OBJECTIVE: Injury during routine spinal cord procedures could result in devastating consequences for the surgical patient. Spinal cord monitoring through somatosensory evoked potentials (SEPs) remains a viable method for prevention of serious injury. METHODS: The adaptive coherence estimation (ACE) is a method to iteratively calculate signal match quality through successive filter entrainment. Here we compare the speed of detection with ACE to conventional amplitude measurements. Both absolute magnitude of ACE and amplitude as well as slope change detector algorithm (Farley-Hinich) was run as well to determine the earliest time when a significant change occurred. RESULTS: The standard error for the ACE algorithm is close to one tenth of the amplitude measure, Since the ACE algorithm achieved low variance during baseline measurement, we were able to achieve rapid detection of injury. For absolute magnitude detection ACE was faster than amplitude for the 20 g injury weight class. It took an average of 10 epochs to detect the injury with adaptive coherence and nearly 19 with standard amplitude metrics using absolute magnitude changes. Abrupt change detection methods using slope change show that ACE provides more favorable detection capabilities comparable to amplitude. Additionally, there was a significant increase in the ROC curve between ACE and amplitude alone (p<0.05). CONCLUSIONS: Because of its excellent detection capabilities, the adaptive coherence method provides an excellent supplement to traditional amplitude for capturing injury-related changes in SEPs. SIGNIFICANCE: Adaptive coherence remains a viable method for rapidly and accurately detecting spinal injury.
OBJECTIVE: Injury during routine spinal cord procedures could result in devastating consequences for the surgical patient. Spinal cord monitoring through somatosensory evoked potentials (SEPs) remains a viable method for prevention of serious injury. METHODS: The adaptive coherence estimation (ACE) is a method to iteratively calculate signal match quality through successive filter entrainment. Here we compare the speed of detection with ACE to conventional amplitude measurements. Both absolute magnitude of ACE and amplitude as well as slope change detector algorithm (Farley-Hinich) was run as well to determine the earliest time when a significant change occurred. RESULTS: The standard error for the ACE algorithm is close to one tenth of the amplitude measure, Since the ACE algorithm achieved low variance during baseline measurement, we were able to achieve rapid detection of injury. For absolute magnitude detection ACE was faster than amplitude for the 20 g injury weight class. It took an average of 10 epochs to detect the injury with adaptive coherence and nearly 19 with standard amplitude metrics using absolute magnitude changes. Abrupt change detection methods using slope change show that ACE provides more favorable detection capabilities comparable to amplitude. Additionally, there was a significant increase in the ROC curve between ACE and amplitude alone (p<0.05). CONCLUSIONS: Because of its excellent detection capabilities, the adaptive coherence method provides an excellent supplement to traditional amplitude for capturing injury-related changes in SEPs. SIGNIFICANCE: Adaptive coherence remains a viable method for rapidly and accurately detecting spinal injury.