3 research outputs found
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
Deep Learning for scalp High Frequency Oscillations Identification
Since last 2 decades, High Frequency Oscillations (HFOs) are studied as a
promising biomarker to localize the epileptogenic zone of patients with
refractory focal epilepsy. As HFOs visual detection is time consuming and
subjective, automatization of HFO detection is required. Most HFO detectors
were developed on invasive electroencephalograms (iEEG) whereas scalp
electroencephalograms (EEG) are used in clinical routine. In order HFO
detection can benefit to more patients, scalp HFO detectors has to be
developed. However, HFOs identification in scalp EEG is more challenging than
in iEEG since scalp HFOs are of lower rate, lower amplitude and more likely to
be corrupted by several sources of artifacts than iEEG HFOs. The main goal of
this study is to explore the ability of deep learning architecture to identify
scalp HFOs from the remaining EEG signal. Hence, a binary classification
Convolutional Neural Network (CNN) is learned to analyze High Density
Electroencephalograms (HD-EEG). EEG signals are first mapped into a 2D
time-frequency image, several color definitions are then used as an input for
the CNN. Experimental results show that deep learning allows simple end-to-end
learning of preprocessing, feature extraction and classification modules while
reaching competitive performance