Anomaly Detection on Electroencephalography with Self-supervised Learning

Epilepsy is one of the most common neurological diseases in humans, and electroencephalography (EEG) is the most widely used method for clinicians to detect epileptic seizures. However, it is error-prone to detect epileptic seizures by manually observing EEG, and labeling epilepsy data is an expensive and time-consuming process. In this study, without requiring any epileptic EEG data and only based on normal EEGs, a new selfsupervised learning method is proposed for anomaly detection on EEG signals. In particular, a series of scaling transformations are performed on the original EEG data to generated self-labeled scaled EEG data, where different labels correspond to different scaling transformations. Then using the self-labeled normal EEG dataset, a multi-class classifier can be trained to accurately predict the scaling transformations on new normal EEG data, but not accurately on abnormal (epileptic) EEGs. The inconsistency between the predicted scaling transformations and the groundtruth scaling transformations can then be used to measure the degree of abnormality in a new EEG data. Comprehensive experimental evaluations demonstrate that the proposed self-supervised method outperforms classic anomaly detection methods including one-class support vector machine (SVM) and autoencoders. The robustness of the proposed method also has been empirically proved with different classifier structures and by varying relevant hyper-parameters.