Hidden Markov model based epileptic seizure detection using tunable Q wavelet transform.

Deba Prasad Dash,Maheshkumar H Kolekar

doi:10.7555/jbr.34.20190006

Deba Prasad Dash, Maheshkumar H Kolekar

Open Access

https://doi.org/10.7555/jbr.34.20190006

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Abstract

Epilepsy is one of the most prevalent neurological disorders affecting 70 million people worldwide. The present work is focused on designing an efficient algorithm for automatic seizure detection by using electroencephalogram (EEG) as a noninvasive procedure to record neuronal activities in the brain. EEG signals' underlying dynamics are extracted to differentiate healthy and seizure EEG signals. Shannon entropy, collision entropy, transfer entropy, conditional probability, and Hjorth parameter features are extracted from subbands of tunable Q wavelet transform. Efficient decomposition level for different feature vector is selected using the Kruskal-Wallis test to achieve good classification. Different features are combined using the discriminant correlation analysis fusion technique to form a single fused feature vector. The accuracy of the proposed approach is higher for Q=2 and J=10. Transfer entropy is observed to be significant for different class combinations. Proposed approach achieved 100% accuracy in classifying healthy-seizure EEG signal using simple and robust features and hidden Markov model with less computation time. The proposed approach efficiency is evaluated in classifying seizure and non-seizure surface EEG signals. The system has achieved 96.87% accuracy in classifying surface seizure and nonseizure EEG segments using efficient features extracted from different J level.

Highlights

EEG is a noninvasive low-cost technique used to record electrical activity of the neurons and detect neurological disorders such as seizure and dementia[1]
Healthy-seizure (A-E) EEG signal is perfectly detected for both time and frequency domain features
This can be realized by the fact that the cluster created for each group has overlapping region which resulted in low accurate classification model

Summary

Introduction

EEG is a noninvasive low-cost technique used to record electrical activity of the neurons and detect neurological disorders such as seizure and dementia[1]. The seizure patterns vary a lot depending on the type of epilepsy. The present seizure detection technique in hospital is manual and detection accuracy depends largely on the doctor's expertise. Detecting interseizure can help clinicians predicting seizure when a clear seizure pattern is not present in the EEG signal. Different authors have used wavelet transform[2,3] and empirical mode decomposition (EMD) in seizure detection. Empirical mode decomposition technique was used to decompose signals. Features such as Higuchi's fractal dimension, collision, Shannon and minimum entropy features were extracted from each intrinsic mode function

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