Decoding Parkinsonian Tremor: An Explainable Framework Integrating Multi-Revolution Spatial and Spectral Dynamics of Spiral Drawings

Abstract

Parkinson's disease (PD) manifests in motor impairments that are detectable through digitized spiral drawings. This study introduces an explainable framework for PD screening using a novel radial-sampling feature fusion approach. We transform 2D spiral images into 1D revolution signals via a systematic ray-sampling technique to extract three distinct revolutions. We integrate spatial metrics, such as inter-revolution spacing variability and RMS radial derivatives, with spectral descriptors derived from Fast Fourier Transform (FFT) analysis across low, mid, and high harmonic bands. A total of 20 features were utilized to train state-of-the-art machine learning models, including Support Vector Machines (SVMs), Random Forests (RFs), and Light Gradient Boosting Machines (LightGBMs). Among these, the RF classifier demonstrated superior performance. Subsequent 5-fold cross-validation stability analysis along with feature importance analysis identified RMS radial derivative of the outer revolution as the most critical biomarker. Stratified Cross-Validation demonstrates that combining spatial and frequency features significantly enhances detection accuracy compared to single-domain methods, facilitating effective clinical deployment even in data-scarce environments. This interpretable pipeline provides a robust, low-cost white-box screening tool, offering a practical alternative to opaque deep-learning models for early clinical intervention.