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Autor(en): 
  • Harikumar Rajaguru
  • Sunil Kumar Prabhakar
  • Comprehensive Analysis of Extreme Learning Machine and Continuous Genetic Algorithm for Robust Classification of Epilepsy from EEG Signals 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 5-10 Tagen versandfertig
    Veröffentlichung:  Januar 2017  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Continuous GA / Epilepsy / epileptic seizure / extreme learning machine / independent component analysis / Informatik / Principal Component Analysis / singular value decomposition
    ISBN:  9783960670995 
    EAN-Code: 
    9783960670995 
    Verlag:  Anchor Academic Publishing 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 220 mm / B 155 mm / D 3 mm 
    Gewicht:  74 gr 
    Seiten:  36 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Epilepsy is a common and diverse set of chronic neurological disorders characterized by seizures. It is a paroxysmal behavioral spell generally caused by an excessive disorderly discharge of cortical nerve cells of the brain. Epilepsy is marked by the term ¿epileptic seizures¿. Epileptic seizures result from abnormal, excessive or hyper-synchronous neuronal activity in the brain. About 50 million people worldwide have epilepsy, and nearly 80% of epilepsy occurs in developing countries. The most common way to interfere with epilepsy is to analyse the EEG (electroencephalogram) signal which is a non-invasive, multi channel recording of the brain¿s electrical activity. It is also essential to classify the risk levels of epilepsy so that the diagnosis can be made easier. This study investigates the possibility of Extreme Learning Machine (ELM) and Continuous GA as a post classifier for detecting and classifying epilepsy of various risk levels from the EEG signals. Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used for dimensionality reduction.

      



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