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portada EEG Signal Classification Using Hidden Markov Model
Type
Physical Book
Author
Publisher
Language
English
Pages
60
Format
Paperback
Dimensions
22.9x15.2x0.4 cm
Weight
0.10 kg.
ISBN13
9786139961320
Categories

EEG Signal Classification Using Hidden Markov Model

Vigneshkumar Arunachalam (Author) · Harikumar Rajaguru (Author) · Ganesh Babu Chidambaram (Author) · LAP Lambert Academic Publishing · Paperback

EEG Signal Classification Using Hidden Markov Model - Arunachalam, Vigneshkumar ; Rajaguru, Harikumar ; Babu Chidambaram, Ganesh

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NT$ 1,553

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Synopsis "EEG Signal Classification Using Hidden Markov Model"

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).

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