We have designed and developed several computer algorithms designed to monitor the spatiotemporal dynamics of the EEC signal and predict seizures. Preliminary data indicates that some algorithms can predict seizures with at least 80% sensitivity (SENS) with a false-prediction rate (FPR) of approximately 0.125 per hour. We propose to evaluate the on-line real-time performance of the algorithm which performed best in our preliminary studies. The algorithm monitors EEG signals from each scalp electrode, iteratively calculates the degree of chaos (values of STLmax) from 10.24 second epochs of signal from each electrode, automatically selects sites likely to participate in the next preictal transition, and predicts a seizure when a preictal transition is detected. It is incorporated into Dynamical Seizure Warning System (DSWS) consisting of a C++ program running on a PC, networked to the clinical recording systems in the Epilepsy Monitoring Unit.
The Specific Aims of the project are: (1) determine SENS and FPR of the DSWS when used on-line in real time in long-term EEG recordings with scalp and with intracranial depth electrodes, (2) test the hypothesis that the on-line, real-time performance of the DSWS is better than statistically-based naive prediction algorithms, and (3) evaluate the cause of failed predictions and false predictions generated by the DSWS. This study is designed primarily to evaluate the performance of DSWS in inpatient monitoring applications. However, the results will also be useful for evaluating the potential of this algorithm for other applications, including closed-loop therapeutic devices for the vagal nerve stimulation, or deep brain stimulations. For example, it is likely that such stimulators are more effective if stimulation in activated prior to an impending seizure, and may reduce the amount of stimulation required for seizure control. Information obtained in this study will provide information as to the feasibility of such devices as well as provide information that will be useful in the design of such devices.
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| Kelly, K M; Shiau, D S; Kern, R T et al. (2010) Assessment of a scalp EEG-based automated seizure detection system. Clin Neurophysiol 121:1832-43 |
| DuBois, J M; Boylan, L S; Shiyko, M et al. (2010) Seizure prediction and recall. Epilepsy Behav 18:106-9 |
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| Tayal, A H; Tian, M; Kelly, K M et al. (2008) Atrial fibrillation detected by mobile cardiac outpatient telemetry in cryptogenic TIA or stroke. Neurology 71:1696-701 |
| Kharlamov, Elena A; Downey, Kathy L; Jukkola, Peter I et al. (2008) Expression of GABA A receptor alpha1 subunit mRNA and protein in rat neocortex following photothrombotic infarction. Brain Res 1210:29-38 |
| Nair, S P; Jukkola, P I; Quigley, M et al. (2008) ABSENCE SEIZURES AS RESETTING MECHANISMS OF BRAIN DYNAMICS. Cybern Syst Anal 44:664-672 |
| Liu, Chang-Chia; Pardalos, Panos M; Chaovalitwongse, W Art et al. (2008) Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains. J Comb Optim 15:276-286 |
| Kelly, Kevin M (2008) Thrombin: is it on a par with seizures and epilepsy? Epilepsy Curr 8:110-2 |
| Talathi, Sachin S; Hwang, Dong-Uk; Spano, Mark L et al. (2008) Non-parametric early seizure detection in an animal model of temporal lobe epilepsy. J Neural Eng 5:85-98 |
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