Seizure detection by analyzing the number of channels selected by cross-correlation using TUH EEG seizure corpus

Status epilepticus is caused by a seizure lasting more than 5 minutes or several seizures in this time. For the detection of seizures, encephalograms are visually analyzed by doctors, but this has certain limitations, which can be reduced using algorithms that allow the identification of seizure patterns. Usually, the algorithms use all the channels of the electroencephalography, which causes more computational time. Therefore, the paper proposes an algorithm that seeks to verify that the use of fewer channels chosen for having less cross-correlation can lead to better seizure detection metrics. Of the classification algorithms used, XGBoost is the one that shows a more noticeable difference in sensitivity between 3 channels (80.64%) and 22 channels (78.19%). Also, ”FP1-F7”, ”A1-T3”, ”P3-O1” and ”FP1-F3” are the best channels for seizure detection. Research showed that using fewer channels selected by cross-correlation can improve seizure detection.