High Performance Seizure Monitoring and Alert System

DESCRIPTION (provided by applicant): Patients are frequently hospitalized for management of uncontrolled seizures due to epilepsy or acute neurological insults such as trauma, stroke, infections, and a number of toxic and metabolic disorders. Inpatient management of seizures is complicated by the fact that they occur intermittently and unpredictably. Researchers at Optima Neuroscience have developed an automated algorithm to accurately detect seizures by analyzing the spatiotemporal patterns of scalp EEG signals. We propose to commercialize this algorithm into a user-friendly seizure monitoring and alert (SMA) system for clincial research as well as for bedside use in hospital epilepsy monitoring and intensive care units. For such a system to be clinically useful, it is imperative that the detection algorithm must perform with a high sensitivity and low false detection rate. In Phase I, we will develop and test an SMA prototype that will (1) read and process on-line real-time EEG signals as designed in the Optima seizure detection algorithm; (2) generate an alert when an event is detected, and (3) send selected EEG segments containing the detected event to the physician for verification. This prototype will serve as the basis for subsequent devices designed specifically for two clinical applications: (1) Epilepsy Monitoring Units, and (2) Intensive Care Units. Successful commercialization of this SMA device will improve inpatient management of seizures by allowing for detection of intermittent and previously misdiagnosed events. PUBLIC HEALTH RELEVANCE: Although automated monitoring for critical heart and lung function is the standard of care in all hospitals, monitoring the function of the brain currently relies almost exclusively upon bedside clinical observations. As a result, a large number of subclinical seizures (only subtle observable changes) go undiagnosed every day. The primary goal of this project is to build and test a prototype for a greatly needed automated system to alert staff untrained in neurology to the presence of seizure activities. The overall goal is to improve the diagnosis and treatment of patients suffering from seizure disorders, particularly in community hospitals where EEG trained neurologist may not be available.