Characterization of In Vivo Biosignal Dynamics as Quantitative Biomarkers of SUDEP
Prevention of sudden unexpected death in epilepsy (SUDEP) is complicated by the inability to accurately predict which patients are at risk. Many SUDEP risk factors have been proposed based on demographic (e.g., early onset epilepsy, generalized tonic-clonic seizures, and male sex) and physiological (e.g., heart rate variability and suppression of brain activity after seizures) parameters. Unfortunately, these risk factors have been shown to have limited predictive value as many patients with low risk profiles still die of SUDEP, while some patients with higher risk profiles do not. This project seeks to identify reliable biomarkers to predict SUDEP risk utilizing innovative mathematical/dynamical analyses of simultaneous recordings of brain, heart and respiratory activity. A two gene model of human SUDEP, the Scn2a, Kcna1 double mutant mouse, will be used towards this goal. Biosignal analysis in SUDEP is currently very limited and restricted to analysis of either short-term electroencephalographic (EEG) or electrocardiographic (ECG) signals. However, our preliminary results show that the study of the interactions and associations between biosignals could be the key for the development of reliable SUDEP biomarkers. Such developed biomarkers may help shed light on the underlying mechanisms of SUDEP and transform the clinical treatment of epilepsy by pinpointing patients at risk of SUDEP and allowing for optimized therapeutic intervention.
Principal Investigator: Iasemidis, Leonidas Ph.D. -- Biomedical Engineering/CBERS
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