portada EEG Signal Classification Using Hidden Markov Model (en Inglés)

EEG Signal Classification Using Hidden Markov Model (en Inglés)

Formato

Libro Físico

Autor

Vigneshkumar Arunachalam

Harikumar Rajaguru

Ganesh Babu Chidambaram

Editorial

LAP Lambert Academic Publishing

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

22.9x15.2x0.4 cm

Peso

0.10 kg.

ISBN13

9786139961320

Categorías

Tecnología de la información: cuestiones generales

EEG Signal Classification Using Hidden Markov Model (en Inglés)

Vigneshkumar Arunachalam (Autor) · Harikumar Rajaguru (Autor) · Ganesh Babu Chidambaram (Autor) · LAP Lambert Academic Publishing · Tapa Blanda

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Reseña del libro "EEG Signal Classification Using Hidden Markov Model (en Inglés)"

This book emphasize on Epilepsy which is a neurological disorder with preponderance of about 1-2% of the world's population. It is due to excessive synchronization of cortical neuronal networks and is characterized by sudden recurrent and transient disturbances of perception or behavior. Epileptic seizures are classified as partial or focal, generalized, unilateral and unclassified seizures. Focal epileptic seizures involve only part of cerebral hemisphere and in corresponding parts of the body. Generalized epileptic seizures involve the entire brain and produce bilateral motor symptoms usually with loss of consciousness. Both types of epileptic seizures can occur at all ages.One of the most important tools for diagnosing Epilepsy includes monitoring brain activity through the electroencephalogram (EEG). The EEG signature is transient waveform of isolated spikes, spike trains.It also assists in classifying the underlying epileptic syndrome. The feature extraction is done by Independent Component Analysis (ICA) and final risk level classification is done by Hidden Markov Model (HMM). Performance evaluation is done by Kappa function and Mean Square Error (MSE).