Electrical and Electronic Engineering - Research Publications
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ItemINFERRING PATIENT-SPECIFIC PHYSIOLOGICAL PARAMETERS FROM INTRACRANIAL EEG: APPLICATION TO CLINICAL DATA(WILEY-BLACKWELL, 2012-09-01)Purpose: Intracranial EEG (iEEG) provides information regarding where and when seizures occur, whilst the underlying mechanisms are hidden. However physiologically plausible mechanisms for seizure generation and termination are explained by neural mass models, which describe the macroscopic neural dynamics. Fusion of models with patient-specific data allows estimation and tracking of the normally hidden physiological parameters. By monitoring changes in physiology, a new understanding of seizures can be achieved. This work addresses model-data fusion for iEEG for application in a clinical setting. Method: Data was recorded from three patients undergoing evaluation for epilepsy-related surgery at St. Vincent's Hospital, Melbourne. Using this data, we created patient-specific neural mass mathematical models based on the formulation of Jansen and Rit (1995). The parameters that were estimated include the synaptic gains, time constants, and the firing threshold. The estimation algorithm utilized the Unscented Kalman Filter (Julier and Uhlmann, 1997). Result: We demonstrate how parameters changed in relation to seizure initiation, evolution and termination. We also show within-patient (across different seizures) and between-patient specificity of the parameter estimates. Conclusion: The fusion of clinical data and mathematical models can be used to infer valuable information about the underlying mechanisms of epileptic seizure generation. This information could be used to develop novel therapeutic strategies
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ItemINFERRING PATIENT-SPECIFIC PHYSIOLOGICAL PARAMETERS FROM INTRACRANIAL EEG: THEORETICAL STUDIES(WILEY-BLACKWELL, 2012-09)