysis of sleep EEG data is investigated and compared to linear measures.
Methods: We calculated DC of artifact-free 1 min segments of all-night sleep EEG recordings of 4 healthy young subjects. Non-linearity was tested by comparing with DC values of surrogate data. Linear properties of the segments were characterized by estimating the self-similarity exponent ? based on the detrended fluctuation analysis which quantifies the persistence of the signal and by calculating spectral power in the delta, theta, alpha and sigma bands, respectively.
Results: We found weak nonlinear signatures in all sleep stages, but most pronounced in sleep stage 2. Strong correlations between DC and linear measures were established for the self-similarity exponent ? and delta power, respectively.
Conclusions: The dimensional complexity of the sleep EEG is influenced by both linear and nonlinear features. It cannot be directly interpreted as a nonlinear synchronization measure of brain activity, but yields valuable information when combined with the analysis of linear measures.
self-similarity exponent, spectral power, nonlinear analysis, surrogate analysis
199-209
Shen, Y.
b7aa33ab-b778-4454-a00c-f40c4ca46539
Olbrich, E.
09ca5fb1-9d9a-46a4-89aa-fc2726d7c258
Achermann, P.
ebb236fd-19da-4e30-ac96-2797784632f0
Meier, P.F.
803df7a3-67f8-46af-bae6-1c8fc2b63535
2003
Shen, Y.
b7aa33ab-b778-4454-a00c-f40c4ca46539
Olbrich, E.
09ca5fb1-9d9a-46a4-89aa-fc2726d7c258
Achermann, P.
ebb236fd-19da-4e30-ac96-2797784632f0
Meier, P.F.
803df7a3-67f8-46af-bae6-1c8fc2b63535
Shen, Y., Olbrich, E., Achermann, P. and Meier, P.F.
(2003)
Dimensional complexity and spectral properties of the human sleep EEG.
Clinical Neurophysiology, 114 (2), .
(doi:10.1016/S1388-2457(02)00338-3).
Abstract
Objective: The relevance of the dimensional complexity (DC) for the analysis of sleep EEG data is investigated and compared to linear measures.
Methods: We calculated DC of artifact-free 1 min segments of all-night sleep EEG recordings of 4 healthy young subjects. Non-linearity was tested by comparing with DC values of surrogate data. Linear properties of the segments were characterized by estimating the self-similarity exponent ? based on the detrended fluctuation analysis which quantifies the persistence of the signal and by calculating spectral power in the delta, theta, alpha and sigma bands, respectively.
Results: We found weak nonlinear signatures in all sleep stages, but most pronounced in sleep stage 2. Strong correlations between DC and linear measures were established for the self-similarity exponent ? and delta power, respectively.
Conclusions: The dimensional complexity of the sleep EEG is influenced by both linear and nonlinear features. It cannot be directly interpreted as a nonlinear synchronization measure of brain activity, but yields valuable information when combined with the analysis of linear measures.
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Published date: 2003
Keywords:
self-similarity exponent, spectral power, nonlinear analysis, surrogate analysis
Organisations:
Operational Research
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Local EPrints ID: 29754
URI: http://eprints.soton.ac.uk/id/eprint/29754
PURE UUID: ec1d2922-260b-49e9-82cf-7e92683bdfd0
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Date deposited: 12 May 2006
Last modified: 15 Mar 2024 07:34
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Author:
Y. Shen
Author:
E. Olbrich
Author:
P. Achermann
Author:
P.F. Meier
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