Our proposed system obtained the highest classification accuracy (CACC) of 78.4% and 79.34% during training and evaluation using the SVM classifier. The dataset is the most substantial EEG data publicly available, which contains an EEG recording of 2130 distinct subjects. In the proposed method, we have used a single-channel EEG dataset of Temple University Hospital.
The different combinations of the extracted features were supplied to various classifiers for the classification of normal and abnormal EEG signals. Subsequently, fuzzy entropy, logarithmic of the squared norm, and fractal dimension are computed for each SB. Using the wavelet decomposition, we obtain subbands (SBs) of EEG signals. Larry walks you through Normal Normal Variant, Normal Sleep, Normal Peds, and Normal Neonatal before taking you through Abnormal Adult, Peds, and Neonate EEG pattern recognition. Children with a normal EEG were younger than those with. The proposed methodology focuses on automated detection of epilepsy using a novel stop-band energy (SBE) minimized orthogonal wavelet filter bank. One hundred and seventy-five children were included in the study. It may lead to an erroneous classification of EEGs. Visual inspection of the EEG signal by observing a change in frequency or amplitude in long-duration signals is an arduous task for the clinicians. However, the interpretation of a particular type of abnormality using the EEG signal is a subjective affair and may vary from clinician-to-clinician. Electroencephalograms (EEG) are widely used to detect epilepsy accurately. Epilepsy is a neural disorder that is associated with the central nervous system (CNS) in which the brain activity sometimes becomes abnormal, which may lead to seizures, loss of awareness, unusual sensations, and behavior.