Abstract: Aiming at the problem that the automatic epileptic seizure detection algorithm focused on the establishment of a detection model for single patient, and the generalization ability was weak, we proposed an automatic epileptic seizure detection algorithm for non-specific patient based on machine learning. The algorithm used the electroencephalography (EEG) data of multiple epileptic patients, analyzed the characteristics of EEG data after preprocessing the data, and then selected the characteristics to train an automatic epileptic seizure detection model for non-specific patients. The algorithm did not need to establish a separate detection model for each patient, it could detect epilepsy in different patients with only one detection model. The accuracy, sensitivity and specificity of the algorithm are 0.877 4,0.854 8 and 0.9, respectively.

Key words: seizure detection, machine learning, electroencephalography data, filter, feature extraction, feature selection