Name: Comprehensive Analysis of Extreme Learning Machine and Continuous Genetic Algorithm for Robust Classification of Epilepsy From eeg Signals (en Inglés)
SKU: 62474064
Price: 38.77 USD
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Author: Harikumar Rajaguru; Sunil Kumar Prabhakar

portada Comprehensive Analysis of Extreme Learning Machine and Continuous Genetic Algorithm for Robust Classification of Epilepsy From eeg Signals (en Inglés)

Comprehensive Analysis of Extreme Learning Machine and Continuous Genetic Algorithm for Robust Classification of Epilepsy From eeg Signals (en Inglés)

Formato

Libro Físico

Editorial

Anchor Academic Publishing

Autor

Harikumar Rajaguru; Sunil Kumar Prabhakar

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9783960670995

Comprehensive Analysis of Extreme Learning Machine and Continuous Genetic Algorithm for Robust Classification of Epilepsy From eeg Signals (en Inglés)

Harikumar Rajaguru; Sunil Kumar Prabhakar (Autor) · Anchor Academic Publishing · Tapa Blanda

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Reseña del libro "Comprehensive Analysis of Extreme Learning Machine and Continuous Genetic Algorithm for Robust Classification of Epilepsy From eeg Signals (en Inglés)"

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.