Background

Slowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best.

Aim

We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of bradykinesia.

Methods

We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naïve Bayes, Logistic Regression, Support Vector Machine) to predict no/slight bradykinesia (UPDRS = 0–1) or mild/moderate/severe bradykinesia (UPDRS = 2–4), and presence or absence of Parkinson's diagnosis.

Results

A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe bradykinesia with an estimated test accuracy of 0.8. A Naïve Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67.

Conclusion

The method described here presents an approach for predicting bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of bradykinesia prediction is comparable to that recorded by blinded human experts.

中文翻译：

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智能手机视频中帕金森病运动迟缓的监督分类

背景

运动缓慢，称为运动迟缓，是帕金森氏症的核心临床症状，也是其诊断的基础。临床医生通常通过对患者的手指和拇指反复敲击进行视觉判断来评估运动迟缓。然而，专家评估的评估者间一致性已被证明充其量只是适度的。

目的

我们提出了一种低成本的非接触式系统，使用智能手机视频来自动确定运动迟缓的存在。

方法

我们在临床环境中收集了 70 个手指敲击评估视频（40 只帕金森手，30 只控制手）。帕金森病的两名临床专家对诊断不知情，他们使用统一帕金森病评定量表 (UPDRS) 对视频进行了评估，以给出 0 到 4 级的运动迟缓严重程度。我们开发了一种计算机视觉方法，可识别与手部运动相关的区域并提取临床相关特征。在输入分类模型（朴素贝叶斯、逻辑回归、支持向量机）之前使用主成分分析进行降维，以预测无/轻度运动迟缓（UPDRS = 0-1）或轻度/中度/重度运动迟缓（UPDRS = 2-4） )，以及是否存在帕金森病的诊断。

结果

具有径向基函数内核的支持向量机预测轻度/中度/重度运动迟缓的存在，估计测试精度为 0.8。朴素贝叶斯模型预测帕金森病的存在，估计测试准确度为 0.67。

结论

此处描述的方法提出了一种从手指敲击测试视频中预测运动迟缓的方法。该方法对光照条件和相机定位具有鲁棒性。在一组试点数据上，运动迟缓预测的准确性与不知情的人类专家记录的准确性相当。