Classification of Epileptic EEG Signals by Wavelet based CFC
This work addresses the need for accurate seizure detection in EEG signals for medical diagnosis, though it appears incremental by combining wavelet CFC with existing statistical methods.
The paper tackled the problem of classifying epileptic EEG signals by proposing a novel wavelet-based cross-frequency coupling (CFC) approach for feature extraction, achieving high accuracy in distinguishing ictal seizures from regular brain activity as part of a computer-aided diagnosis system.
Electroencephalogram, an influential equipment for analyzing humans activities and recognition of seizure attacks can play a crucial role in designing accurate systems which can distinguish ictal seizures from regular brain alertness, since it is the first step towards accomplishing a high accuracy computer aided diagnosis system (CAD). In this article a novel approach for classification of ictal signals with wavelet based cross frequency coupling (CFC) is suggested. After extracting features by wavelet based CFC, optimal features have been selected by t-test and quadratic discriminant analysis (QDA) have completed the Classification.