Epilepsy Seizure Detection Using Akima Spline Interpolation Based Ensemble Empirical Mode Kalman Filter Decomposition by EEG Signals

Abstract

Epilepsy is an incessant neurological disorder. The Epilepsy seizures are generated due to the aggravation in transient signals in Cerebrum. These seizures can be detected by analyzing the Electroencephalogram (EEG) Signals. The Akima Spline Interpolation based Ensemble Empirical Mode Kalman Filter Decomposition (ASI-EEMKFD) model proposed in the paper focuses on detecting seizures automatically through a stable algorithm written in Python by using PyEEG package. The signal detection process is done in three phases. First, the EEG signals are acquired through data sets. Then the signal is decomposed using Akima Spline interpolation for finding the intrinsic mode function. Further the signal is decomposed by implementing the steps involved in the Ensemble Empirical Mode Decomposition (EEMD). During the decomposition Kalman filter is used in order to remove the white Gaussian noise. Finally, the decomposed signals are applied to the Long Term Short Term Memory (LTST) deep learning classifier which classifies the ictal, pre-ictal and healthy signal. Our proposed method produces the result higher compared with the existing EEMD Methods with the accuracy rate of 98.2%, sensitivity of 94.96% and specificity of 93.72%.