Exploratory Outlier Detection for Acceleromyographic Neuromuscular Monitoring: Machine Learning Approach.

ABSTRACT:

Background

Perioperative quantitative monitoring of neuromuscular function in patients receiving neuromuscular blockers has become internationally recognized as an absolute and core necessity in modern anesthesia care. Because of their kinetic nature, artifactual recordings of acceleromyography-based neuromuscular monitoring devices are not unusual. These generate a great deal of cynicism among anesthesiologists, constituting an obstacle toward their widespread adoption. Through outlier analysis techniques, monitoring devices can learn to detect and flag signal abnormalities. Outlier analysis (or anomaly detection) refers to the problem of finding patterns in data that do not conform to expected behavior.

Objective

This study was motivated by the development of a smartphone

SUBMITTER: Verdonck M 

PROVIDER: S-EPMC8768027 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature