Enhanced Epileptic Seizure Detection using Imbalanced Classification
Prabhsimar Kaur1, Vishal Bharti2, Srabanti Maji3
1Prabhsimar Kaur*, Department of Computer Science and Engineering, DIT University, Dehardun, India.
2Vishal Bharti, Department of Computer Science and Engineering, DIT University, Dehardun, India.
3Srabanti Maji, Department of Computer Science and Engineering, DIT University, Dehardun, India.
Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 2412-2420 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2894059120/2020©BEIESP | DOI: 10.35940/ijrte.A2894.059120
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© 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 the second most persistent neurological condition, endangering the lives of patients. Though there have been many advancements in neurological imaging approaches, the Electroencephalogram (EEG) still remains to be the most effective tool for testing and diagnosing epileptic patients. The visual analytics of EEG signals is a very prolonged process and always open to the subjective judgment of the physicians. The main goal of our study is to build an automatic classifier that can analyze and detect epilepsy from EEG recordings obtained from epileptic and healthy patients, thus helping the neurosurgeons to diagnose epilepsy in a better way. Synthetic minority oversampling technique (SMOTE) has been used for balancing the EEG dataset and the Principal component analysis (PCA) technique is applied further, for reducing the EEG signal dimensionality. For data classification, seven machine learning classifiers have been used and after comparing the results the authors conclude that Artificial Neural Network (ANN), outperforms the other classifiers by providing an accuracy of 97.82%.
Keywords: Epilepsy Detection, Electroencephalogram, oversampling, class imbalance, dimension reduction, optimized parameter.
Scope of the Article: Classification