Analysis of Sampling Techniques Towards Epileptic Seizure Detection from Imbalanced Dataset

Epileptic Seizure is a neurological disorder that occurs due to abnormal activities of brain neurons. Epilepsy can affect patients' health and can lead to life-threatening emergencies. Predicting epilepsy before the initiation of the onset is highly effective to avoid the seizure by medication. machine learning algorithms have been implemented in classifying epilepsy from Electroencephalograms (EEG) data. Machine learning algorithms experience reduced performance when classes are imbalanced. The imbalance class problem can be handled by different sampling techniques including oversampling minority class, undersampling majority class, and combined of both. In this paper, both traditional and state-of-the-art sampling techniques have been experimented and evaluated for their capability of improving the imbalance ratio. A Deep Neural Network (DNN) based framework is also presented in this paper. The classification performance has been investigated by applying several ML approaches with the effect of different balancing ratio. We performed experiments on Epileptic Seizure Recognition dataset. The experimental results show that the sampling techniques improve the imbalance class ratio as well as the classification performance.