Identification of Fatal Familial Insomnia Sleep Disorder Using Ga Optimization with Svm Classifier
Sudha Ramya Karri1, Daisy Rani Alli2, Annepu Bhujanga Rao3
1Sudha Ramya Karri*, Lecturer, Department of EIE, Govt Polytechnic for Women, Srikakulam, AP, India
2Daisy Rani Alli, Assistant Professor, Dept. of IT, Andhra University, Visakhapatnam, AP, India.
3Annepu Bhujanga Rao, Professor, Dept. of IT, Andhra University, Visakhapatnam, AP, India.
Manuscript received on December 16, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 651-657 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8308019320/2020©BEIESP | DOI: 10.35940/ijitee.C8308.019320
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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: Among the genetic disorders in brain Fatal familial insomnia (FFI) is one of the rare disorder. The inability of sleeping is called as FFI which leads to the process of becoming progressively worse mentally and physically. The recordings of brain functioning is known as Electroencephalogram (EEG) and which seeks a vital role in observing and finding the sleep disorders like FFI. The EEG senses the brain functionality. In this paper , genetic optimization based SVM classifier is used to identify the sleep disorder. The proposed technique is used to optimize the features which are obtained from features reduction techniques like PCA, ICA and LDA. For performing the experimental results Physio Net database is obtained. Preprocessing of the data, performing feature reduction, next feature optimization and then classification using Support vector machine is the process for identifying the FFI. The performance measures such as accuracy, sensitivity, specificity, F1-Score, Recall are computed . The comparison of results obtained using different condition are observed and the ranges are determined for normal class, Bruxism class and FFI class.
Keywords: EEG Signals, Insomnia Sleep Disorder, Genetic Algorithm, SVM Classifier.
Scope of the Article: Algorithm Engineering