Epileptic Focus Localization Based on iEEG by Using Positive Unlabeled (PU) Learning

Abstract

Epilepsy is a chronic disorder of the brain. Intracranial electroencephalogram (iEEG) recorded from cortex is the most popular measurement for not only the diagnosis of epilepsy, but also the focus localization that is crucial for the surgery. In recent years, the machine learning methods have been rapidly developed and applied successfully to various real world problems. Given sufficient number of samples, the powerful deep learning methods can achieve high performance for epileptic focus localization. However, it is a challenging task to obtain large amount of labeled iEEG regarding focal/non-focal channels, since the annotations must be performed by multiple clinical experts through visual judgment on the long term iEEG signals. In order to reduce the necessary number of labeled training samples, we introduce the positive unlabeled (PU) learning method for classification of focal and non-focal epileptic iEEG signals. The proposed method enables us to learn a binary classifier by using small amount of labeled data containing only one class (i.e., focal signals) and unlabeled data containing two classes (i.e., focal and non-focal signals), which greatly reduces the workload of clinical experts for annotations. Experimental results on Bern dataset and iEEG recorded from Juntendo University Hospital demonstrate the effectiveness of our method.