Biomedical Engineering - Research Publications

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Author: Cook, Mark × Type: Conference Paper ×

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    Model Parameter Estimation As Features to Predict the Duration of Epileptic Seizures From Onset.
    Liu, Y ; Xia, S ; Soto-Breceda, A ; Karoly, P ; Cook, MJ ; Grayden, DB ; Schmidt, D ; Kuhlmann, L (IEEE, 2023-07)
    The durations of epileptic seizures are linked to severity and risk for patients. It is unclear if the spatiotemporal evolution of a seizure has any relationship with its duration. Understanding such mechanisms may help reveal treatments for reducing the duration of a seizure. Here, we present a novel method to predict whether a seizure is going to be short or long at its onset using features that can be interpreted in the parameter space of a brain model. The parameters of a Jansen-Rit neural mass model were tracked given intracranial electroencephalography (iEEG) signals, and were processed as time series features using MINIROCKET. By analysing 2954 seizures from 10 patients, patient-specific classifiers were built to predict if a seizure would be short or long given 7 s of iEEG at seizure onset. The method achieved an area under the receiver operating characteristic curve (AUC) greater than 0.6 for five of 10 patients. The behaviour in the parameter space has shown different mechanisms are associated with short/long seizures.Clinical relevance-This shows that it is possible to classify whether a seizure will be short or long based on its early characteristics. Timely interventions and treatments can be applied if the duration of the seizures can be predicted.
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    Evidence of Onset and Sustained Neural Responses to Isolated Phonemes from Intracranial Recordings in a Voice-based Cursor Control Task
    Meng, K ; Lee, S-H ; Goodarzy, F ; Vogrin, S ; Cook, MJ ; Lee, S-W ; Grayden, DB (ISCA-INT SPEECH COMMUNICATION ASSOC, 2022)
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    Implementation of a closed-loop BCI system for real-time speech synthesis under clinical constraints
    Meng, K ; Kim, E ; Vogrin, S ; Cook, MJ ; Goodarzy, F ; Grayden, DB ; Chung, CK (IEEE, 2022)
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    A Neural Mass Model of Spontaneous Burst Suppression and Epileptic Seizures
    Freestone, DR ; Nesic, D ; Jafarian, A ; Cook, MJ ; Grayden, DB (IEEE, 2013)
    The paper presents a neural mass model that is capable of simulating the transition to and from various forms of paroxysmal activity such as burst suppression and epileptic seizure-like waveforms. These events occur without changing parameters in the model. The model is based on existing neural mass models, with the addition of feedback of fast dynamics to create slowly time varying parameters, or slow states. The goal of this research is to establish a link between system properties that modulate neural activity and the fast changing dynamics, such as membrane potentials and firing rates that can be manipulated using electrical stimulation. Establishing this link is likely to be a necessary component of a closed-loop system for feedback control of pathological neural activity.