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A moving window approximate entropy in wavelet framework for automatic detection of the onset of epileptic seizures

Epilepsy is, in general, a diseased condition where the brain fires abnormal signals, which results in convolutions in the muscles-which occurs suddenly to the patients. In this work, we prescribe a novel method to automatically identify the onset of epileptic seizures. A moving window approximate entropy (ApEn) is run over the Electroencephalogram (EEG) signal with the epileptic seizures. ApEn value drops at the conjuncture of the onset of the epileptic seizures. This ApEn characteristic drop is considered as feature for detecting the onset. Moreover this ApEn characteristic drop is enhanced using wavelet transform forming the feature to be in ApEn wavelet framework. Three neural networks namely Feed forward Back propagation, Elman and Radial basis networks is employed for automatically detecting the onset of the epileptic seizure. They are compared with each other for their performance with ApEn as feature and ApEn in wavelet framework as feature. It was found that radial basis network is giving a better overall accuracy of 94%, when their inputs are features being ApEn in wavelet framework. Additionally, the onset sample detected by the algorithm and manually identified onset sample by the encephalographers are compared and it was found that our algorithm was able to detect the onset of the epileptic seizures with an average detection delay of only 0.2 second for the 200 segments of EEG considered across 31 patients, which is low compared to earlier works in the literature.

Author(s): N. Arunkumar, K. Ramkumar, V. Venkataraman