Texture analysis based graph approach for automatic detection of neonatal seizure from multi-channel EEG signals

Seizure detection is a particularly difficult task for neurologists to correctly identify the Electroencephalography (EEG)-based neonatal seizures in a visual manner. There is a strong demand to recognize the seizures in more automatic manner. Developing an expert seizure detection system with an acceptable performance level can partly fill this research gap. This paper proposes a new framework for the automated detection of neonatal seizures based on the Morse Wavelet approach that is coupled with a local binary pattern algorithm, and a graphbased community detection algorithm. An ensemble classifier method is designed to detect neonatal seizures prevalent in EEG signals. Our findings show that only 59 of the texture features can exhibit the abnormal increase in an EEG amplitude and the spikes notable during a seizure. The present results demonstrate that the proposed seizure detection model is more accurate for the detection of seizures compared with some of the traditional approaches.