Multiscale mutual mode entropy in theta brain wave analysis for epilepsy

This article analyzes theta rhythms of EEG signals from the healthy and epileptics with the algorithm called Multiscale Mutual Mode Entropy. By calculation of Multiscale Mutual Mode Entropy of EEG signals from two channels, we can quantify the coupling degree between two sequences and obtain the coupling information of electroencephalogram. The original EEG signals are filtered and scaling processed. The calculation results show that entropy value of healthy people is higher than that of people with epilepsy on high scale, and the entropy value of original data is higher than that of their surrogate data. We also find evidence of the superiority of Multiscale Mutual Mode Entropy algorithm in terms of noise resistance. Therefore, we can easily find out the coupling information between the sequences of specific rhythm EEG by means of Multiscale Mutual Mode Entropy, which facilitates assessment of brain function and pathological detection.

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