Article: SMOTE and ABC optimised RBF network for coping with imbalanced class in EEG signal classification Journal: International Journal of Medical Engineering and Informatics (IJMEI) 2018 Vol.10 No.3 pp.215 - 234 Abstract: This paper proposes a novel approach for coping with imbalanced class problem by combining the best attribute of synthetic minority over-sampling technique (SMOTE) and artificial bee colony optimised radial basis function neural networks to identify epileptic seizure from electroencephalography (EEG) signal. EEG is the recording of electrical activity in brain. Careful analysis of these recordings can provide valuable information and understanding the mechanisms of several brain disorder diseases such as epilepsy. Since epileptic seizures occur irregularly and unpredictably, automatic seizure detection in EEG recordings is highly required. We have used discrete wavelet transform (DWT) technique for extraction of potential features from the signal. For classification of these signals into two classes, we have trained the RBFN by a modified version of ABC algorithm (MABC). In this work, we realise, this two class classification problem is highly imbalanced i.e., the instances in one class known as majority class outnumber the instances of other class called the minority class. The SMOTE is first applied to generate synthetic instances in the positive class to balance the training data set. Using the resulting balanced dataset, the MABC optimised RBF network is then constructed to identify the epileptic seizure. Inderscience Publishers - linking academia, business and industry through research

Title: SMOTE and ABC optimised RBF network for coping with imbalanced class in EEG signal classification

Authors: Sandeep Kumar Satapathy; Satchidananda Dehuri; Alok Kumar Jagadev

Addresses: Department of Computer Science and Engineering, ITER, S'O'A University, Bhubaneswar-751030, Odisha, India ' Department of Information and Communication Technology, Fakir Mohan University, VyasaVihar, Januganj, Balasore-756019, Odisha, India ' School of Computer Engineering, KIIT University, Bhubaneswar, 751024, Odisha, India

Abstract: This paper proposes a novel approach for coping with imbalanced class problem by combining the best attribute of synthetic minority over-sampling technique (SMOTE) and artificial bee colony optimised radial basis function neural networks to identify epileptic seizure from electroencephalography (EEG) signal. EEG is the recording of electrical activity in brain. Careful analysis of these recordings can provide valuable information and understanding the mechanisms of several brain disorder diseases such as epilepsy. Since epileptic seizures occur irregularly and unpredictably, automatic seizure detection in EEG recordings is highly required. We have used discrete wavelet transform (DWT) technique for extraction of potential features from the signal. For classification of these signals into two classes, we have trained the RBFN by a modified version of ABC algorithm (MABC). In this work, we realise, this two class classification problem is highly imbalanced i.e., the instances in one class known as majority class outnumber the instances of other class called the minority class. The SMOTE is first applied to generate synthetic instances in the positive class to balance the training data set. Using the resulting balanced dataset, the MABC optimised RBF network is then constructed to identify the epileptic seizure.

Keywords: electroencephalography; radial basis function neural networks; artificial bee colony; ABC; discrete wavelet transform; DWT; imbalanced class.

DOI: 10.1504/IJMEI.2018.093350

International Journal of Medical Engineering and Informatics, 2018 Vol.10 No.3, pp.215 - 234

Received: 15 Dec 2015
Accepted: 01 Feb 2017
Published online: 25 Jul 2018 *

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Title: SMOTE and ABC optimised RBF network for coping with imbalanced class in EEG signal classification

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