Epilepsy Early Warning

An 'Early Warning' Device to Allow Epilepsy Patients to Live a More Normal Life

Goal: 
To develop a wearable 'early warning' device for epilepsy patients using advanced machine learning technology
Early Warning Device

The goal of the proposed research is to develop a wearable "early warning" device
attached to an implantable microelectrode array that will give otherwise untreatable
epilepsy patients enough time to take a medicine or prepare for the seizure (e.g. get out of
the pool, pull the car over to side of the road or get off a ladder or stairs). The device
would use detector software based on advanced machine learning technology to detect an
impending seizure. The learning system would be trained with data from the implanted

Online High Frequency Oscillation Detection

Goal: 
To develop a combination of hardware and software to automatically detect High Frequency Oscillations (HFOs) in real-time and in a clinical setting
Online High Frequency Oscillation Detection

High frequency oscillations (HFOs), or brief bursts in the high gamma band (80-500 Hz), have been studied as potential biomarkers of epileptic activity. Since the early 1990's, it has been recognized that increased high gamma power is present within the epileptogenic region at seizure onset in adults (Allen, Fish et al. 1992; Alarcon, Binnie et al. 1995) and children (Fisher, Webber et al. 1992; Traub, Whittington et al. 2001). Interictal fast ripples (Figure 1) have been detected almost exclusively in epileptogenic regions (Staba, Wilson et al. 2002; Jacobs, Levan et al.

EEGMine: A Distributed Framework for Learning on iEEG Data

Goal: 
This project aims to develop a distributed framework for Data Management and Machine Learning on iEEG data obtained from Epilepsy patients
Distributed Data Mining (DDM) on iEEG data

Sample of EEG Graph from a patient

Project Twiki link: 
https://power.ldeo.columbia.edu/twiki/bin/view/SeizurePrediction/EEGMineDocs
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