Increased recognition of the incidence of nonconvulsive seizures (NCSs) in critically ill patients has led to a growing demand for continuous EEG monitoring in Intensive Care Units (ICUs). However, one of the biggest challenges to the expanding use of continuous EEG monitoring in the ICU lies in the difficulty of providing a timely review of clinical data by EEG experts. As a result, though EEG is being recorded continuously, ICU physicians may not be alerted to the occurrence of seizures until several hours later, following thorough review of the raw EEG by specialized personnel. The end result is compromised patient care and the inability to make appropriate real-time treatment decisions. Automated seizure detection software is occasionally used to assist in the EEG review process. However, existing seizure detection software is inadequate for most ICU patients because of the abnormal background EEG and highly variable seizure discharges that occur in encephalopathic patients. These EEG patterns differ greatly from those patterns that occur in epilepsy monitoring units (EMUs). As a result, commercially available software performs poorly when used with critical care patients, resulting in missed seizures and a high incidence of false detections. While available EEG-trending software is more useful, there are significant technical limitations in existing algorithms: 1) inability to show clear changes for bref, focal or slowly evolving, low frequency seizures, 2) low specificity in differentiating NCSs that require urgent treatment from other abnormalities, which are usually not treated with anti-seizure medicines, and 3) limited scientific evidence of clinical utility - none are FDA approved for ICU use. The overall goal of this SBIR project is to develop and commercialize an accurate, reliable, and user-friendly ICU seizure monitoring and alert system, CereScope"""""""". The system will feature a novel automated seizure detection algorithm, ICU-ASDA, with a high sensitivity (>85 percent) and low false detection rate (<0.2/hr or 5/day). To enhance the system sensitivity, it will also be interfaced with artifact-reduced novel quantitative EEG (qEEG) trending, a Seizure Index (SI) which facilitates rapid recognition of potential seizure patterns by visual inspection. CereScope"""""""" will automatically create and transmit digital graphic files containing detected events for immediate expert review. Having completed the design and training studies for the ICU-ASDA, in this Phase I project we propose a clinical study to statistically evaluate and validate the performance of the algorithm that will meet the FDA's requirements. In addition, we will conduct studies at four Neuro-ICUs to investigate how combining qEEG trending with the results from the detection algorithm can enhance overall performance of the system.
The specific aims of this Phase I feasibility study are: 1) Conduct a clinical study to evaluate the performance (sensitivity and false detection rate) of a novel seizure detection algorithm, ICU-ASDA, in EEG recordings from acutely ill adult patients, and 2) Investigate the utility of novel qEEG trends for enhancing sensitivity in identifying NCSs in long-term ICU EEG recordings when used in conjunction with the ICU-ASDA. Successful commercialization of the CereScope"""""""" system will improve the recognition and management of seizures in critically ill patients.
Increased recognition of the high incidence of seizures in critically ill patients has led to a growing demand for continuous brain monitoring in Intensive Care Units (ICUs). However, one of the biggest challenges to the expanding use of continuous brain monitoring in the ICU is the difficulty of providing a timely review of the high volumes of EEG (brain electrical activity signal) data by experts, resulting in an inability to make appropriae real-time treatment decisions and compromised patient care. The overall goal of this SBIR project is to develop a novel system for EEG analysis that will allow ICU physicians, technicians, and nurses to rapidly identify seizures as well as other abnormal changes in brain function.
| Halford, J J; Shiau, D; Desrochers, J A et al. (2015) Inter-rater agreement on identification of electrographic seizures and periodic discharges in ICU EEG recordings. Clin Neurophysiol 126:1661-9 |
| Sackellares, J Chris; Shiau, Deng-Shan; Halford, Jonathon J et al. (2011) Quantitative EEG analysis for automated detection of nonconvulsive seizures in intensive care units. Epilepsy Behav 22 Suppl 1:S69-73 |
