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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Estimating paediatric normative values for nerve studies using clustering techniques
G K Cooray1,2,3, D Motan2, K Howse2
1Karolinska Institutet, Stockholm, 171 77, Sweden.
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.

