KNN Classifier and K-Means Clustering for Robust Classification of Epilepsy from EEG Signals. A Detailed Analysis

Rajaguru, Harikumar; Prabhakar, Sunil Kumar

ISBN 10: 3960671407 / ISBN 13: 9783960671404

Edité par Anchor Academic Publishing, 2017

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In.

N° de réf. du vendeur ria9783960671404_new

Détails bibliographiques

• Titre: KNN Classifier and K-Means Clustering for Robust Classification of Epilepsy from EEG Signals. A Detailed Analysis
• Éditeur: Anchor Academic Publishing
• Date de parution: 2017
• Langue: anglais
• ISBN à 10 chiffres: 3960671407
• ISBN à 13 chiffres: 9783960671404
• Reliure: Couverture souple
• État de l'article: New

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Synopsis

Epilepsy is a chronic disorder, the hallmark of which is recurrent, unprovoked seizures. Many people with epilepsy have more than one type of seizures and may have other symptoms of neurological problems as well. Epilepsy is caused due to sudden recurrent firing of the neurons in the brain. The symptoms are convulsions, dizziness and confusion. One out of every hundred persons experiences a seizure at some time in their lives. It may be confused with other events like strokes or migraines. Unfortunately, the occurrence of an epileptic seizure seems unpredictable and its process still is hardly understood. In India, the number of persons suffering from epilepsy is increasing every year. The complexity involved in the diagnosis and therapy has to be cost effective. In this project, the authors applied an algorithm which is used for a classification of the risk level of epilepsy in epileptic patients from Electroencephalogram (EEG) signals. Dimensionality reduction is done on the EEG dataset by applying Power Spectral density. The KNN Classifier and K-Means clustering is implemented on these spectral values to epilepsy risk level detection. The Performance Index (PI) and Quality Value (QV) are calculated for the above methods. A group of twenty patients with known epilepsy findings are used in this study.