The physiology of intraoperative error: using electrokardiograms to understand operator performance during robot-assisted surgery simulations

Background No platform for objective, synchronous and on-line evaluation of both intraoperative error and surgeon physiology yet exists. Electrokardiogram (EKG) metrics have been associated with cognitive and affective features that are known to impact surgical performance but have not yet been analyzed in conjunction with real-time error signals using objective, real-time methods. Methods EKGs and operating console point-of-views (POVs) for fifteen general surgery residents and five non-medically trained participants were captured during three simulated robotic-assisted surgery (RAS) procedures. Time and frequency-domain EKG statistics were extracted from recorded EKGs. Intraoperative errors were detected from operating console POV videos. EKG statistics were synchronized with intraoperative error signals. Results Relative to personalized baselines, IBI, SDNN and RMSSD decreased 0.15% (S.E. 3.603e−04; P = 3.25e−05), 3.08% (S.E. 1.603e−03; P