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 i 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 remains to be the development and commercialization of the CereScope, an all-in-one high-quality full-scalp (10-20 system) EEG acquisition device with capability of real-time signal display, real-time electrode impedance monitoring, as well as high-performance seizure monitoring and alert (SMA) in nearly real-time. Intended clinical settings include but are not limited to, ambulatory EEG monitoring, 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 several automated algorithms to accurately detect seizures (for different patient populations) by analyzing the spatiotemporal patterns of scalp EEG signals. The algorithm for adult EMU patients was incorporated in our IdentEvent seizure detection software, which received FDA approval on October 16, 2009. During the Phase I and Phase II of this SBIR project, we completed CereScope's (1) hardware design and software implementation, (2) electric safety (UL) test, (3) signal quality assessment on normal volunteer subjects, (4) fully integration with the SMA module, and (5) pilot clinical study on EMU patients. Building upon these achievements and with the strong support of our research teams, we now propose in this Phase IIB application a pathway to expand the diagnostic utility of the CereScope. It has been well-documented that, despite of some underlying similarities, there exist significant differences in both ictal and background EEG patterns among these patient populations. Therefore, to ensure CereScope's clinical utility, in this Phase IIB application, we propose to conduct three clinical studies, each in a different patient population. Each clinical study will be conducted at four major medical centers. These studies will evaluate how well the CereScope records video and EEG signals while simultaneously analyzing the sending alerts for possible seizure events. The studies will also generate performance statistics (sensitivity and false detection rate) on real-time seizure detection. This Phase IIB has the following specific aims: (1) complete performance validation of CereScope(tm) for adult EMU patients, (2) complete performance validation of CereScope(tm) for adult ambulatory cEEG patients, and (3) complete performance validation of CereScope(tm) for adult ICU patients. Successful commercialization of CereScope(tm) will improve inpatient management of seizures by allowing for detection of intermittent and previously misdiagnosed events.
Automated monitoring for critical heart and lung function has been the standard of care in all hospitals for decades. However, as important as it is, monitoring brain functions 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 test a prototype for a greatly needed automated system to acquire and analyzing brain electrical activities and alert staff untrained in neurology to the presence of seizure attacks, all in real-time. 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|