Quantum-Inspired Neural Networks for High-Precision Nerve Conduction Velocity Estimation

Abstract This study presents a transformative quantum-inspired machine learning framework for nerveconduction velocity (NCV) analysis that significantly advances current diagnostic capabilities. Ournovel approach integrates quantum computing principles with advanced signal processing to overcome the three fundamental limitations of conventional methods: oversimplified nerve modeling,temperature sensitivity, and static measurement interpretation. The framework introduces threekey innovations: quantum Fourier feature transformation achieving 32% better feature separation(p<0.001), a temperature-resilient hybrid neural network (64-32-16 architecture), which reduces thermal dependence by 68%, and a probabilistic uncertainty quantification system. Extensive validationon 1000 simulated cases demonstrated exceptional performance metrics, including unprecedentedprecision (MSE: 0.42±0.03 m/s), superior explanatory power (R2: 0.91±0.02), and excellent earlydetection capability (94% sensitivity). The system maintains full clinical interpretability while delivering 28% better temperature compensation and 92% detection rate for critical 3-5µm fibers.These advancements establish a new standard in electrodiagnostic medicine, combining theoreticalinnovation from quantum information theory with immediate clinical applicability for neuropathydiagnosis. The framework’s open-source implementation facilitates widespread adoption and furtherdevelopment of precision neurophysiology.