Machine learning based analysis and detection of trend outliers for electromyographic neuromuscular monitoring.

Autor: Verdonck, Michaël, Carvalho, Hugo, Fuchs-Buder, Thomas, Brull, Sorin J., Poelaert, Jan
Zdroj: Journal of Clinical Monitoring & Computing; Oct2024, Vol. 38 Issue 5, p1163-1173, 11p
Abstrakt: Purpose: Neuromuscular monitoring is frequently plagued by artefacts, which along with the frequent unawareness of the principles of this subtype of monitoring by many clinicians, tends to lead to a cynical attitute by clinicians towards these monitors. As such, the present study aims to derive a feature set and evaluate its discriminative performance for the purpose of Train-of-Four Ratio (TOF-R) outlier analysis during continuous intraoperative EMG-based neuromuscular monitoring. Methods: Patient data was sourced from two devices: (1) Datex-Ohmeda Electromyography (EMG) E-NMT: a dataset derived from a prospective observational trial including 136 patients (21,891 TOF-R observations), further subdivided in two based on the type of features included; and (2) TetraGraph: a clinical case repository dataset of 388 patients (97,838 TOF-R observations). The two datasets were combined to create a synthetic set, which included shared features across the two. This process led to the training of four distinct models. Results: The models showed an adequate bias/variance balance, suggesting no overfitting or underfitting. Models 1 and 2 consistently outperformed the others, with the former achieving an F1 score of 0.41 (0.31, 0.50) and an average precision score (95% CI) of 0.48 (0.35, 0.60). A random forest model analysis indicated that engineered TOF-R features were proportionally more influential in model performance than basic features. Conclusions: Engineered TOF-R trend features and the resulting Cost-Sensitive Logistic Regression (CSLR) models provide useful insights and serve as a potential first step towards the automated removal of outliers for neuromuscular monitoring devices. Trial registration: NCT04518761 (clinicaltrials.gov), registered on 19 August 2020. Key points: • Neuromuscular monitoring is still suboptimally employed by anesthesiologists despite recent recommendations. • Artefactual phenomena are frequently reported as a cause of distrust in neuromuscular monitoring. • Machine-learning models based on universal neuromuscular monitoring variables can be developed to minimise artefacts and so increase end-user compliance. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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