Medicine (St Vincent's) - 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, 2012-09-01)
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ItemIdentification of a Neural Mass Model of Burst Suppression(IEEE, 2019)Burst suppression includes alternating patterns of silent and fast spike activities in neuronal activities observable in micro to macro scale recordings. Biological models of burst suppression are given as dynamical systems with slow and fast states. The aim of this paper is to give a method to identify parameters of a mesoscopic model of burst suppression that can provide insights into study underlying generators of intracranial electroencephalogram (iEEG) data. An optimisation technique based upon a genetic algorithm (GA) is employed to find feasible model parameters to replicate burst patterns in the iEEG data with paroxysmal transitions. Then, a continuous discrete unscented Kalman filter (CD-UKF) is used to infer hidden states of the model and to enhance the identification results from the GA. The results show promise in finding the model parameters of a partially observed mesoscopic model of burst suppression.
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ItemNo Preview AvailableEndoscopic features of buried Barrett's mucosa: visible to the trained eye?(Wiley, 2019-12-01)
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ItemA case of actinic granuloma responding to oral retinoids(WILEY, 2019-05-01)
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ItemRisk factors for injury in a community-treated cohort of patients with epilepsy in Australia(WILEY, 2019-03-01)OBJECTIVE: There remains a paucity of knowledge regarding specific epilepsy-related risk factors for accidents and injuries in people with epilepsy. Injury studies in people with epilepsy are overrepresented, with tertiary based populations that are prone to bias from severe disease. This study aims to assess the contribution of epilepsy-related risk factors to injuries in a community-based cohort. METHODS: We performed a retrospective nested case-control study on patients recruited into the Tasmanian Epilepsy Register (TER) from July 1, 2001 to June 30, 2002. The TER is a community-based cohort of patients with epilepsy in Tasmania, Australia, recruited from the national prescription database and interviewed for epilepsy diagnosis, injuries, and risk factors using validated questionnaires with diagnosis made by an epilepsy specialist. The primary outcome measures were lifetime and recent 12-month injury. Multivariate logistic regression with multiple imputation modeling responder nondisclosure was performed, adjusting for age, gender, region, socioeconomic status, seizure frequency, and epilepsy duration. RESULTS: A total of 819 patients with epilepsy were included in this study. Ten percent of patients experienced an injury in the preceding year. Before adjusting for seizure frequency, any seizure over the past 12 months was associated with recent injury (adjusted odds ratio [OR] = 7.90, 95% confidence interval [CI] = 4.17-14.96). Impaired awareness, cluster seizures, sleep-only seizures, and convulsive seizure were characteristics found to significantly influence injuries irrespective of seizure frequency. Although a warning appeared initially protective for recent injuries (OR = 0.39, 95% CI = 0.22-0.69), this was entirely explained by seizure frequency, with the effect becoming nonsignificant. SIGNIFICANCE: Likely due to their unpredictable nature, seizures expose patients with epilepsy to a high risk of life-threatening injury. These findings emphasize the importance of seizure freedom for maximizing the safety of patients with epilepsy.
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