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.
1. Chapter 1 : Introduction: Engineering research in neurological disorders - p. Page xi 2. Chapter 2 : Concepts of EEG analysis and seizures - p. Page 22 3. Chapter 3 : Seizure detection methods and analysis - p. Page 49 4. Chapter 4 : Pattern adapted wavelet-based seizure prediction - p. Page 99 5. Chapter 5 : Effective method for epileptic and nonepileptic seizure classification - p. Page 122 6. Chapter 6 : Concluding remarks and future scope - p. Page 144 7. Index - p. Page 154
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.
1. Chapter 1 : Introduction: Engineering research in neurological disorders - p. Page xi 2. Chapter 2 : Concepts of EEG analysis and seizures - p. Page 22 3. Chapter 3 : Seizure detection methods and analysis - p. Page 49 4. Chapter 4 : Pattern adapted wavelet-based seizure prediction - p. Page 99 5. Chapter 5 : Effective method for epileptic and nonepileptic seizure classification - p. Page 122 6. Chapter 6 : Concluding remarks and future scope - p. Page 144 7. Index - p. Page 154
