LAUSR

Epilepsy is known as a heterogeneous neurological disorder affecting 1 to 3 percent of the worldwide population. Epileptic seizures occur when brain cells feature abnormal synchronized recurrent activities. In this study, the recurrence characteristics of multichannel scalp electroencephalography (EEG) signals are extracted using Heterogeneous Recurrence Analysis (HRA) to investigate seizure phenomena. For this aim, imaged-EEGs are made using time, frequency, and statistical elementary features extracted from 2-second epochs. Each recording's channel-set provides the ground rule for placing feature values in the imaged-EEGs. Applying HRA method to imaged-EEGs extracts temporal recurrent features from successive epochs among the neighboring channels. Despite existing methods using each individual channel's characteristics as features for each epoch, this method can provide spatial heterogeneous recurrence information for each region of the image, consequently regions of the brain. Our method was evaluated using two publicly available datasets recorded from pediatric patients at Boston Children's Hospital (CHB-MIT) and American university of Beirut Medical Center (ABMC). Considering only temporal detection of seizures, the averaged evaluation parameters are 99.6% accuracy, 99.7% sensitivity, 99.4% specificity on 24 patients of CHB-MIT dataset, and 98.5% accuracy, 97.9% sensitivity, and 98.5% specificity on 6 patients of ABMC. The results show that the accuracy and specificity of the proposed method are comparable to the best machine learning baseline methods while the sensitivity is better. Besides good classification results, HRA on imaged-EEGs can give us valuable information about the patient's brain lobe/s in which recurrent features are distinctive for seizure detection.