Epileptic Seizure Prediction Through Machine Learning and Spatio-Temporal Features Based Time Series Analysis of Intracranial Electroencephalogram Data
N. Ilakiyaselvan1, Hardik Bhatt2, Utkarsh Shukla3, A. Nayeemulla khan4, A. Shahina5
1N. Ilakiyaselvan*, School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India.
2Hardik Bhatt, School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India.
3Utkarsh Shukla, School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India.
4A. Nayeemulla Khan, School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India.
5A. Shahina, Department of Information Technology, SSn College of Engineering, Kalavakkam, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 4763-4770 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2068109119/2019©BEIESP | DOI: 10.35940/ijeat.A2068.109119
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Epilepsy is a group of neurological disorders identifiable by infrequent but recurrent seizures. Seizure prediction is widely recognized as a significant problem in the neuroscience domain. Developing a Brain-Computer Interface (BCI) for seizure prediction can provide an alert to the patient, providing a buffer time to get the necessary emergency medication or at least be able to call for help, thus improving the quality of life of the patients. A considerable number of clinical studies presented evidence of symptoms (patterns) before seizure episodes and thus, there is large research on seizure prediction, however, there is very little existing literature that illustrates the use of structured processes in machine learning for predicting seizures. Limited training data and class imbalance (EEG segments corresponding to preictal phase, the duration just before the seizure, to about an hour prior to the episode, are usually in a tiny minority) are a few challenges that need to be addressed when employing machine learning for this task. In this paper we present a comparative study of various machine learning approaches that can be used for classification of EEG signals into preictal and interictal (Interictal is the time between seizures) using the features extracted from the intracranial EEG. Publicly available data has been used for this purpose for both human and canine subjects. After data pre-processing and extensive feature extraction, different models are trained and are effectively used to analyze the temporal dynamics of the brain (interictal and preictal) in affected subjects. We present the improved results for various classification algorithms, with AUROC values of best classification models at 0.99.
Keywords: Epilepsy, Electroencephalogram, Seizure Prediction, Linear Classifier, Ensemble Classifier, Time series analysis.