A Novel Unsupervised Autoencoder-Based HFOs Detector in Intracranial EEG Signals

High frequency oscillations (HFOs) have demonstrated their potency acting as an effective biomarker in epilepsy. However, most of the existing HFOs detectors are based on manual feature extraction and supervised learning, which incur laborious feature selection and time-consuming labeling process. In order to tackle these issues, we propose an automatic unsupervised HFOs detector based on convolutional variational autoencoder (CVAE). First, each selected HFO candidate (via an initial detection method) is converted into a 2-D time-frequency map (TFM) using continuous wavelet transform (CWT). Then, CVAE is trained on the red channel of the TFM (R-TFM) dataset so as to achieve the goal of dimensionality reduction and reconstruction of input feature. The reconstructed R-TFM dataset is later classified by K-means algorithm. Experimental results show that the proposed method outperforms four existing detectors, and achieve 92.85% in accuracy, 93.91% in sensitivity, and 92.14% in specificity.