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Brain Seizure Detection and Classification Using EEG Signals
- 1st Edition - September 9, 2021
- Authors: Varsha K. Harpale, Vinayak Bairagi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 1 1 2 0 - 7
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 1 1 2 1 - 4
Brain Seizure Detection and Classification Using Electroencephalographic Signals presents EEG signal processing and analysis with high performance feature extraction. The book cove… Read more
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Request a sales quoteBrain 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
Biomedical Engineers and researchers in neural engineering and EEG signal processing Students, researchers and clinicians in neuroscience, neurologists, and neurosurgeons. Corporate market for researchers and industries developing EEG instruments and technology
1 INTRODUCTION1.1 Introduction1.2 Methods of Brain Analysis1.4 Recent Trends in EEG Analysis for Feature Extraction1.5 Trends in Artifact Removal and FPGA Architecture Designs1.6 Seizure Detection Techniques1.7 Seizure Prediction Techniques1.8 Classification of Epileptic and Non Epileptic Seizures1.9 Risk Analysis of Seizures1.10 Summary
2 EEG Processing and Artifact Removal2.1 Behavior Measurement and Recording of Brain Signals2.2 EEG Processing and Mathematical Model2.2.1 Types of EEG Based on Placement of Electrodes2.2.2 Mathematical Model of EEG signals2.3 Artifacts Observed in EEG Signals2.3.1 Independent Component Analysis (ICA)2.3.2 Concept of ICA2.3.3 Flow of EEGLAB Tool2.4 Epilepsy2.4.1 Basics of Seizures2.4.2 Types of Seizures2.5 Summary
3 SEIZURE DETECTION3.1 EEG Feature Extractions3.1.1 Time Domain Features3.1.2 Frequency Domain Features3.1.3 Time-Frequency Analysis of EEG Signal3.2 Proposed Seizure Detection Method3.2.1 Dataset3.3 Feature Extraction for Seizure Detection3.3.1 Time and Frequency Domain features3.3.2 Feature extraction using Discrete Wavelet Transform3.3.3 Features extracted using Empirical Mode Decomposition3.3.4 Feature Extraction by Singular Spectrum Empirical Mode Decomposition (SSEMD)3.3.5 Feature Selection and Feature Vector Construction3.4 Classification Methods3.5 Hardware Architecture for Seizure Detection3.5.1 Genetic Algorithm (GA) for Dimensionality Reduction3.5.2 Genetic Algorithm (GA) implementation on FPGA3.6 Result and Performance Analysis of Seizure Detection3.6.1 Seizure Detection Results3.7 Summary
4 SEIZURE PREDICTION4.1 Introduction4.2 Recent Trends In Fuzzy Classifier Of Epileptic Seizure Detection4.3 Dataset4.4 Feature Extraction4.4.1 Features Selected from Time and Frequency Domain4.4.2 Feature Extraction Using Pattern Adapted Continuous Wavelet Transform4.4.3 Feature Selection4.5 Classification Model for Pre-seizure, Seizure and Normal State4.5.1 Fuzzy Logic model4.6 Feature Extraction For Pre-Seizure Identification4.6.1 Features Selection ANOVA4.7 Performance Evaluation4.7.1 Pre-Seizure Detection For Seizure Prediction4.8 Summary
5 SEIZURE CLASSIFICATION5.1 Introduction5.2 Dataset5.3 Seizure Classification5.3.1 Feature Extraction using DWT, EMD and EWT5.3.2 Methodology Proposed for Effective Classification of Seizures5.4 Classification Using Machine Learning Classifiers5.5 Risk Analysis Model5.6 Result and Discussion5.7 Summary
6 RESULT AND DISCUSSION6.1 Introduction6.2 Seizure Detection Results6.3 Seizure Prediction Results6.4 Seizure Classification Results6.5 Conclusion6.6 Future Research Directions
- No. of pages: 176
- Language: English
- Edition: 1
- Published: September 9, 2021
- Imprint: Academic Press
- Paperback ISBN: 9780323911207
- eBook ISBN: 9780323911214
VH
Varsha K. Harpale
Dr. Mrs. Varsha K. Harpale, is working as academician in Pimpri Chinchwad College of Engg. Pune Maharashtra, India. Her work profile is Associate Professor in E&TC and Associate Dean Quality Assurance for maintaining and improving quality of education in autonomous institute. She has completed her PhD in biomedical signal processing. She has 20 years of teaching experiences, 2 books , 4 copyrights, 2 patents, best paper awards and 30+ quality publication on her credits. She is currently working as member secretary of IEEE Signal Processing Society, Pune Chapter.
Affiliations and expertise
Assistant Professor, Department of Engineering and Technology, Pimpri Chinchwad College of Engineering, SSPU University, Pune, IndiaVB
Vinayak Bairagi
Dr. Vinayak K. Bairagi, is a recognized PhD guide in Savitribai Phule Pune University. He is working as Professor at Department of E electronics and Telecommunication Engg. and actively working as Chairman, IEEE Signal Processing Society Pune Chapter. He has teaching experience of 14 years and research experience of 10 years. He has filed 12 patents and 5 copyrights in technical field. He has published more than 70 papers. He has received IEI national level Young Engineer Award (2014) and ISTE national level Young Researcher Award (2015) for his excellence in the field of engineering. He also has 5 books and 6 book chapters on his credits. His area of interest is Biomedical Signal Processing and Brain Imaging.
Affiliations and expertise
PhD Mentor in Electronics Engineering, Savitribai Phule Pune University, Pune, Maharashtra, IndiaRead Brain Seizure Detection and Classification Using EEG Signals on ScienceDirect