Early Detection of Epilepsy using Automatic Speech Recognition

Abstract

Objectives: Epilepsy is a neurological disorder that is characterized by occurrence of seizures. The Electroencephalogram (EEG) signals are used as the primary source of data for the study of epilepsy. This study uses Mel Frequency Cepstral Coefficients(MFCC) for early detection of epilepsy in adults. Method: Use of MFCC is a de-facto method of Automatic Speech Recognition (ASR). Extending the use of the same method for EEG signals yields reliable results as the properties of EEG signals resemble the properties of speech signals. The training and test samples were taken from EEG database of the University of Bonn. Using the database a support vector machine was trained and then was used for testing. Findings: The use of MFCC and along with Support Vector Machine (SVM) has an average accuracy of 98.5%. Therefore, an epileptic EEG signal can be detected with a high accuracy. The results reaffirmed the fact that there is a high correlation between the speech signals and EEG signals. The newer methods of ASR may be explored for finer results. There is a significant improvement in accuracy over other methods of epilepsy detection.