Download or read book An Epileptic Seizure Detection Method from EEG Signals Based on a Classifier Driven Feature Reduction Technique written by Raymond N. Kamel and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Epileptic seizure detection can improve the quality of life of epileptic patients, allow for more accurate medication, and minimize the risk of sudden unexpected death in epilepsy (SUDEP). This thesis work aims to develop a robust and stable algorithm for epileptic seizure detection through the classification of EEG signals. To achieve this aim, a methodology is proposed to develop a classifier that can differentiate between the healthy (normal), interictal, and ictal states of EEG signals, while maximizing the classification accuracy and minimizing the computational redundancy. The main pillar upon which this methodology is designed is using a problem-specific classifier-driven feature reduction technique. This technique involves training a bagged trees ensemble that utilizes the complete set of features extracted from the denoised time-domain and time-frequency domain sub-bands of interest. Based on this ensemble, a predictor importance analysis is conducted to reduce the features fed to the classifier to only those with the highest estimates of importance due to their significant contribution to the classification accuracy. A random forest ensemble is finally trained using the reduced features set to classify the involved signals. The University of Bonn EEG dataset was used for testing and validating the proposed methodology through formulating nine 2-class and two 3-class classification problems using its different signal sets. The classification accuracy achieved on the experimented 11 classification problems ranged between 97.15% ℗ł 1.45% and 100.00% ℗ł 0.00%, and the stability of the developed models were assured through running each model 100 times and analyzing their performance metrics. Developing such an algorithm for the real-time classification would minimize the need for the laborious manual classification of the EEG signals and serve as an accurate seizure detection algorithm for the implantable seizure control devices.
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 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 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 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 Epileptic Seizure Prediction Using Electroencephalogram Signals written by Ratnaprabha Ravindra Borhade and published by . This book was released on 2024-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents an innovative method of EEG-based feature extraction and classification of seizures using EEG signals. It describes the methodology required for EEG analysis, seizure detection, seizure prediction and seizure classification. It contains a compilation of all techniques used in the literature and emphasises on newly proposed techniques. The book concentrates on a brief discussion of existing methods for epileptic seizure diagnosis and prediction and introduces new efficient methods specifically for seizure prediction. Focuses on the mathematical models and machine learning algorithms from a perspective of clinical deployment of EEG-based Epileptic Seizure Prediction Discusses recent trends in seizure detection, prediction and classification methodologies Provides engineering solutions to severity or risk analysis of detected seizures at remote places Presents wearable solutions to seizure prediction Includes details of the use of deep learning for Epileptic Seizure Prediction using EEG This book acts as a reference for academicians and professionals who are working in the field of Computational Biomedical Engineering and are interested in the domain of EEG-based disease prediction"--
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 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 Epileptic Seizures and the EEG written by Andrea Varsavsky and published by CRC Press. This book was released on 2016-04-19 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: A study of epilepsy from an engineering perspective, this volume begins by summarizing the physiology and the fundamental ideas behind the measurement, analysis and modeling of the epileptic brain. It introduces the EEG and provides an explanation of the type of brain activity likely to register in EEG measurements, offering an overview of how these EEG records are and have been analyzed in the past. The book focuses on the problem of seizure detection and surveys the physiologically based dynamic models of brain activity. Finally, it addresses the fundamental question: can seizures be predicted? Based on the authors' extensive research, the book concludes by exploring a range of future possibilities in seizure prediction.
Download or read book Advances in Data Driven Computing and Intelligent Systems written by Swagatam Das and published by Springer Nature. This book was released on with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book EEG SIGNAL PROCESSING A Machine Learning Based Framework written by R. John Martin and published by Ashok Yakkaldevi. This book was released on 2022-01-31 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: 1.1 Motivation Analysis of non-stationary and non-linear nature of signal data is the prime talk in signal processing domain today. On employing biomedical equipments huge volume of physiological data is acquired for analysis and diagnostic purposes. Inferring certain decisions from these signals by manual observation is quite tedious due to artefacts and its time series nature. As large volume of data involved in biomedical signal processing, adopting suitable computational methods is important for analysis. Data Science provides space for processing these signals through machine learning approaches. Many more biomedical signal processing implementations are in place using machine learning methods. This is the inspiration in adopting machine learning approach for analysing EEG signal data for epileptic seizure detection.
Download or read book Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition SoCPaR 2019 written by Ajith Abraham and published by Springer Nature. This book was released on 2020-07-31 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights recent research on soft computing, pattern recognition and biologically inspired computing. It presents 24 selected papers from the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019) and 5 papers from the 11th World Congress on Nature and Biologically Inspired Computing (NaBIC 2019), held at Vardhaman College of Engineering, Hyderabad, India, on December 13–15, 2019. SoCPaR–NaBIC is a premier conference and brings together researchers, engineers and practitioners whose work involves soft computing and bio-inspired computing, as well as their industrial and real-world applications. Including contributions by authors from 15 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.
Download or read book Algorithms for EEG based Monitoring of Epileptic Seizures written by Javad Birjandtalab Golkhatm and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Millions of people around the world suffer from epilepsy. Approximately 0.1 percent of epileptic patients die from unexpected deaths. It is of a great value if technology can provide a method to efficiently monitor the seizures and alert the caregivers to help patients. An Electroencephalography (EEG) signal is able to discover any neuron’s misfiring or excessive neural activity which can be a sign of a neurological disorder. It is proven that EEG signals are the best markers for detection and diagnosis of the epileptic seizures. Frequency domain features (like normalized in-band power spectral density) are known as most informative attributes to extract meaningful information from EEG signals. In this work, we addressed three main challenges in the area of epileptic seizure monitoring. First, we proposed a channel selection method which selects the most informative EEG channels out of full EEG channel set. We embedded high dimensional spectral features into the low dimension space to improve the accuracy seizure detection. Second, we suggested two novel imbalance learning techniques to address the problem of class imbalance in the seizure dataset. Using this approach the classification models can better get trained and learn more from seizure samples. Third, we proposed a personalized seizure prediction methodology to extract footprint of seizure and identify pre-seizure attributes based on each patient’s response that time. Using this approach, the accuracy of seizure prediction is improved since only the most informative portion of pre-seizure data is used for prediction.
Download or read book Machine Learning in Natural Complex Systems written by Andre Gruning and published by Frontiers Media SA. This book was released on 2023-04-11 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Biologically Inspired Techniques in Many Criteria Decision Making written by Satchidananda Dehuri and published by Springer Nature. This book was released on 2022-06-03 with total page 718 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes best-selected, high-quality research papers presented at Second International Conference on Biologically Inspired Techniques in Many Criteria Decision Making (BITMDM 2021) organized by Department of Information & Communication Technology, Fakir Mohan University, Balasore, Odisha, India, during December 20-21, 2021. This proceeding presents the recent advances in techniques which are biologically inspired and their usage in the field of many criteria decision making. The topics covered are biologically inspired algorithms, nature-inspired algorithms, multi-criteria optimization, multi-criteria decision making, data mining, big-data analysis, cloud computing, IOT, machine learning and soft computing, smart technologies, crypt-analysis, cognitive informatics, computational intelligence, artificial intelligence and machine learning, data management exploration and mining, computational intelligence, and signal and image processing.
Download or read book Biomedical Signals Based Computer Aided Diagnosis for Neurological Disorders written by M. Murugappan and published by Springer Nature. This book was released on 2022-06-17 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biomedical signals provide unprecedented insight into abnormal or anomalous neurological conditions. The computer-aided diagnosis (CAD) system plays a key role in detecting neurological abnormalities and improving diagnosis and treatment consistency in medicine. This book covers different aspects of biomedical signals-based systems used in the automatic detection/identification of neurological disorders. Several biomedical signals are introduced and analyzed, including electroencephalogram (EEG), electrocardiogram (ECG), heart rate (HR), magnetoencephalogram (MEG), and electromyogram (EMG). It explains the role of the CAD system in processing biomedical signals and the application to neurological disorder diagnosis. The book provides the basics of biomedical signal processing, optimization methods, and machine learning/deep learning techniques used in designing CAD systems for neurological disorders.
