Exploratory outlier detection for acceleromyographic neuromuscular monitoring: Machine learning approach

Michaël Verdonck*, Hugo Carvalho, Johan Berghmans, Patrice Forget, Jan Poelaert

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
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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 app intended for neuromuscular monitoring based on combined accelerometric and angular hand movement data. During the paired comparison stage of this app against existing acceleromyography monitoring devices, it was noted that the results from both devices did not always concur. This 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 of erroneous TOF measurements by developing an outlier detection algorithm. Methods: A data set encompassing 533 high-sensitivity TOF measurements from 35 patients was created based on a multicentric open label trial of a purpose-built accelero- and gyroscopic-based neuromuscular monitoring app. A basic set of features was extracted based on raw data while a second set of features was purpose engineered based on TOF pattern characteristics. Two cost-sensitive logistic regression (CSLR) 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 with 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 acceleromyography-based reliability issues.

Original languageEnglish
Article numbere25913
JournalJournal of Medical Internet Research
Issue number6
Publication statusPublished - 21 Jun 2021

Bibliographical note

Funding Information:
This research was funded by the Flanders Innovation and Entrepreneurship Fund, the Willy Gepts Fund for Scientific Research, and the Society for Anesthesia and Resuscitation of Belgium (SARB).

Publisher Copyright:
© Michaël Verdonck, Hugo Carvalho, Johan Berghmans, Patrice Forget, Jan Poelaert. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 06.06.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.


  • Acceleromyography
  • Anesthesiology
  • Machine learning
  • Monitoring devices
  • Monitors
  • Neuromuscular
  • Neuromuscular monitoring
  • Outlier analysis
  • Postoperative residual curarization
  • Train-of-four


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