Detection of Epileptic Seizure EEG Signal Using Multiscale Entropies and Complete Ensemble Empirical Mode Decomposition
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Detection of Epileptic Seizure EEG Signal Using Multiscale Entropies and Complete Ensemble Empirical Mode Decomposition Gurwinder Singh1 · Manpreet Kaur2 · Birmohan Singh1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Epilepsy is a severe neurological disease which is diagnosed by analyzing Electroencephalogram. The epileptic seizure detection technique based on multiscale entropies and complete ensemble empirical mode decomposition (CEEMD) is proposed in this paper. CEEMD is used for the estimation of sub-bands and two multiscale entropies; multiscale dispersion entropy (MDE) and refined composite MDE are extracted from the sub-bands. The feature selection method, configured by hybridizing the filter based and wrapper based method, is used to select relevant multiscale entropies. The hybrid method has not only reduced features but also improved classification performance. An artificial neural network is trained with relevant features and performance is measured using classification accuracy, sensitivity and specificity. Five clinically relevant classification problems are used to assess the proposed technique. The performance is also compared with the state of the art techniques. The proposed technique has shown an improvement in detection of seizures and can be used to build the clinical system for epileptic seizure detection. Keywords Epilepsy · Electroencephalogram · Multiscale entropies · Complete ensemble empirical mode decomposition (CEEMD) · Artificial neural network
* Birmohan Singh [email protected]; [email protected] Gurwinder Singh [email protected] Manpreet Kaur [email protected] 1
Department of Computer Science and Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, India
2
Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, India
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1 Introduction One of the most common neurological diseases is epilepsy, which affects nearly 50 million people worldwide [1]. A person suffering from epilepsy experiences recurrent seizures due to the firing of the multiple neurons at a same time [2].This deeply affects the normal behavior and the cognitive process of a person [3]. Electroencephalogram (EEG) is one of the most effective techniques used for measuring the electrical activity of the brain for diagnosis of various neurological disorders like epilepsy, brain tumor, sleep disorders, encephalitis, and stroke [4]. EEG signals are recorded by placing electrodes either on the intracranial area or the scalp of the patients’ brain. Scalp-EEG measures the activities of the neurons which are nearer to the surface of the brain whereas intracranial EEG record activities of neurons which are deep inside of the brain [5]. In the case of epilepsy, longterm EEG monitoring is done, which results in the generation of a huge volume of EEG data. Manual analysis of such data is tedious and is much prone to error
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