Accurate Multiscale Permutation Entropy Analysis of Brain Rhythm to Detect Epileptic Seizure
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Electroencephalogram(EEG) has been a standard tool to monitor the status of the brain. For quantification of EEG, permutation entropy has been of interest due to simplicity and robustness to noise. A multiscale extension of PE, called multiscale PE(MPE), has been promising in fully describing the dynamical characteristics of EEG over multiple temporal scales. However, an imprecise estimation of MPE at large scales limits its application for analyzing of short EEG. Here, a new multiscale PE measure which aims at estimating entropy accurately is presented. By computing PE of all possible coarse-grained time series and averaging the values of PE at each scale, the resultant composite MPE (CMPE) yields improved accuracy in estimation of entropy. Thus, the CMPE measure accomplishes consistent quantification of entropy regardless of the length of data. This advantage of CMPE renders its capability for analyzing EEG signals. Through simulations with two synthetic noises, CMPE has proved its capability over MPE in terms of accuracy. Experimental results using normal, inter-ictal and ictal EEG recordings have shown that the CMPE measure has leaded an improved discrimination capability for three different neurological states (normal, inter-ictal, and ictal states) than the conventional PE family.
Abstract
Electroencephalogram(EEG) has been a standard tool to monitor the status of the brain. For quantification of EEG, permutation entropy has been of interest due to simplicity and robustness to noise. A multiscale extension of PE, called multiscale PE(MPE), has been promising in fully describing the dynamical characteristics of EEG over multiple temporal scales. However, an imprecise estimation of MPE at large scales limits its application for analyzing of short EEG. Here, a new multiscale PE measure which aims at estimating entropy accurately is presented. By computing PE of all possible coarse-grained time series and averaging the values of PE at each scale, the resultant composite MPE (CMPE) yields improved accuracy in estimation of entropy. Thus, the CMPE measure accomplishes consistent quantification of entropy regardless of the length of data. This advantage of CMPE renders its capability for analyzing EEG signals. Through simulations with two synthetic noises, CMPE has proved its capability over MPE in terms of accuracy. Experimental results using normal, inter-ictal and ictal EEG recordings have shown that the CMPE measure has leaded an improved discrimination capability for three different neurological states (normal, inter-ictal, and ictal states) than the conventional PE family.
참고문헌 (16)
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