Name: Efficient Automatic Techniques for Detection of Neurological Disorders (en Inglés)
SKU: 57970442
Price: 83640.00 CLP
Availability: InStock
Author: Anand, Satyajit ; Jaiswal, Sandeep
ISBN: 9786203471489

portada Efficient Automatic Techniques for Detection of Neurological Disorders (en Inglés)

Efficient Automatic Techniques for Detection of Neurological Disorders (en Inglés)

Formato

Libro Físico

Autor

Satyajit Anand

Sandeep Jaiswal

Editorial

LAP Lambert Academic Publishing

Idioma

Inglés

N° páginas

168

Encuadernación

Tapa Blanda

Dimensiones

22.9x15.2x1 cm

Peso

0.25 kg.

ISBN13

9786203471489

Categorías

Ingeniería electrónica

Efficient Automatic Techniques for Detection of Neurological Disorders (en Inglés)

Satyajit Anand (Autor) · Sandeep Jaiswal (Autor) · LAP Lambert Academic Publishing · Tapa Blanda

Libro Nuevo Importado

Envío: 21 a 25 días háb.

$ 167.270$ 83.640

-50%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 10 unidades

$ 83.640

Llega entre el 16 Oct y el 22 Oct a Santiago, Región Metropolitana. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Efficient Automatic Techniques for Detection of Neurological Disorders (en Inglés)"

Electroencephalography (EEG) is used to measure the electric potentials which indicate electrical activity of the subject's brain. The EEG is commonly used by clinicians, researchers, and scientists for studying how the brain works and for identifying brain diseases. The EEG is one of the most vital tools which is used in the study of the patient brain's electrical activity for diagnosing the brain abnormalities like head injury, epilepsy, brain stroke, brain tumors, dementia, sleep disorders, and constant monitoring of the measurement of anesthesia during operation. The work is carried out in this thesis primarily concentrates on how various brain activities of EEG signals can be classified for analyzing several brain neurological diseases. For this, we have proposed methods and algorithms for two-class classification as well as multiclass classification of the EEG signals from different brain activities. The proposed techniques/algorithms are compared with earlier reported work on the same dataset to examine the performance of those algorithms/techniques in terms of classification accuracy.