An intelligent epilepsy seizure detection system using adaptive mode decomposition of EEG signals.

Epilepsy is a chronic neurological disorder that involves abnormal electrical signal patterns of the brain called seizures. The brain's electrical signals can be recorded using an electroencephalogram (EEG). EEG recordings can be used to monitor complex and non-stationary signals produced by the brain for detecting epilepsy seizures. Machine learning (ML) methods have been successfully applied in different domains to accurately classify the patterns based upon dataset features. However, ML methods are unable to analyze the raw EEG signals. Appropriate features must be extracted from EEG recordings for detecting epilepsy seizures using signal processing methods. This work proposes an intelligent system by integrating variational mode decomposition (VMD) and Hilbert transform (HT) method for extracting useful features from EEG signals and stacked neural network (NN) method for detecting epilepsy seizures. VMD method decomposers EEG signals into intrinsic mode functions, followed by the HT method for extracting features from EEG signals. The stacked-NN approach is applied for detecting epilepsy seizures using extracted features. The performance of the proposed system is validated using benchmark datasets for epilepsy seizure detection provided by Bonn University and, Neurology and Sleep Centre, New Delhi (NSC-ND). The performance of the proposed system is compared with other ML methods and state of the art approaches in the field. The reported results demonstrate that the proposed system can detect up to 100% accurate epilepsy seizures using NSC-ND data set and up to 99% accurate epilepsy seizures using Bonn university dataset. The comparative results also demonstrate the better performance of the proposed system over other ML methods and existing approaches for detecting epilepsy seizures. The remarkable performance of the proposed system can help neurological experts to detect epilepsy seizures accurately using EEG signals and can be embedded into the real-time diagnosis of the disease.