Academic Press Brain Seizure Detection and Classification Using EEG Signals

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. Review Provides the most current and leading-edge research in brain seizure detection as applied to the treatment of paralysis From the Back Cover 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 occurs when too many nerve cells in the brain “fire too quickly causing an “electrical storm. The EEG signals recorded from epileptic patients are analyzed for monitoring extracting behavior of signals during onset seizures. Epileptic seizure detection still poses challenges in the field of accurate seizure detection and prediction of seizures. Mostly these techniques are analyzed on the basis of detection and classification accuracy, sensitivity and specificity. Brain Seizure Detection and Classification Using EEG Signals presents EEG signal processing and analysis with high performance feature extraction. The Time and Frequency Domain (TFD), Wavelet Transform (WT) and Empirical Mode Decomposition (EMD) are optimized feature extraction methods presented by the authors. The book also covers the feature selection method based on One-way ANOVA along with high performance machine learning classifiers for 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 will be compared with existing methods of feature extraction using Wavelet Transform (WT) and Empirical Wavelet Transform (EWT). The machine learning classifiers are used for classification of EEG signal in normal, epileptic seizure, non-epileptic seizure, and thus non-epileptic patients. One of the major new contributions of the book is identification of non-epileptic patients using SSEWT. About the Author 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 wor