Medicine (St Vincent's) - Research Publications
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ItemNo Preview AvailableModel Parameter Estimation As Features to Predict the Duration of Epileptic Seizures From Onset.(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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ItemA circle criterion observer for estimating the unmeasured membrane potential of neuronal populations(IEEE, 2011-12-01)A circle criterion observer is designed for estimating the unmeasured membrane potential of neuronal populations using the electroencephalogram (EEG) from a class of parameterised models that replicates patterns seen on the EEG. Compared to existing similar designs, we provide a less conservative linear matrix inequality (LMI) condition that is shown to be fulfilled for the neural models we consider. The designed observer is robust towards disturbances in the input and measurement, as well as model uncertainty. We show that the observer can be designed for a model that reproduces alpha rhythms in the EEG as an illustrative example.
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ItemParameter and state estimation for a class of neural mass models(IEEE, 2012-01-01)We present an adaptive observer which asymptotically reconstructs the parameters and states of a model of interconnected cortical columns. Our study is motivated by the fact that the considered model is able to realistically reproduce patterns seen on (intracranial) electroencephalograms (EEG) by varying its parameters. Therefore, by estimating its parameters and states, we could gain a better understanding of the mechanisms underlying neurological phenomena such as seizures, which might lead to the prediction of the onsets of epileptic seizures. Simulations are performed to illustrate our results.
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ItemA nonlinear estimator for the activity of neuronal populations in the hippocampus(IFAC - International Federation of Automatic Control, 2011-01-01)We present an estimator design to reconstruct the mean membrane potential of individual neuronal populations from a single channel simulated electroencephalographic signal based on a model of the hippocampus. The robustness of the estimator against variations in the synaptic gains of the neuronal populations and disturbances in the input and measurement is studied. Our results are further illustrated in simulations.
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ItemState and parameter estimation of nonlinear systems: a multi-observer approach(IEEE, 2014)We present a multi-observer approach for the parameter and state estimation of continuous-time nonlinear systems. For nominal parameter values in the known parameter set, state observers are designed with a robustness property. At any time instant, one observer is selected by a given criterion to provide its state estimate and its corresponding nominal parameter value. Provided that a persistency of excitation condition holds, we guarantee the convergence of state and parameter estimates up to a given margin of error which can be reduced by increasing the number of observers. The potential computational burden of the scheme is eased by introducing a dynamic parameter re-sampling technique, where the nominal parameter values are iteratively updated using a zoom-in procedure on the parameter set. We illustrate the efficacy of the algorithm on a model of neural dynamics.