Proposed applications of machine learning to intraoperative neuromonitoring during spine surgeries

Abstract

Intraoperative neurophysiological monitoring (IONM) provides data on the state of neurological functionality. However, the current state of technology impedes the reliable and timely extraction and communication of relevant information. Advanced signal processing and machine learning (ML) technologies can develop a robust surveillance system that can reliably monitor the current state of a patient's nervous system and promptly alert the surgeons of any imminent risk. Various ML and signal processing tools can be utilized to develop a real-time, objective, multi-modal IONM based-alert system for spine surgery. Next generation systems should be able to obtain inputs from anesthesiologists on vital sign disturbances and pharmacological changes, as well as being capable of adapting patient baseline and model parameters for patient variability in age, gender, and health. It is anticipated that the application of automated decision guiding of checklist strategies in response to warning criteria can reduce human work-burden, improve accuracy, and minimize errors.

Keywords

• Somatosensory evoked potential (SSEP)
• Decision making
• Signal processing
• Motor evoked potential (MEP)
• Principal component analysis (PCA)
• Support vector model