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.
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