An Effective Cluster-Based Outlier Detection with Optimized Deep Neural Network for Epileptic Seizure Detection and Classification Model
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International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2021 by IJETT Journal | ||
| Volume-69 Issue-3 |
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| Year of Publication : 2021 | ||
| Authors : P. Suguna, B. Kirubagari, R. Umamaheswari |
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| DOI : 10.14445/22315381/IJETT-V69I3P214 |
Citation
MLA Style: P. Suguna, B. Kirubagari, R. Umamaheswari "An Effective Cluster-Based Outlier Detection with Optimized Deep Neural Network for Epileptic Seizure Detection and Classification Model" International Journal of Engineering Trends and Technology 69.3(2021):76-84.
APA Style:P. Suguna, B. Kirubagari, R. Umamaheswari. An Effective Cluster-Based Outlier Detection with Optimized Deep Neural Network for Epileptic Seizure Detection and Classification Model International Journal of Engineering Trends and Technology, 69(3),76-84.
Abstract
Epileptic seizure diagnosis using Electroencephalogram (EEG) signal is an essential process in the healthcare sector to detect the abnormal growth of brain activities. Since epileptic seizure detection by physicians requires more time, it is needed to design an automated epileptic detection model. Due to the advancements in the Deep Learning (DL) models, it can be employed in the diagnosis of epileptic seizures from EEG signals. This paper presents a new hierarchical clustering with adaptive momentum (ADAM) optimized Deep Neural Network (DNN), called the HC-DNN-ADAM model. The presented model involves HC-based outlier detection to remove the unwanted data from the input dataset; thereby, the classifier results can be improved. In addition, the HC-DNN-ADAM model utilizes DNN for the classification process where the hyperparameters of DNN are tuned by ADAM optimizer. For examining the effectual classifier results analysis of the HC-DNN-ADAM model, a benchmark Epileptic Seizure Recognition dataset is utilized. The application of HC and ADAM paves a way to achieve better classification outcomes. The performance of the HC-DNN-ADAM model has been validated using a benchmark dataset. The simulation outcomes signified that the HC-DNN-ADAMmodel has resulted in maximum detection performance with an accuracy of 99.74% on the classification of multiple classes of seizures.
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Keywords
Data classification, Deep learning, Epileptic seizure, Outlier detection.