<p>This paper presents an advanced machine learning (ML) framework for precise nerve conduction velocity (NCV) analysis, integrating multiscale signal processing with physiologically-constrained deep learning. Our approach addresses three fundamental limitations of conventional NCV techniques: (1) oversimplified nerve fiber modeling, (2) temperature sensitivity, and (3) static measurement interpretation. The proposed framework combines: (i) entropy-optimized wavelet analysis for adaptive multiscale signal decomposition, (ii) thermodynamically-regularized neural networks incorporating Arrhenius kinetics, and (iii) stochastic progression models for uncertainty-aware longitudinal tracking. Through data extracted from prior studies in this field, rigorously validated across 1842 patients from 28 medical centers, we demonstrate significant improvements: 23.4% enhancement in motor NCV accuracy (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_8367_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="70" /> </InlineMediaObject> <EquationSource Format="TEX">\(\hbox {p} < 0.001\)</EquationSource> </InlineEquation>) and 28.7% for sensory fibers. The framework maintains physiological interpretability while achieving superior performance through: (a) wavelet-optimized resolution scales (2–8 ms for motor, 0.5–2 ms for sensory fibers), (b) temperature compensation accurate to <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_8367_Article_IEq2.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="110" /> </InlineMediaObject> <EquationSource Format="TEX">\(0.58 \pm 0.19\,\hbox {m/s}\)</EquationSource> </InlineEquation> across 20-<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_8367_Article_IEq3.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="36" /> </InlineMediaObject> <EquationSource Format="TEX">\(40^\circ \hbox {C}\)</EquationSource> </InlineEquation>, and (c) probabilistic progression tracking with 88.9% treatment response prediction accuracy. This work establishes new standards for ML applications in clinical neurophysiology by rigorously combining biophysical first principles with data-driven learning, offering both theoretical advances and immediate clinical utility for neuropathy diagnosis and monitoring.</p>

Advanced multiscale machine learning for nerve conduction velocity analysis

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2025年7月2日

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Hossein Sadeghi

摘要

This paper presents an advanced machine learning (ML) framework for precise nerve conduction velocity (NCV) analysis, integrating multiscale signal processing with physiologically-constrained deep learning. Our approach addresses three fundamental limitations of conventional NCV techniques: (1) oversimplified nerve fiber modeling, (2) temperature sensitivity, and (3) static measurement interpretation. The proposed framework combines: (i) entropy-optimized wavelet analysis for adaptive multiscale signal decomposition, (ii) thermodynamically-regularized neural networks incorporating Arrhenius kinetics, and (iii) stochastic progression models for uncertainty-aware longitudinal tracking. Through data extracted from prior studies in this field, rigorously validated across 1842 patients from 28 medical centers, we demonstrate significant improvements: 23.4% enhancement in motor NCV accuracy ( \(\hbox {p} < 0.001\) ) and 28.7% for sensory fibers. The framework maintains physiological interpretability while achieving superior performance through: (a) wavelet-optimized resolution scales (2–8 ms for motor, 0.5–2 ms for sensory fibers), (b) temperature compensation accurate to \(0.58 \pm 0.19\,\hbox {m/s}\) across 20- \(40^\circ \hbox {C}\) , and (c) probabilistic progression tracking with 88.9% treatment response prediction accuracy. This work establishes new standards for ML applications in clinical neurophysiology by rigorously combining biophysical first principles with data-driven learning, offering both theoretical advances and immediate clinical utility for neuropathy diagnosis and monitoring.