Abstract
Epileptic seizures are usually investigated using EEG. The dynamic and statistical properties of brain waves of an individual with seizure are different from a normal person’s brain waves. This paper exploits these underlying properties of EEG using Lyapunov exponent and approximate entropy and proposes a novel statistical feature namely Gini’s coefficient. In this paper, we propose an automated system for detecting seizure using statistical and machine learning algorithm. The data used was publicly available with five different classes (normal to seizure). Linear discriminant analysis (LDA) was used to classify the extracted features. The proposed method gives the best accuracy of 100% in detecting seizure from the EEG.
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Khan, B.A., Hashmi, A., Farooq, O. (2020). An Automated System for Epileptic Seizure Detection Using EEG. In: Kolhe, M., Tiwari, S., Trivedi, M., Mishra, K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 94. Springer, Singapore. https://doi.org/10.1007/978-981-15-0694-9_15
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DOI: https://doi.org/10.1007/978-981-15-0694-9_15
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