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Epileptic Seizure Detection Using Multimodal Sensor Data and Machine Learning

Epileptic Seizure Detection Using Multimodal Sensor Data and Machine Learning
Author: Alexandra P. Hamlin
Publisher:
Total Pages: 342
Release: 2019
Genre:
ISBN:
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Over 65 million people worldwide suffer from epilepsy, and one third of these people have refractory epilepsy, which means typical epilepsy medications have not worked for them. Many patients who suffer from epilepsy are unaware of their seizures, yet doctors rely on reports by patients and families to assess the effectiveness of treatment. Studies have shown that patients do not remember more than half of their seizures, yet seizure journals are the most common method used to track seizure activity in outpatient settings. In epilepsy monitoring units, patients are monitored using combined video and electroencephalography (EEG) recordings. This is considered the gold standard of epilepsy monitoring, but is impractical for use outside of a hospital setting. Several studies have investigated using one or several other methods to track specific types of seizures, but not have been able to reliably detect all types of seizures. This thesis investigates the use of a suite of non-cerebral, i.e., non-EEG sensors that provide time series data with supervised machine learning to detect seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 unrecorded, mean age 36.17), five of which had a total of seven seizures. The patients were monitored in the inpatient Epilepsy Monitoring Unit at the Dartmouth Hitchcock Medical Center, while wearing sensors including electrocardiography, electrodermal activity, electromyography, accelerometry, and audio data. Post-processing of the data shows that seizure data are separable from non-seizure data by using linear discriminant analysis on features derived from the signals recorded. The mean area under the ROC curve calculated using linear discriminant analysis for each patient was .9682. A detailed study was conducted to identify the features and sensors that contribute most significantly to separability of data acquired during seizures from other data. Subsequently, a real-time approach was evaluated using a discriminative classifier. A support vector machine was able to classify seizure and non-seizure data with high accuracy. The results presented show that this multimodal approach to seizure detection is promising. Moving forward, more data will be collected and other machine learning techniques will be investigated to increase accuracy in real-time seizure detection.

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection
Author: Sandeep Kumar Satapathy
Publisher: Academic Press
Total Pages: 134
Release: 2019-02-10
Genre: Medical
ISBN: 0128174277
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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

Translational Machine Learning for Epilepsy Therapy

Translational Machine Learning for Epilepsy Therapy
Author: Steven Baldassano
Publisher:
Total Pages: 0
Release: 2017
Genre:
ISBN:
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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.

Brain Seizure Detection and Classification Using EEG Signals

Brain Seizure Detection and Classification Using EEG Signals
Author: Varsha K. Harpale
Publisher: Academic Press
Total Pages: 176
Release: 2021-09-09
Genre: Science
ISBN: 0323911218
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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

Advances in Computer Vision and Computational Biology

Advances in Computer Vision and Computational Biology
Author: Hamid R. Arabnia
Publisher: Springer Nature
Total Pages: 903
Release: 2021-08-05
Genre: Technology & Engineering
ISBN: 3030710513
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The book presents the proceedings of four conferences: The 24th International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'20), The 6th International Conference on Health Informatics and Medical Systems (HIMS'20), The 21st International Conference on Bioinformatics & Computational Biology (BIOCOMP'20), and The 6th International Conference on Biomedical Engineering and Sciences (BIOENG'20). The conferences took place in Las Vegas, NV, USA, July 27-30, 2020, and are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Authors include academics, researchers, professionals, and students. Presents the proceedings of four conferences as part of the 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20); Includes the tracks on Image Processing, Computer Vision, & Pattern Recognition, Health Informatics & Medical Systems, Bioinformatics, Computational Biology & Biomedical Engineering; Features papers from IPCV'20, HIMS'20, BIOCOMP'20, and BIOENG'20.

Epileptic Seizure Prediction Using Electroencephalogram Signals

Epileptic Seizure Prediction Using Electroencephalogram Signals
Author: Ratnaprabha Ravindra Borhade
Publisher:
Total Pages: 0
Release: 2024-10
Genre: Medical
ISBN: 9781032725932
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"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"--

EEG SIGNAL PROCESSING: A Machine Learning Based Framework

EEG SIGNAL PROCESSING: A Machine Learning Based Framework
Author: R. John Martin
Publisher: Ashok Yakkaldevi
Total Pages: 139
Release: 2022-01-31
Genre: Art
ISBN: 1678180068
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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.