The time-varying dynamics and non-stationarity of epileptic seizures makes their detection difficult. Osono et. al. in ([1]) proposed an adaptable seizure detection algorithm ('SDA'), however, that has had great success. In this presentation, we begin with an overview of the original detection algorithm's architecture, describing its degrees of freedom that provide flexibility and outline a procedure to adapt the method to improve performance. The adaptation consists of generating multiple candidate digital filters using various techniques from signal processing, defining a practical optimization criteria, and using this criteria to select the best filter candidate. Coupled within the procedure is the selection of a corresponding optimal percentile value for use in the nonlinear (order statistic) filtering step that follows in the algorithm. Finally, we discuss how the algorithm has been utilized for closed-loop therapy, in which seizure detections are used to trigger electrical stimulations in the brain designed to prevent the development of a seizure before its disabling effects occur.
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