Supervised classification of bradykinesia in Parkinson's disease from smartphone videos
Supervised classification of bradykinesia in Parkinson's disease from smartphone videos
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DOI：
10.1016/j.artmed.2020.101966
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发表时间：
2020-11-01
影响因子：
7.5
通讯作者：
Wong, David C.
中科院分区：
文献类型：
--
作者：
Williams, Stefan;Relton, Samuel D.;Wong, David C.
背景：运动迟缓，即运动迟缓，是帕金森病的核心临床体征，也是诊断帕金森病的基础。临床医生通常通过对患者重复敲击手指和拇指进行视觉判断来评估运动迟缓。然而，评级间协议的专家评估已被证明是只有温和的，在bother.Aim：我们提出了一个低成本的，非接触式的系统，使用智能手机的视频，以自动确定存在的运动迟缓。方法：我们收集了70个视频的手指轻拍评估在临床环境（40帕金森氏症的手，30控制手）。两名帕金森氏症临床专家对诊断不知情，使用统一帕金森氏病评定量表（Unified Pakinson's Disease Rating Scale，缩写为CARS）对视频进行评估，给出运动迟缓严重程度在0到4之间的等级。我们开发了一种计算机视觉方法，可以识别与手部运动相关的区域并提取临床相关特征。在输入到分类模型之前，使用主成分分析进行模糊性降低（朴素贝叶斯、逻辑回归、支持向量机）预测无/轻微运动迟缓（GSRS = 0-1）或轻度/中度/重度运动迟缓（平均评分= 2-4），以及是否存在帕金森病的诊断。具有径向基函数核的支持向量机预测轻度/中度/重度运动迟缓的存在，估计测试准确度为0.8。朴素贝叶斯模型预测帕金森病的存在，估计测试准确率为0.67。结论：本文所述的方法提供了一种从手指敲击测试视频预测运动迟缓的方法。该方法对照明条件和相机定位是鲁棒的。在一组试验数据上，运动迟缓预测的准确性与盲态人类专家记录的准确性相当。
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 (Naive 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 Naive 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.