Purpose:Current methods for diagnosis of PD rely on clinical examination. The
accuracy of diagnosis ranges between 73% and 84%, and is influenced by the
experience of the clinical assessor. Hence, an automatic, effective and
interpretable supporting system for PD symptom identification would support
clinicians in making more robust PD diagnostic decisions. Methods: We propose
to analyze Parkinson's tremor (PT) to support the analysis of PD, since PT is
one of the most typical symptoms of PD with broad generalizability. To realize
the idea, we present SPA-PTA, a deep learning-based PT classification and
severity estimation system that takes consumer-grade videos of front-facing
humans as input. The core of the system is a novel attention module with a
lightweight pyramidal channel-squeezing-fusion architecture that effectively
extracts relevant PT information and filters noise. It enhances modeling
performance while improving system interpretability. Results:We validate our
system via individual-based leave-one-out cross-validation on two tasks: the PT
classification task and the tremor severity rating estimation task. Our system
presents a 91.3% accuracy and 80.0% F1-score in classifying PT with non-PT
class, while providing a 76.4% accuracy and 76.7% F1-score in more complex
multiclass tremor rating classification task. Conclusion: Our system offers a
cost-effective PT classification and tremor severity estimation results as
warning signs of PD for undiagnosed patients with PT symptoms. In addition, it
provides a potential solution for supporting PD diagnosis in regions with
limited clinical resources.

利用消费级别摄像头采集的视频作为输入，我们提出了 SPA-PTA，一种基于深度学习的帕金森震颤（Parkinson's tremor，PT）分类和震颤严重程度估计系统。该系统通过有效提取相关 PT 信息和过滤噪声的新型注意模块，实现了 91.3% 的准确率和 80.0% 的 F1 分数的 PT 分类，以及 76.4% 的准确率和 76.7% 的 F1 分数的复杂多分类震颤评级任务。该系统可作为一种经济实惠的 PD 预警工具，用于对患有 PT 症状的未诊断患者进行诊断，并在医疗资源有限的地区提供辅助 PD 诊断的潜在解决方案。