A Hybrid and Automated Epileptic Seizure Detection Model using Improved PSO and Extreme Learning ANFIS Network
Sumant Kumar Mohapatra1, Biswa Ranjan Swain2, Madhusmita Mohanty3
1Sumant Kumar Mohapatra*, Assistant Professor, Department of Electronics and Telecommunication Engineering, Trident Academy of Technology, B.P.U.T, Bhubaneswar, Odisha, India.
2Biswa Ranjan Swain, Assistant Professor, Department of Electronics and Telecommunication Engineering, Trident Academy of Technology, B.P.U.T, Bhubaneswar, Odisha, India.
3Madhusmita Mohanty, Department of Electronics and Telecommunication Engineering, Trident Academy of Technology, B.P.U.T, Bhubaneswar, Odisha, India.
Manuscript received on December 12, 2019. | Revised Manuscript received on December 25, 2019. | Manuscript published on January 10, 2020. | PP: 2950-2957 | Volume-9 Issue-3, January 2020. | Retrieval Number: C9126019320/2020©BEIESP | DOI: 10.35940/ijitee.C9126.019320
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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: The main aim of the proposed work is to generate an accurate automated seizure detection model for the performance evaluation of the improvement on epileptic patients in an improved manner. Long data sets of EEG signals are recorded for a long duration of time which has taken from PhysioNet CHB-MIT EEG datset for this experimental work. Six types of elements are excerpted from EEG signals by using WPT method and which is then classified by using CFS method. Then, all the features are combinely inputted to the rule based twin- support vector machines (TSVMs ) to detect normal, ictal and pre-ictal EEG segments. The developed seizure detection WPT-KWMTSVM method achieved excellent performance with the average Accuracy, specificity, sensitivity, G-mean, positive predictive value, and Mathews correlation coefficients are 97.14%, 97.33%, 97.00%, 97.31%, 96.85%, 95.96% respectively The average area under curve (AUC) is approximately 1. The proposed method is able to enhance the seizure detection outcomes for proper clinical diagnosis in medical applications.
Keywords: EEG Signal, Epileptic Seizure, IPSO WPT, ELANFIS, MLPNN, RBFNN, ANFIS
Scope of the Article: Smart Learning and Innovative Education Systems