迈向可解释的基于语音的帕金森病筛查：联合传递函数-特征-分类器-集成选择

文献信息

中文摘要

背景： 帕金森病（PD）的诊断依赖于对运动体征的主观临床检查，而这些体征在疾病早期可能轻微、间歇性或缺失，这促使人们寻找客观、低成本、非侵入性的标志物以实现更早期、更一致的检测。语音记录仅需一个麦克风即可获取，是强有力的候选标志物，本研究开发了一种基于语音的PD筛查机器学习流程，该流程建立在竞争群优化器（CSO）之上，它联合搜索声学特征子集、分类器配置和二值化传递函数，而非像大多数先前流程那样仅优化特征子集。方法：在两个公开的、按受试者分组的语音数据集Oxford和Naranjo上进行评估，在相同方案下与五种基线方法比较，每种方法运行20次，我们提出的流程在Naranjo上取得最高平均平衡准确率0.847，在Oxford上取得第二高0.810，这些准确率与更广泛的基于语音的PD筛查文献一致；由于该证据来自两个小型、单一录音方案、回顾性公开数据集，分别包含31名和80名受试者，我们将其呈现为迈向初筛分诊或就诊间监测工具的初步、令人鼓舞的一步，有待在前瞻性收集的多中心队列中进行外部验证，而非作为独立的诊断工具。结果：作为迈向临床可解释性的初步步骤，我们检查了在所提出流程的20次重复运行中哪些声学特征被最一致地选择，并将其与帕金森病性发声障碍的既定生理相关物进行对照；任意两次运行完整特征子集之间的一致性较弱，但音高周期熵——一种对非周期性音高周期变异性的非线性动力学度量——在两个数据集上被选择的频率远高于随机水平，这与潜在的病理生理学一致，而不仅仅是具有预测性。结论：这些结果支持基于语音、经元启发式优化的筛查作为一种合理、可解释、低负担的工具，用于远程医疗和家庭监测。

英文摘要

Parkinson's disease (PD) diagnosis relies on subjective clinical examination of motor signs that can be mild, intermittent, or absent early in the disease course, motivating objective, low-cost, non-invasive markers for earlier, more consistent detection. Voice recordings, acquirable with nothing more than a microphone, are a strong candidate, and this study develops a machine learning pipeline for voice-based PD screening built on the Competitive Swarm Optimizer (CSO), which jointly searches the acoustic feature subset, classifier configuration, and binarization transfer function, instead of optimizing the feature subset alone as most prior pipelines do. Evaluated on two public, subject-grouped voice datasets, Oxford and Naranjo, against five baselines under an identical protocol across 20 runs per method, our proposed pipeline attains the highest mean balanced accuracy on Naranjo with 0.847 and the second-highest on Oxford with 0.810, accuracies consistent with the wider voice-based PD screening literature; because this evidence comes from two small, single-recording-protocol, retrospective public datasets of 31 and 80 subjects each, we present it as an initial, encouraging step toward a first-pass triage or between-visit monitoring tool, pending external validation on a prospectively collected, multi-site cohort, not as a standalone diagnostic instrument. As an initial step toward clinical interpretability, we check which acoustic features are selected most consistently across 20 repeated runs of the proposed pipeline against established physiological correlates of Parkinsonian dysphonia; agreement between any two runs' complete feature subsets is weak, but pitch period entropy, a nonlinear-dynamical measure of aperiodic pitch period variability, is selected far more often than chance on both datasets, consistent with the underlying pathophysiology and not merely predictive. These results support voice-based, metaheuristic-optimized screening as a plausible, interpretable, low-burden tool for telemedicine and home monitoring.