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Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

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Published on: June 6, 2015

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

Ludovic Gardy¹, Jonathan Curot², Luc Valton²

¹Centre de Recherche Cerveau et Cognition (CerCo, CNRS UMR5549), Toulouse 31300, France; Université Paul Sabatier, Toulouse 31300, France; Ecole Nationale de l'Aviation Civile, (ENAC), Toulouse 31300, France.

Journal of Neuroscience Methods

|December 15, 2024

Summary

A new method, WALFRID, efficiently detects fast-ripples (FR) in epilepsy using convolutional neural networks (CNN) and human validation. This tool aids neurologists in identifying the epileptogenic zone from intracerebral EEG data.

Keywords:

CNN Epilepsy Fast-ripples HFO hybrid electrodes iEEG

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Published on: December 6, 2016

Area of Science:

Neuroscience
Medical Technology

Background:

Fast-ripples (FR) are high-frequency oscillations (200-600 Hz) crucial for identifying the epileptogenic zone in epilepsy.
Detecting FR in intracerebral EEG (iEEG) is challenging due to varying recording scales (macro- and microwires).

Purpose of the Study:

To develop and validate a novel, efficient method for detecting fast-ripples (FR) in intracerebral EEG (iEEG).
To ensure the method's effectiveness across both macro- and microwire iEEG recordings.

Main Methods:

A convolutional neural network (CNN) was trained on over 11,000 FR events from 38 epilepsy patients.
The detection pipeline includes CNN-based event identification, feature-based false positive control, and human validation via a GUI.
The method, WALFRID, processes raw iEEG data without pre-processing steps like artifact rejection.

Main Results:

WALFRID demonstrated high performance with up to 99.95% sensitivity and 96.51% precision on simulated data.
The detector successfully adapted to both macro- and microwire iEEG recordings.
Human validation effectively eliminated remaining false positives, requiring minimal time per subject.

Conclusions:

WALFRID offers a user-friendly tool for neurologists, mimicking their analytical workflow.
The method is easily usable, understandable, and correctable by clinicians.
WALFRID performs comparably or superiorly to existing FR detection methods.