This technology describes critical parameters that can be used to normalize age-related variability in EEG characteristics in patients under anesthesia.
Neurophysiologic data obtained from EEG monitoring serves as an important guide for safe anesthesia care in elderly patients undergoing surgery. However, EEG characteristics vary greatly among patients of different ages, and current EEG monitoring devices do not account for this age-related variability. This makes accurate monitoring of older patients under general anesthesia difficult to achieve. As such, a method for normalization of EEG data to correct for age-associated differences would improve EEG monitoring.
This technology describes two age-associated parameters that are robustly correlated with age in EEG data: permutation in the beta range and approximate entropy in the beta range. These parameters can be used as normalizers for EEG data to accurately determine anesthetic levels in patients. As such, incorporation of these parameters into EEG monitoring devices could improve the safety of anesthesia care for elderly patients, thereby preventing adverse surgical outcomes.
This technology has been validated with clinical data.
IR CU20105
Licensing Contact: Sara Gusik