Automated epileptic seizure detection by analyzing wearable EEG signals using extended correlation-based feature selection

Document Type

Conference Proceeding

Publication Title

2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

Publisher

IEEE

School

School of Science

RAS ID

43789

Funders

National Natural Science Foundation of China Shandong Provincial Key Research & Development Project

Comments

Guo, Y., Zhang, Y., Mursalin, M., Xu, W., & Lo, B. (2018). Automated epileptic seizure detection by analyzing wearable EEG signals using extended correlation-based feature selection. In 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN) (pp. 66-69). IEEE. https://doi.org/10.1109/BSN.2018.8329660

Abstract

Electroencephalogram (EEG) that measures the electrical activity of the brain has been widely employed for diagnosing epilepsy which is one kind of brain abnormalities. With the advancement of low-cost wearable brain-computer interface devices, it is possible to monitor EEG for epileptic seizure detection in daily use. However, it is still challenging to develop seizure classification algorithms with a considerable higher accuracy and lower complexity. In this study, we propose a lightweight method which can reduce the number of features for a multiclass classification to identify three different seizure statuses (i.e., Healthy, Interictal and Epileptic seizure) through EEG signals with a wearable EEG sensors using Extended Correlation-Based Feature Selection (ECFS). More specifically, there are three steps in our proposed approach. Firstly, the EEG signals were segmented into five frequency bands and secondly, we extract the features while the unnecessary feature space was eliminated by developing the ECFS method. Finally, the features were fed into five different classification algorithms, including Random Forest, Support Vector Machine, Logistic Model Trees, RBF Network and Multilayer Perceptron. Experimental results have shown that Logistic Model Trees provides the highest accuracy of 97.6% comparing to other classifiers.

DOI

10.1109/BSN.2018.8329660

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