解码帕金森震颤：一个整合螺旋线图多旋转空间和频谱动态的可解释框架
Decoding Parkinsonian Tremor: An Explainable Framework Integrating Multi-Revolution Spatial and Spectral Dynamics of Spiral Drawings
AI总结：
研究利用新型径向采样特征融合方法，将螺旋图像转换为旋转信号，提取空间与频谱特征，训练机器学习模型筛查帕金森病。RF分类器性能最佳，外旋转均方根径向导数是关键生物标志物，该方法提高检测准确性，提供实用筛查工具。
AI中文摘要：
帕金森病（PD）的运动障碍可通过数字化螺旋线图检测出来。本研究引入一种使用新型径向采样特征融合方法进行PD筛查的可解释框架。通过系统的射线采样技术将二维螺旋图像转换为一维旋转信号以提取三个不同的旋转。我们整合了空间指标，如旋转间距变异性和均方根径向导数，以及通过快速傅里叶变换（FFT）分析在低、中、高谐波频段得出的频谱描述符。利用总共20个特征训练了包括支持向量机（SVM）、随机森林（RF）和轻梯度提升机（LightGBM）在内的先进机器学习模型。其中，RF分类器表现出卓越性能。随后的5折交叉验证稳定性分析以及特征重要性分析确定外旋转的均方根径向导数为最关键的生物标志物。分层交叉验证表明，与单域方法相比，结合空间和频率特征显著提高了检测准确性，即使在数据稀缺环境中也便于有效临床应用。这个可解释的流程提供了一个强大、低成本的白盒筛查工具，为早期临床干预提供了一种实用的替代不透明深度学习模型的方法。
英文摘要：
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.
