Download or read book Comprehensive Analysis of Extreme Learning Machine and Continuous Genetic Algorithm for Robust Classification of Epilepsy from EEG Signals written by Harikumar Rajaguru and published by Anchor Academic Publishing. This book was released on 2017 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: Epilepsy is a common and diverse set of chronic neurological disorders characterized by seizures. It is a paroxysmal behavioral spell generally caused by an excessive disorderly discharge of cortical nerve cells of the brain. Epilepsy is marked by the term “epileptic seizures”. Epileptic seizures result from abnormal, excessive or hyper-synchronous neuronal activity in the brain. About 50 million people worldwide have epilepsy, and nearly 80% of epilepsy occurs in developing countries. The most common way to interfere with epilepsy is to analyse the EEG (electroencephalogram) signal which is a non-invasive, multi channel recording of the brain’s electrical activity. It is also essential to classify the risk levels of epilepsy so that the diagnosis can be made easier. This study investigates the possibility of Extreme Learning Machine (ELM) and Continuous GA as a post classifier for detecting and classifying epilepsy of various risk levels from the EEG signals. Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used for dimensionality reduction.
Download or read book KNN Classifier and K Means Clustering for Robust Classification of Epilepsy from EEG Signals A Detailed Analysis written by Harikumar Rajaguru and published by Anchor Academic Publishing. This book was released on 2017-05 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 migraines. Unfortunately, the occurrence of an epileptic seizure seems unpredictable and its process still is hardly understood. In India, the number of persons suffering from epilepsy is increasing every year. The complexity involved in the diagnosis and therapy has to be cost effective. In this project, the authors applied an algorithm which is used for a classification of the risk level of epilepsy in epileptic patients from Electroencephalogram (EEG) signals. Dimensionality reduction is done on the EEG dataset by applying Power Spectral density. The KNN Classifier and K-Means clustering is implemented on these spectral values to epilepsy risk level detection. The Performance Index (PI) and Quality Value (QV) are calculated for the above methods. A group of twenty patients with known epilepsy findings are used in this study.
Download or read book EEG Brain Signal Classification for Epileptic Seizure Disorder Detection written by Sandeep Kumar Satapathy and published by Academic Press. This book was released on 2019-02-10 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book EEG Signal Analysis and Classification written by Siuly Siuly and published by Springer. This book was released on 2017-01-03 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents advanced methodologies in two areas related to electroencephalogram (EEG) signals: detection of epileptic seizures and identification of mental states in brain computer interface (BCI) systems. The proposed methods enable the extraction of this vital information from EEG signals in order to accurately detect abnormalities revealed by the EEG. New methods will relieve the time-consuming and error-prone practices that are currently in use. Common signal processing methodologies include wavelet transformation and Fourier transformation, but these methods are not capable of managing the size of EEG data. Addressing the issue, this book examines new EEG signal analysis approaches with a combination of statistical techniques (e.g. random sampling, optimum allocation) and machine learning methods. The developed methods provide better results than the existing methods. The book also offers applications of the developed methodologies that have been tested on several real-time benchmark databases. This book concludes with thoughts on the future of the field and anticipated research challenges. It gives new direction to the field of analysis and classification of EEG signals through these more efficient methodologies. Researchers and experts will benefit from its suggested improvements to the current computer-aided based diagnostic systems for the precise analysis and management of EEG signals. /div
Download or read book Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals written by Harikumar Rajaguru and published by Anchor Academic Publishing. This book was released on 2017-03 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes’ rule in which it is the probability based theorem. The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG.
Download or read book Brain Seizure Detection and Classification Using EEG Signals written by Varsha K. Harpale and published by Academic Press. This book was released on 2021-09-09 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brain Seizure Detection and Classification Using Electroencephalographic Signals presents EEG signal processing and analysis with high performance feature extraction. The book covers the feature selection method based on One-way ANOVA, along with high performance machine learning classifiers for the classification of EEG signals in normal and epileptic EEG signals. In addition, the authors also present new methods of feature extraction, including Singular Spectrum-Empirical Wavelet Transform (SSEWT) for improved classification of seizures in significant seizure-types, specifically epileptic and Non-Epileptic Seizures (NES). The performance of the system is compared with existing methods of feature extraction using Wavelet Transform (WT) and Empirical Wavelet Transform (EWT). The book's objective 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. Presents EEG signal processing and analysis concepts with high performance feature extraction Discusses recent trends in seizure detection, prediction and classification methodologies Helps classify epileptic and non-epileptic seizures where misdiagnosis may lead to the unnecessary use of antiepileptic medication Provides new guidance and technical discussions on feature-extraction methods and feature selection methods based on One-way ANOVA, along with high performance machine learning classifiers for classification of EEG signals in normal and epileptic EEG signals, and new methods of feature extraction developed by the authors, including Singular Spectrum-Empirical Wavelet
Download or read book Fuzzy Genetic Algorithms Svm Methods for Epilepsy Classification written by Harikumar Rajaguru and published by LAP Lambert Academic Publishing. This book was released on 2012-05 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Epileptic seizures result from a sudden electrical disturbance to the brain. Approximately one in every 100 persons will experience a seizure at some time in their life. In this work, we propose a genetic algorithm, SVM based fuzzy knowledge integration framework that is used for classification of risk level of epilepsy in diabetic patients from Electroencephalogram (EEG) signals. A statistical analysis of the EEG signal to indicate the onset of epilepsy based on chi square tests and control limits. Ten known diabetic patients with raw EEG recording are studied. Chapter 1 introduces the features of EEG signals and focus of the research. Chapter 2 discusses about Statistical analysis and quantification of Diabetic epilepsy risk through Chi-square tests. Chapter 3 reviews the fundamentals of fuzzy systems. Chapter 4 enumerates the Genetic algorithms for optimization of epilepsy risk levels. SVM techniques as a post classifier for epilepsy detection are discussed in Chapter 5. Results are discussed in Chapter 6. Chapter 7 brings out the conclusion. Chapter 8 shows the Future scope. This monograph is useful for all Engineering undergraduate, graduates students and practicing engineers.
Download or read book Analysis and Classification of EEG Signals for Brain Computer Interfaces written by Szczepan Paszkiel and published by Springer Nature. This book was released on 2019-08-31 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the problem of EEG signal analysis and the need to classify it for practical use in many sample implementations of brain–computer interfaces. In addition, it offers a wealth of information, ranging from the description of data acquisition methods in the field of human brain work, to the use of Moore–Penrose pseudo inversion to reconstruct the EEG signal and the LORETA method to locate sources of EEG signal generation for the needs of BCI technology. In turn, the book explores the use of neural networks for the classification of changes in the EEG signal based on facial expressions. Further topics touch on machine learning, deep learning, and neural networks. The book also includes dedicated implementation chapters on the use of brain–computer technology in the field of mobile robot control based on Python and the LabVIEW environment. In closing, it discusses the problem of the correlation between brain–computer technology and virtual reality technology.
Download or read book Brain Computer Interface written by Narayan Panigrahi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book discusses electroencephalogram (EEG) signal processing using effective methodology and algorithms. It provides a basic introduction on EEG; classification of different components presents in EEG and helps reader to understand the scope of processing EEG signal and its associated applications. Further, it covers specific aspects such as Epilepsy detection, exploitation of P300 for various application, design of an EEG acquisition system, detection of saccade Fix and Blink from EEG and Ego data. Features: Explains the basis of Brain Computer Interface and how it can be established using different EEG signal characteristics. Covers the detailed classification of different types of EEG signals with respect to their physical characteristics. Explains detection and diagnosis of Epileptic seizure from EEG data of a subject. Review's design and development a low cost and robust EEG acquisition system. Provides mathematical analysis of EEG including MATLAB® codes for students to experiment with EEG data. This book aims at Graduate students and Researchers in Biomedical, Electrical, Electronics & Communication Engineering, and Health Cyber Physical Systems"--
Download or read book EEG Signal Processing and Machine Learning written by Saeid Sanei and published by John Wiley & Sons. This book was released on 2021-09-27 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: EEG Signal Processing and Machine Learning Explore cutting edge techniques at the forefront of electroencephalogram research and artificial intelligence from leading voices in the field The newly revised Second Edition of EEG Signal Processing and Machine Learning delivers an inclusive and thorough exploration of new techniques and outcomes in electroencephalogram (EEG) research in the areas of analysis, processing, and decision making about a variety of brain states, abnormalities, and disorders using advanced signal processing and machine learning techniques. The book content is substantially increased upon that of the first edition and, while it retains what made the first edition so popular, is composed of more than 50% new material. The distinguished authors have included new material on tensors for EEG analysis and sensor fusion, as well as new chapters on mental fatigue, sleep, seizure, neurodevelopmental diseases, BCI, and psychiatric abnormalities. In addition to including a comprehensive chapter on machine learning, machine learning applications have been added to almost all the chapters. Moreover, multimodal brain screening, such as EEG-fMRI, and brain connectivity have been included as two new chapters in this new edition. Readers will also benefit from the inclusion of: A thorough introduction to EEGs, including neural activities, action potentials, EEG generation, brain rhythms, and EEG recording and measurement An exploration of brain waves, including their generation, recording, and instrumentation, abnormal EEG patterns and the effects of ageing and mental disorders A treatment of mathematical models for normal and abnormal EEGs Discussions of the fundamentals of EEG signal processing, including statistical properties, linear and nonlinear systems, frequency domain approaches, tensor factorization, diffusion adaptive filtering, deep neural networks, and complex-valued signal processing Perfect for biomedical engineers, neuroscientists, neurophysiologists, psychiatrists, engineers, students and researchers in the above areas, the Second Edition of EEG Signal Processing and Machine Learning will also earn a place in the libraries of undergraduate and postgraduate students studying Biomedical Engineering, Neuroscience and Epileptology.
Download or read book EEG Signal Processing written by Saeid Sanei and published by John Wiley & Sons. This book was released on 2013-05-28 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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, providing expansive coverage of algorithms and tools from the field of digital signal processing. It discusses their applications to medical data, using graphs and topographic images to show simulation results that assess the efficacy of the methods. Additionally, expect to find: explanations of the significance of EEG signal analysis and processing (with examples) and a useful theoretical and mathematical background for the analysis and processing of EEG signals; an exploration of normal and abnormal EEGs, neurological symptoms and diagnostic information, and representations of the EEGs; reviews of theoretical approaches in EEG modelling, such as restoration, enhancement, segmentation, and the removal of different internal and external artefacts from the EEG and ERP (event-related potential) signals; coverage of major abnormalities such as seizure, and mental illnesses such as dementia, schizophrenia, and Alzheimer’s disease, together with their mathematical interpretations from the EEG and ERP signals and sleep phenomenon; descriptions of nonlinear and adaptive digital signal processing techniques for abnormality detection, source localization and brain-computer interfacing using multi-channel EEG data with emphasis on non-invasive techniques, together with future topics for research in the area of EEG signal processing. The information within EEG Signal Processing has the potential to enhance the clinically-related information within EEG signals, thereby aiding physicians and ultimately providing more cost effective, efficient diagnostic tools. It will be beneficial to psychiatrists, neurophysiologists, engineers, and students or researchers in neurosciences. Undergraduate and postgraduate biomedical engineering students and postgraduate epileptology students will also find it a helpful reference.
Download or read book Models of EEG Data Mining and Classification in Temporal Lobe Epilepsy Wavelet chaos neural Network Methodology and Spiking Neural Networks written by Samanwoy Ghosh Dastidar and published by . This book was released on 2007 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: EEG-based epilepsy diagnosis and seizure detection is still in its early experimental stages. In this research, a multi-paradigm approach is advocated, integrating three novel computational paradigms: wavelet transforms, chaos theory, and artificial neural networks.
Download or read book The Analysis of High Frequency Oscillations and Suppression in Epileptic Seizure Data written by Chia-Hung Kuo and published by . This book was released on 2014 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study, several techniques for the variability analysis of electroencephalography (EEG) have been developed with application to the detection of high-frequency oscillations (HFOs) and suppressed EEG classification. EEG refers to the electrical activities in the subdural or cerebral cortex of the brain and can be recorded by several neurophysiological methods, including noninvasive and invasive recordings. EEG in the range of 40-500 Hz, referred to as HFOs, can be recorded in human brains using depth microelectrodes. Various studies in human or animal models supported that HFOs were related to epileptogensis, and evidence was also provided to suggest that neocortical seizures might begin with low-amplitude HFOs. We apply several HFO detection and FFT-based algorithms to 19 IEEG recordings from the Epilepsy Monitoring Unit at University Hospitals Case Medical Center to further verify the connection between HFO activities and clinical seizure onset. Based on the confirmed onset channel from the recordings, the performance of these HFOs detection algorithms has been evaluated in both the time domain and the frequency domain including full band analysis. The frequency band from 35 to 80 Hz with no overlapping of analysis epochs has been selected for further investigations on the full 100/102 channel recordings. Results from various algorithms including Line-Length, Curve-Length, Benchmark and the algorithm proposed in this work indicated quantifiable changes in HFO energy before and after seizure onset. This suggests that HFOs may play an important role in the development of methods for the detection and prediction of epileptic seizures. Post-ictal generalized EEG suppression (PGES) is an Electroencephalographic (EEG) biomarker for risk of Sudden Unexpected Death in Epilepsy (SUDEP). Breathing, movement and muscle artifact can render quantification of PGES difficult. An automated EEG suppression detection algorithm using a genetic algorithm (GA) and adaptive neuro–fuzzy inference system (ANFIS) is proposed for detecting EEG suppression after epileptic seizures. The EEG features for separating the normal and suppressed EEG include short-time energy measurement methods and approximate entropy (ApEn). The ANFIS classifier used the GA to select an optimal feature set as the input for classification. Two types of classifiers, patient specific and patient independent, are developed and tested and the results support that the proposed scheme is a robust approach for the detection of EEG suppression. Two-channel and six-channel classifiers using the short-time energy measurement methods obtained from subdividing the 1-9 Hz EEG frequency band have also been developed and tested.
Download or read book Epileptic Seizure Prediction Using Electroencephalogram Signals written by Manoj Shrikrishna Nagmode and published by . This book was released on 2024-12-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Translational Machine Learning for Epilepsy Therapy written by Steven Baldassano and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Continuous medical data monitoring is playing an increasingly important role in patient care, both in and out the hospital. Diagnosing and treating patients with epilepsy is especially reliant on continuous EEG monitoring to identify and respond to seizures. However, as use of continuous EEG becomes more common, both for long-term inpatient monitoring and in ambulatory or implanted devices, the burden of study interpretation is rapidly outpacing available physician resources. In particular, the advent of implanted neuroresponsive devices for treating medically-refractory epilepsy is generating large, streaming datasets potentially lasting for several years and containing hundreds of seizures. The current need for manual review of long-term, continuous EEG data introduces tremendous health care costs and can result in significant delays in seizure diagnosis and treatment. Automated data processing is essential to improve data usage, accurately and rapidly detect seizures, and provide scalability in clinical practice. This thesis aims to develop platforms for automated data analysis and event detection using custom machine learning algorithms for application in the intensive care unit and in implanted neural devices. The work presented in this thesis progresses through the development of each component of an automated data analysis platform. The first section describes a system for real-time data analysis and caretaker notification in the ICU, with a focus on the process necessary to harness multi-modal data from clinical recording sources. The next section details the process of developing machine learning algorithms for seizure detection. In this section, I present novel seizure detection strategies as well as a competition designed to crowdsource algorithm development. This work produced several highly-accurate, open-source seizure detection methods, validated in extended human implanted device data, along with pipelines to facilitate algorithm application and benchmarking in new datasets. The last section covers the integration of data management and seizure detection for implementation in next-generation medical devices. I present a novel paradigm to leverage cloud computing resources for seizure detection in an implanted device. This system is then implemented in vivo using a canine epilepsy model, with real-time seizure detection on streaming data from Medtronic's RC+S neurostimulating device. These algorithms and flexible analysis platforms are a step toward automating analysis of EEG data for epilepsy therapy. It is my hope that such systems will improve medical data usage, reshape caretaker workflow, and increase the clinical power of continuous medical monitoring.
Download or read book Machine Learning Theory and Applications written by and published by Newnes. This book was released on 2013-05-16 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical learning and analysis techniques have become extremely important today, given the tremendous growth in the size of heterogeneous data collections and the ability to process it even from physically distant locations. Recent advances made in the field of machine learning provide a strong framework for robust learning from the diverse corpora and continue to impact a variety of research problems across multiple scientific disciplines. The aim of this handbook is to familiarize beginners as well as experts with some of the recent techniques in this field.The Handbook is divided in two sections: Theory and Applications, covering machine learning, data analytics, biometrics, document recognition and security. Very relevant to current research challenges faced in various fields Self-contained reference to machine learning Emphasis on applications-oriented techniques
Download or read book Pathological Brain Detection written by Shui-Hua Wang and published by Springer. This book was released on 2018-07-20 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides detailed practical guidelines on how to develop an efficient pathological brain detection system, reflecting the latest advances in the computer-aided diagnosis of structural magnetic resonance brain images. Matlab codes are provided for most of the functions described. In addition, the book equips readers to easily develop the pathological brain detection system further on their own and apply the technologies to other research fields, such as Alzheimer’s detection, multiple sclerosis detection, etc.
