Automated Detection of High-Frequency Oscillations in Epilepsy Based on a Convolutional Neural Network

Epilepsy is one of the most common chronic neurological diseases. High-frequency oscillations (HFOs) have emerged as promising biomarkers for the epileptogenic zone. However, visual marking of HFOs is a time-consuming and laborious process. Several automated techniques have been proposed to detect HFOs, yet these are still far from being suitable for application in a clinical setting. Here, ripples and fast ripples from intracranial electroencephalograms were detected in six patients with intractable epilepsy using a convolutional neural network (CNN) method. This approach proved more accurate than using four other HFO detectors integrated in RIPPLELAB, providing a higher sensitivity (77.04% for ripples and 83.23% for fast ripples) and specificity (72.27% for ripples and 79.36% for fast ripples) for HFO detection. Furthermore, for one patient, the Cohen's kappa coefficients comparing automated detection and visual analysis results were 0.541 for ripples and 0.777 for fast ripples. Hence, our automated detector was capable of reliable estimates of ripples and fast ripples with higher sensitivity and specificity than four other HFO detectors. Our detector may be used to assist clinicians in locating epileptogenic zone in the future.