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。

基于姿势的帕金森病颤抖类型和级别分析从视频

Pose-based Tremor Type and Level Analysis for Parkinson's Disease from  Video

While Parkinson's disease (PD) is typically characterized by motor disorder,
there is evidence of diminished emotion perception in PD patients. This study
examines the utility of affective Electroencephalography (EEG) signals to
understand emotional differences between PD vs Healthy Controls (HC), and for
automated PD detection. Employing traditional machine learning and deep
learning methods, we explore (a) dimensional and categorical emotion
recognition, and (b) PD vs HC classification from emotional EEG signals. Our
results reveal that PD patients comprehend arousal better than valence, and
amongst emotion categories, \textit{fear}, \textit{disgust} and
\textit{surprise} less accurately, and \textit{sadness} most accurately.
Mislabeling analyses confirm confounds among opposite-valence emotions with PD
data. Emotional EEG responses also achieve near-perfect PD vs HC recognition.
{Cumulatively, our study demonstrates that (a) examining \textit{implicit}
responses alone enables (i) discovery of valence-related impairments in PD
patients, and (ii) differentiation of PD from HC, and (b) emotional EEG
analysis is an ecologically-valid, effective, facile and sustainable tool for
PD diagnosis vis-á-vis self reports, expert assessments and resting-state
analysis.}

通过研究帕金森症（PD）患者和健康对照者（HC）的情感脑电图（EEG）信号，本研究使用传统机器学习和深度学习方法，探讨维度和分类情感识别以及 PD 与 HC 分类的有效性，结果表明情感 EEG 响应几乎可以完美地识别出 PD 和 HC，而通过研究 “隐含” 反应可以发现 PD 患者的价值相关缺陷，并且情感 EEG 分析是一种生态有效、易于实现和可持续的 PD 诊断工具。