Christian Leth Petersen1, Matthias Görges, Roslyn Massey, Guy Albert Dumont, J Mark Ansermino. 1. From the *Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada; †Pediatric Anesthesia Research Team, Child and Family Research Institute, Vancouver, British Columbia, Canada; and ‡Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada.
Abstract
BACKGROUND: Recent research and advances in the automation of anesthesia are driving the need to better understand electroencephalogram (EEG)-based anesthesia end points and to test the performance of anesthesia monitors. This effort is currently limited by the need to collect raw EEG data directly from patients. METHODS: A procedural method to synthesize EEG signals was implemented in a mobile software application. The application is capable of sending the simulated signal to an anesthesia depth of hypnosis monitor. Systematic sweeps of the simulator generate functional monitor response profiles reminiscent of how network analyzers are used to test electronic components. RESULTS: Three commercial anesthesia monitors (Entropy, NeuroSENSE, and BIS) were compared with this new technology, and significant response and feature variations between the monitor models were observed; this includes reproducible, nonmonotonic apparent multistate behavior and significant hysteresis at light levels of anesthesia. CONCLUSIONS: Anesthesia monitor response to a procedural simulator can reveal significant differences in internal signal processing algorithms. The ability to synthesize EEG signals at different anesthetic depths potentially provides a new method for systematically testing EEG-based monitors and automated anesthesia systems with all sensor hardware fully operational before human trials.
BACKGROUND: Recent research and advances in the automation of anesthesia are driving the need to better understand electroencephalogram (EEG)-based anesthesia end points and to test the performance of anesthesia monitors. This effort is currently limited by the need to collect raw EEG data directly from patients. METHODS: A procedural method to synthesize EEG signals was implemented in a mobile software application. The application is capable of sending the simulated signal to an anesthesia depth of hypnosis monitor. Systematic sweeps of the simulator generate functional monitor response profiles reminiscent of how network analyzers are used to test electronic components. RESULTS: Three commercial anesthesia monitors (Entropy, NeuroSENSE, and BIS) were compared with this new technology, and significant response and feature variations between the monitor models were observed; this includes reproducible, nonmonotonic apparent multistate behavior and significant hysteresis at light levels of anesthesia. CONCLUSIONS: Anesthesia monitor response to a procedural simulator can reveal significant differences in internal signal processing algorithms. The ability to synthesize EEG signals at different anesthetic depths potentially provides a new method for systematically testing EEG-based monitors and automated anesthesia systems with all sensor hardware fully operational before human trials.