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. However, inpatient management of seizures is complicated by the fact that they occur intermittently and unpredictably, and thus it is not infrequent that patients'seizures go unrecognized. This can result in unnecessarily prolonged hospital stays, or worse, delay of treatment and irreversible brain injury. Therefore, there is a great need to develop an accurate bed-side seizure monitroing and alert (SMA) system. The overall goal of this SBIR project is to commercialize an accurate, reliable, and user-friendly EEG-based seizure monitoring and alert (EEGSMA) system for use in clinical settings where patients require close neurological monitoring. Intended clinical settings include but are not limited to, epilepsy monitoring units (EMUs), intensive care units (ICUs), emergency departments (EDs), and general care units for neurology and neurosurgical patients. Researchers at Optima Neuroscience have developed an automated algorithm to accurately detect seizures by analyzing the spatiotemporal patterns of scalp EEG signals. The algorithm was incorporated in our IdentEvent" seizure detection software, which received FDA approval on October 16, 2009. During the Phase I of this SBIR project, we have further completed bed-side hardware design of the SMA system and transformed IdentEvent for real-time application. The SMA system was successfully tested in simulation real-time mode, and initial clinical feasibility testing has been completed. In this Phase II application, we propose to continue the clinical testing for the SMA system as well as expand the functions of the EEGSMA system for use in acute care environments, e.g., ICUs and EDs. To accomplish this, we not only have to develop a reliable, portable, and user-friendly EEG acquisition module that can be set up quickly, but also we need to expand IdentEvent for use in children and for ICU patients. Therefore, the specific aims of this application are: (1) to complete the clinical performance evaluation of the SMA module in an EMU setting, (2) to design and test front end hardware and software components of the EEG head-box and integrate them with the SMA system, (3) to complete pre-clinical testing and pilot outpatient study of the integrated EEGSMA system, (4) to conduct inpatient testing of the integrated EEGSMA system in EMUs, (5) to test the detection algorithm (IdentEvent) on pediatric patients (age 3 ~ 17) in EMUs, and (6) to further develop a seizure detection algorithm for ICU patients. Successful commercialization of this EEGSMA device will improve inpatient management of seizures by allowing for detection of intermittent and previously misdiagnosed events.
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 neurologists may not be available.
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|Shiau, Deng-Shan; Halford, J J; Kelly, K M et al. (2010) SIGNAL REGULARITY-BASED AUTOMATED SEIZURE DETECTION SYSTEM FOR SCALP EEG MONITORING. Cybern Syst Anal 46:922-935|