Articoli correlati a EEG Signal Classification Using Hidden Markov Model
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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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EEG Signal Classification Using Hidden Markov Model
Editore:
LAP LAMBERT Academic Publishing Nov 2018, 2018
ISBN 10: 6139961327
ISBN 13: 9786139961320
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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). 60 pp. Englisch. Codice articolo 9786139961320
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EEG Signal Classification Using Hidden Markov Model
Editore:
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139961327
ISBN 13: 9786139961320
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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). Codice articolo 9786139961320
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EEG Signal Classification Using Hidden Markov Model
Editore:
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139961327
ISBN 13: 9786139961320
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Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Arunachalam VigneshkumarVigneshkumar Arunachalam was awarded M.E in CSE from Anna University Chennai in April 2013.He has 5 Years of teaching experience at college level. Currently he is Assistant Professor CSE Department at Bannari. Codice articolo 261256864
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EEG Signal Classification Using Hidden Markov Model
Editore:
LAP LAMBERT Academic Publishing Nov 2018, 2018
ISBN 10: 6139961327
ISBN 13: 9786139961320
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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).VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 60 pp. Englisch. Codice articolo 9786139961320
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EEG Signal Classification Using Hidden Markov Model
Editore:
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139961327
ISBN 13: 9786139961320
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Da: preigu, Osnabrück, Germania
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Taschenbuch. Condizione: Neu. EEG Signal Classification Using Hidden Markov Model | Vigneshkumar Arunachalam (u. a.) | Taschenbuch | 60 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139961320 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Codice articolo 115133367
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