  This contribution reports an application of MultiFractal Detrended
Fluctuation Analysis, MFDFA based novel feature extraction technique for
automated detection of epilepsy. In fractal geometry, Multifractal Detrended
Fluctuation Analysis MFDFA is a popular technique to examine the
self-similarity of a nonlinear, chaotic and noisy time series. In the present
research work, EEG signals representing healthy, interictal (seizure free) and
ictal activities (seizure) are acquired from an existing available database.
The acquired EEG signals of different states are at first analyzed using MFDFA.
To requisite the time series singularity quantification at local and global
scales, a novel set of fourteen different features. Suitable feature ranking
employing students t-test has been done to select the most statistically
significant features which are henceforth being used as inputs to a support
vector machines (SVM) classifier for the classification of different EEG
signals. Eight different classification problems have been presented in this
paper and it has been observed that the overall classification accuracy using
MFDFA based features are reasonably satisfactory for all classification
problems. The performance of the proposed method are also found to be quite
commensurable and in some cases even better when compared with the results
published in existing literature studied on the similar data set.
