The effect of excessive noise rejection, noise filtering and twitch threshold on mechanomyograph twitch measurements - 27/03/26
Highlights |
• | Quantitative neuromuscular block monitors need appropriate noise management to provide accurate data to clinicians. |
• | Manufacturers of commercially available monitors do not publish noise management algorithms. |
• | Noise management system consisting of excessive noise detection and signal processing algorithm created for a laboratory-built mechanomyograph had “almost perfect” agreement with manual inspection of twitch waveforms when detecting excessive noise. |
• | Varying filtering level and amplitude thresholds had a substantial effect on train-of-four counts but little effect on train-of-four ratios. |
Abstract |
Background |
Quantitative neuromuscular block monitoring devices would ideally produce similar results. However, there can be substantial differences in twitch measurements due to differences in noise management and signal processing. Optimal clinical application of these monitors requires an understanding of how noise management and signal processing affect the results. We used a noise management system for a laboratory-built mechanomyograph to determine the effect of excessive noise rejection, noise filtering, and twitch amplitude threshold on twitch measurements.
Methods |
Twitch waveforms were collected using a mechanomyograph in patients under general anaesthesia. The mechanomyograph noise management system consisted of two parts: excessive noise detection and a signal processing algorithm. The system rejected waveforms containing excessive noise. Waveform signal noise was then filtered, and a minimum amplitude threshold was applied to define a twitch. The performance of the noise detection algorithm was compared to manual noise detection by inspecting each waveform. Two parameters within the signal processing algorithm (filtering level and amplitude threshold) were varied, and their effect on the twitch measurements was assessed.
Results |
A total of 4,371 twitch measurements were collected from 29 patients. A Cohen’s kappa value of 0.82 indicated “almost perfect” agreement between the excessive noise detection algorithm and manual detection. Varying the filtering level and amplitude threshold had a substantial effect on train-of-four counts, with a maximum average difference in count of 0.8, but had a negligible effect on train-of-four ratios.
Conclusions |
A noise management system for a laboratory-built mechanomyograph showed excellent agreement with the manual interpretation of twitch waveforms. Varying the noise filtering level and amplitude threshold had a substantial effect on train-of-four counts but a minimal effect on train-of-four ratios.
Registration |
ClinicalTrials.gov (NCT05006807).
Le texte complet de cet article est disponible en PDF.Keywords : Mechanomyography, Medical devices, Neuromuscular block monitoring, Quantitative monitoring, Train-of-four monitoring, Twitch monitoring
Plan
Vol 45 - N° 3
Article 101780- mai 2026 Retour au numéro
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