Localization of epileptic surgical area using automated hybrid approach based on higher-order statistics with sensitivity analysis and residual wavelet transform

Abstract

Epilepsy is a persistent neurological disorder characterized by epileptic seizures. Medication is often unable to regulate epileptic episodes, and surgery is the only cure for epilepsy. Therefore, localizing the epileptic zone is crucial for effective epileptic surgery. This surgical area can be identified through the presence of cerebral intracranial focal electroencephalogram (FEEG) signals. This article proposes a mixed approach based on higher-order statistics (HOS) with sensitivity analysis and residual wavelet transform (RWT). The sensitivity analysis method examines only the portions of the brain signal dominated by transient and burst events, and measuring a set of frequencies associated with underlying nonlinear dynamics. The suggested approach assesses the brain output in two distinct spaces based on these frequency benchmarks. The RWT investigates transient and impulsive changes in a non-stationary time series by evaluating it in time-scale space. Additionally, the HOS explores nonlinear dynamics in higher-dimensional space without loss of generality. The data are then mapped and ranked using a data-reduction method, LSDA (locality sensitivity discrimination analysis). The pertinent and delicate information was decoded using a deep learning method based on bidirectional long-short term memory (bi-LSTM). The results reveal that the presented algorithm has yielded a better discrimination accuracy of 99.76% on the Bern Barcelona EEG database. The proposed algorithm enables a clinician to locate the epileptogenic zone, which is essential for effective brain surgery.