EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

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  • Author : Sandeep Kumar Satapathy
  • Publisher : Academic Press
  • Pages : 134 pages
  • ISBN : 0128174277
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

Download or Read online EEG Brain Signal Classification for Epileptic Seizure Disorder Detection full in PDF, ePub and kindle. this book written by Sandeep Kumar Satapathy and published by Academic Press which was released on 10 February 2019 with total page 134 pages. We cannot guarantee that EEG Brain Signal Classification for Epileptic Seizure Disorder Detection book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques. Chapters present an overview of machine learning techniques and the tools available, discuss previous studies, present empirical studies on the performance of the NN and SVM classifiers, discuss RBF neural networks trained with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final chapter present future developments in the field. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need the most recent and promising automated techniques for EEG classification. Explores machine learning techniques that have been modified and validated for the purpose of EEG signal classification using Discrete Wavelet Transform for the identification of epileptic seizures Encompasses machine learning techniques, providing an easily understood resource for both non-specialized readers and biomedical researchers Provides a number of experimental analyses, with their results discussed and appropriately validated

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection
  • Author : Sandeep Kumar Satapathy,Satchidananda Dehuri,Alok Kumar Jagadev,Shruti Mishra
  • Publisher : Academic Press
  • Release : 10 February 2019
GET THIS BOOK EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques. Chapters present an overview of machine learning techniques and the tools available, discuss previous studies, present empirical studies on the performance of the NN and SVM classifiers, discuss RBF neural networks trained with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final chapter present

Brain Seizure Detection and Classification Using Electroencephalographic Signals

Brain Seizure Detection and Classification Using Electroencephalographic Signals
  • Author : Varsha K. Harpale,Vinayak Bairagi
  • Publisher : Academic Press
  • Release : 15 July 2021
GET THIS BOOK Brain Seizure Detection and Classification Using Electroencephalographic Signals

Electroencephalogram (EEG) remains the most immediate, simple, and rich source of information for understanding phenomena related to brain electrical activities. The objective of the book is to analyze the EEG signals to observe abnormalities of brain activities called epileptic seizure. Seizure is a neurological disorder in which too many neurons are excited at the same time and are triggered by brain injury or by chemical imbalance. The seizures are predominantly characterized by unpredictable interruptions of normal brain function. A seizure

KNN Classifier and K Means Clustering for Robust Classification of Epilepsy from EEG Signals A Detailed Analysis

KNN Classifier and K Means Clustering for Robust Classification of Epilepsy from EEG Signals  A Detailed Analysis
  • Author : Harikumar Rajaguru,Sunil Kumar Prabhakar
  • Publisher : Anchor Academic Publishing
  • Release : 01 May 2017
GET THIS BOOK KNN Classifier and K Means Clustering for Robust Classification of Epilepsy from EEG Signals A Detailed Analysis

Epilepsy is a chronic disorder, the hallmark of which is recurrent, unprovoked seizures. Many people with epilepsy have more than one type of seizures and may have other symptoms of neurological problems as well. Epilepsy is caused due to sudden recurrent firing of the neurons in the brain. The symptoms are convulsions, dizziness and confusion. One out of every hundred persons experiences a seizure at some time in their lives. It may be confused with other events like strokes or

EEG Signal Processing

EEG Signal Processing
  • Author : Saeid Sanei,Jonathon A. Chambers
  • Publisher : John Wiley & Sons
  • Release : 28 May 2013
GET THIS BOOK EEG Signal Processing

Electroencephalograms (EEGs) are becoming increasingly important measurements of brain activity and they have great potential for the diagnosis and treatment of mental and brain diseases and abnormalities. With appropriate interpretation methods they are emerging as a key methodology to satisfy the increasing global demand for more affordable and effective clinical and healthcare services. Developing and understanding advanced signal processing techniques for the analysis of EEG signals is crucial in the area of biomedical research. This book focuses on these techniques,

Databases Theory and Applications

Databases Theory and Applications
  • Author : Muhammad Aamir Cheema,Wenjie Zhang,Lijun Chang
  • Publisher : Springer
  • Release : 20 September 2016
GET THIS BOOK Databases Theory and Applications

This book constitutes the refereed proceedings of the 27th Australasian Database Conference, ADC 2016, held in Sydney, NSW, Australia, in September 2016. The 33 full papers presented together with 11 demo papers were carefully reviewed and selected from 55 submissions. The mission of ADC is to share novel research solutions to problems of today’s information society that fulfill the needs of heterogeneous applications and environments and to identify new issues and directions for future research. The topics of the presented papers are related to

Intelligent Internet of Things

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  • Author : Farshad Firouzi,Krishnendu Chakrabarty,Sani Nassif
  • Publisher : Springer Nature
  • Release : 21 January 2020
GET THIS BOOK Intelligent Internet of Things

This holistic book is an invaluable reference for addressing various practical challenges in architecting and engineering Intelligent IoT and eHealth solutions for industry practitioners, academic and researchers, as well as for engineers involved in product development. The first part provides a comprehensive guide to fundamentals, applications, challenges, technical and economic benefits, and promises of the Internet of Things using examples of real-world applications. It also addresses all important aspects of designing and engineering cutting-edge IoT solutions using a cross-layer approach

Proceedings of the International Conference on Soft Computing for Problem Solving SocProS 2011 December 20 22 2011

Proceedings of the International Conference on Soft Computing for Problem Solving  SocProS 2011  December 20 22  2011
  • Author : Kusum Deep,Atulya Nagar,Millie Pant,Jagdish Chand Bansal
  • Publisher : Springer Science & Business Media
  • Release : 13 April 2012
GET THIS BOOK Proceedings of the International Conference on Soft Computing for Problem Solving SocProS 2011 December 20 22 2011

The objective is to provide the latest developments in the area of soft computing. These are the cutting edge technologies that have immense application in various fields. All the papers will undergo the peer review process to maintain the quality of work.

Automated Epileptic Seizure Onset Detection

Automated Epileptic Seizure Onset Detection
  • Author : Arvind Dorai
  • Publisher : Unknown
  • Release : 21 June 2021
GET THIS BOOK Automated Epileptic Seizure Onset Detection

Epilepsy is a serious neurological disorder characterized by recurrent unprovoked seizures due to abnormal or excessive neuronal activity in the brain. An estimated 50 million people around the world suffer from this condition, and it is classified as the second most serious neurological disease known to humanity, after stroke. With early and accurate detection of seizures, doctors can gain valuable time to administer medications and other such anti-seizure countermeasures to help reduce the damaging effects of this crippling disorder. The time-varying

EMBC 2004

EMBC 2004
  • Author : IEEE Engineering in Medicine and Biology Society. Conference,IEEE Engineering in Medicine and Biology Society
  • Publisher : IEEE Computer Society Press
  • Release : 21 June 2021
GET THIS BOOK EMBC 2004

"IEEE Catalog Number: 04CH37558"--T.p. verso.

Epilepsy

Epilepsy
  • Author : Isam Jaber Al-Zwaini,Ban Adbul-Hameed Majeed Albadri
  • Publisher : BoD – Books on Demand
  • Release : 13 November 2019
GET THIS BOOK Epilepsy

Epilepsy is the most common neurological disorder globally, affecting approximately 50 million people of all ages. It is one of the oldest diseases described in literature from remote ancient civilizations 2000-3000 years ago. Despite its long history and wide spread, epilepsy is still surrounded by myth and prejudice, which can only be overcome with great difficulty. The term epilepsy is derived from the Greek verb epilambanein, which by itself means to be seized and to be overwhelmed by surprise or attack.

Monitoring of Biomedical Systems Using Non stationary Signal Analysis

Monitoring of Biomedical Systems Using Non stationary Signal Analysis
  • Author : Marcus William Musselman
  • Publisher : Unknown
  • Release : 21 June 2021
GET THIS BOOK Monitoring of Biomedical Systems Using Non stationary Signal Analysis

Monitoring of engineered systems consists of characterizing the normal behavior of the system and tracking departures from it. Techniques to monitor a system can be split into two classes based on their use of inputs and outputs of the system. Systems-based monitoring refers to the case when both inputs and outputs of a system are available and utilized. Conversely, symptomatic monitoring refers to the case when only outputs of the system are available. This thesis extended symptomatic and systems-based monitoring

Nonlinear Analysis in Neuroscience and Behavioral Research

Nonlinear Analysis in Neuroscience and Behavioral Research
  • Author : Tobias A. Mattei
  • Publisher : Frontiers Media SA
  • Release : 31 October 2016
GET THIS BOOK Nonlinear Analysis in Neuroscience and Behavioral Research

Although nonlinear dynamics have been mastered by physicists and mathematicians for a long time (as most physical systems are inherently nonlinear in nature), the recent successful application of nonlinear methods to modeling and predicting several evolutionary, ecological, physiological, and biochemical processes has generated great interest and enthusiasm among researchers in computational neuroscience and cognitive psychology. Additionally, in the last years it has been demonstrated that nonlinear analysis can be successfully used to model not only basic cellular and molecular data