Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Nov 23, 2020
Date Accepted: May 4, 2021
Date Submitted to PubMed: Aug 30, 2021
Exploratory Outlier Analysis for Acceleromyographic Neuromuscular Monitoring
ABSTRACT
Background:
Due to 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 towards their widespread adoption. Through outlier analysis techniques, monitoring devices can learn to detect and flag signal abnormalities.
Objective:
The present study aims to engineer a set of features that enable the detection of outliers in the form of erroneous Train of Four (TOF) measurements from an acceleromyographic-based device. These features are tested for their potential in the detection erroneous TOF measurements by developing an outlier detection algorithm
Methods:
A dataset encompassing 533 high-sensitivity TOF measurements from thirty-five patients was created based on a multi-centric open label trial of a purpose-built accelero- and gyroscopic-based neuromuscular monitoring application. A basic set of features were extracted based on raw data while a second set of features were purpose-engineered based on TOF pattern characteristics. Two cost-sensitive logistic regression models were deployed to evaluate the performance of these features. The final output of the developed models was a binary classification, indicating if a TOF measurement was an outlier or not.
Results:
A total of 7 basic features were extracted based on raw data, while another 8 features were engineered based on TOF pattern characteristics. The model training and testing were based on separate data sets: one of 319 measurements (18 outliers) and a second with 214 measurements (12 outliers). The F1-score (95% CI) was 0.86 (0.48-0.97) for the CSLR model with engineered features, significantly larger than the CSLR model with the basic features (0.29 [0.17-0.53]; P <.001).
Conclusions:
The set of engineered features and their corresponding incorporation in an outlier detection algorithm have the potential to increase overall neuromuscular monitoring data consistency. Integrating outlier flagging algorithms within neuromuscular monitors could potentially reduce overall AMG-based reliability issues. Clinical Trial: NCT03605225 https://clinicaltrials.gov/ct2/show/NCT03605225?term=NCT03605225&draw=2&rank=1
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