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Estimating paediatric normative values for nerve studies using clustering techniques
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DOI：10.1016/j.cnp.2026.02.006.png)
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Abstract
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Objective: To estimate normative values from mixed clinical paediatric electroneurography data using an unsupervised clustering approach. Methods: Electroneurography studies from paediatric patients (2009-2024) were analysed for common motor and sensory nerves. Motor parameters included distal motor latency, CMAP amplitude, duration, area, and conduction velocity; sensory parameters included SNAP amplitude and conduction velocity. Data were grouped into age windows, and within each, t-distributed stochastic neighbour embedding (t-SNE) was applied to identify the normative distribution. The mean, 5th, and 95th centiles were derived and modelled using exponential fits. Results: Normative values were estimated for ages 0-18 years. Motor amplitudes increased with age, and conduction velocities rose rapidly until 3-4 years before plateauing. Distal motor latency showed a brief early dip followed by an increase. Sensory amplitudes peaked between 1 and 8 years, while sensory conduction velocities increased sharply in the first year, then gradually declined. Conclusion: Unsupervised clustering can derive normative paediatric electroneurography values from heterogeneous clinical data, yielding trends consistent with published references. Significance: This data-driven approach is practical, generalisable, and enables identification of likely healthy individuals using multivariate electrophysiological parameters.
Keywords:
Paediatric electroneurography
Normal data
Clustering technique
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