The provided text consists of author submission guidelines for River Publishers and does not contain any clinical study data or findings.
Gamma and beta EEG subbands provide high accuracy (AUC 0.98 and 0.96, respectively) for detecting epileptic seizures using a Naïve Bayes classifier.
This paper presents analysis of Electroencephalograms (EEGs) and subbands (delta, theta, alpha, beta, gamma) using image descriptors for epileptic seizure detection. Short-time Fourier transform (STFT) has been utilized to convert 1-D EEG data into image. All subbands are separated from the time-frequency (t-f) matrix and Haralick features of each subband is fed in the Naïve Bayes (NB) classifier. Receiver operating characteristic (ROC) analysis has been used for performance evaluation of classifier. Among all subbands, gamma band alone shows a maximum AUC of 0.98 to classify between ictal and healthy class, while beta band shows a maximum AUC of 0.96 to differentiate between ictal and interictal class. Significance of this work is it shows the medical advantage of different subbands for the detection process.
Sameer et al. (Wed,) reported a other. The provided text consists of author submission guidelines for River Publishers and does not contain any clinical study data or findings.