Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier.

The electroencephalogram (EEG) signal contains useful information on physiological states of the brain and has proven to be a potential biomarker to realize the complex dynamic behavior of the brain. Epilepsy is a brain disorder described by recurrent and unpredictable interruption of healthy brain function. Diagnosis of patients with epilepsy requires monitoring and visual inspection of long-term EEG by the neurologist, which is found to be a time-consuming procedure. Therefore, this study proposes an automated seizure detection model using a novel computationally efficient feature named sigmoid entropy derived from discrete wavelet transforms. The sigmoid entropy was estimated from the wavelet coefficients in each sub-band and classified using a non-linear support vector machine classifier with leave-one-subject-out cross-validation. The performance of the proposed method was tested with the Ramaiah Medical College and Hospital (RMCH) database, which consists of the 58 Hours of EEG from 115 subjects, the University of Bonn (UBonn), and CHB-MIT databases. Results showed that sigmoid entropy exhibits lower values for epileptic EEG in contrary to other existing entropy methods. We observe a seizure detection rate of 96.34%, a false detection rate of 0.5/h and a mean detection delay of 1.2 s for the RMCH database. The highest sensitivity of 100% and 94.21% were achieved for UBonn and CHB-MIT databases respectively. The performance comparison confirms that sigmoid entropy was found to be better and computationally efficient as compared to other entropy methods. It can be concluded that the proposed sigmoid entropy could be used as a potential biomarker for recognition and detection of epileptic seizures.