Using Haralick features of the gamma band (30-60 Hz) from EEG data achieved a maximum area under the curve (AUC) of 0.96 for classifying between epileptic seizures and healthy states.
Does using Haralick features of the Gamma band (30-60 Hz) from EEG data accurately detect epileptical seizures?
Using Haralick features from only the high-frequency gamma band of EEG data provides high accuracy (AUC 0.96) for seizure detection while reducing computational load.
Effect estimate: AUC 0.96
In this study, gamma band (30-60 Hz) is used for detection of epileptical seizures using Haralick features. Most of the previous methods are based on the whole frequency spectrum for detection. This work use only high frequency electroencephalogram (EEG) subband for seizure detection using image descriptors. To convert one dimensional EEG data into image Short-time Fourier transform (STFT) has been used. Gamma band is cut from the time frequency (t-f) plane and Haralick features is used as image descriptors to fed in the decision tree classifier. The results have been evaluated using receiver operating characteristic (ROC) analysis. Maximum area under curve (AUC) of 0.96 is obtained to classify between seizures and healthy. Advantage of this work is rather using whole frequency band it utilizes only a particular band which reduces computational load. It also shows the utility of gamma band in seizure detection.
Sameer et al. (Sat,) conducted a other in Epileptical Seizures. Gamma band (30-60 Hz) Haralick features was evaluated on Classification between seizures and healthy states (AUC 0.96). Using Haralick features of the gamma band (30-60 Hz) from EEG data achieved a maximum area under the curve (AUC) of 0.96 for classifying between epileptic seizures and healthy states.