NDLI: Fast and Robust Detection of Epilepsy in Noisy EEG Signals Using Permutation Entropy

Content Provider	IEEE Xplore Digital Library
Author	Veisi, I. Pariz, N. Karimpour, A.
Copyright Year	2007
Description	Author affiliation: Ferdowsi Univ., Mashhad (Veisi, I.; Pariz, N.; Karimpour, A.)
Abstract	Permutation entropy (PE) is a new complexity measure which can extract important information from long, complex and high-dimensional time series. The advantages of this measure such as its fast calculation and robustness with respect to additive noise make it suitable for biomedical signal analysis. In this paper the ability of PE for characterizing the normal and epileptic EEG signals is investigated. Classification is performed using discriminant analysis. The effect of additive Gaussian noise on the discrimination performance is also studied and some parameters derived from PE are suggested to improve the classification accuracy when the signal is contaminated with noise. The results indicate that the proposed measures can distinguish normal and epileptic EEG signals with an accuracy of more than 97% for clean EEG and more than 85% for highly noised EEG signals.
Starting Page	200
Ending Page	203
File Size	260507
Page Count	4
File Format	PDF
ISBN	9781424415090
DOI	10.1109/BIBE.2007.4375565
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Epilepsy Electroencephalography Entropy Biomedical measurements Pollution measurement Additive noise Noise robustness Noise measurement Gaussian noise Time measurement permutation entropy EEG epilepsy ordinal pattern
Content Type	Text
Resource Type	Article

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