NDLI: Estimating regularity in epileptic seizure time-series data

Content Provider	IEEE Xplore Digital Library
Author	Radhakrishnan, N. Gangadhar, B.N.
Copyright Year	1983
Abstract	The authors apply Ziv-Lempel (LZ) complexity and approximate entropy (ApEn) as measures to quantify the regularity in the various epochs of epileptic seizure time series data. They demonstrate the potential of complexity measures such as LZ and ApEn in quantifying the regularity at different epochs of epileptic seizure time-series data. It is clearly shown that these measures have high values at the beginning and the end of the seizure, and that they decrease during mid-seizure. In fact, the authors observe in the histogram plot that the frequency of the complexity measure in mid-seizure is quite prominent. This gives one an idea about the epoch where one can find more regular patterns. These measures can also be used as relative indices (comparing across state), rather than absolute indices, by using a larger number of subjects to obtain statistical validity in comparing across conditions. The analysis of time series obtained from complex systems, such as the brain, by the above measures provides an alternative easy way to quantify the regularity with finite-length segments (of the order of 1000 samples). The same can be inferred by calculating the correlation dimension and Lyapunov exponent, but the algorithms used to estimate these invariants are susceptible to error due to the finite sample size and are also highly sensitive to noise. The computational complexity of these algorithms is also high. The authors have also applied these measures across the various states of epilepsy.
Starting Page	89
Ending Page	94
Page Count	6
File Size	958924
File Format	PDF
ISSN	07395175
Volume Number	17
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-05-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Epilepsy Electroencephalography Chaos Time measurement Switches Signal processing Stochastic processes Testing State-space methods Data mining
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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