Detecting fast-ripples on both micro- and macro-electrodes in epilepsy: a wavelet-based CNN detector.

Abstract

Background: Fast-ripples (FR) are short (~10 ms) high-frequency oscillations (HFO) between 200-600Hz that are helpful in epilepsy to identify the epileptogenic zone. Our aim is to propose a new method to detect FR that had to be efficient for intracerebral EEG (iEEG) recorded from both usual clinical macro-contacts (millimeter scale) and microwires (micrometer scale).

New method: Step 1 of the detection method is based on a convolutional neural network (CNN) trained using a large database of >11,000 FR recorded from the iEEG of 38 patients with epilepsy from both macro-contacts and microwires. The FR and non-FR events were fed to the CNN as normalized time-frequency maps. Step 2 is based on feature-based control techniques in order to reject false positives. In step 3, the human is reinstated in the decision-making process for final validation using a graphical user interface.

Results: WALFRID achieved high performance on the realistically simulated data with sensitivity up to 99.95% and precision up to 96.51%. The detector was able to adapt to both macro and micro-EEG recordings. The real data was used without any pre-processing step such as artefact rejection. The precision of the automatic detection was of 57.5. Step 3 helped eliminating remaining false positives in a few minutes per subject.

Comparison with existing methods: WALFRID performed as well or better than 6 other existing methods.

Conclusion: Since WALFRID was created to mimic the work-up of the neurologist, clinicians can easily use, understand, interpret and, if necessary, correct the output.