Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, A. Boboli 8 St., 02-525 Warsaw, Poland.
Department of Neurology, Faculty of Health Science, Medical University of Warsaw, Żwirki i Wigury 61 St., 02-091 Warsaw, Poland.
Sensors (Basel). 2019 Dec 28;20(1):184. doi: 10.3390/s20010184.
Parkinson's disease results in motor impairment that deteriorates patients' quality of life. One of the symptoms negatively interfering with daily activities is kinetic tremor which should be measured to monitor the outcome of therapy. A new instrumented method of quantification of the kinetic tremor is proposed, based on the analysis of circles drawn on a digitizing tablet by a patient. The aim of this approach is to obtain a tremor scoring equivalent to that performed by trained clinicians. Models are trained with the least absolute shrinkage and selection operator (LASSO) method to predict the tremor scores on the basis of the parameters computed from the patients' drawings. Signal parametrization is derived from both expert knowledge and the response of an artificial neural network to the raw data, thus the approach was named multimodal. The fitted models are eventually combined into model ensembles that provide aggregated scores of the kinetic tremor captured in the drawings. The method was verified with a set of clinical data acquired from 64 Parkinson's disease patients. Automated and objective quantification of the kinetic tremor with the presented approach yielded promising results, as the Pearson's correlations between the visual ratings of tremor and the model predictions ranged from 0.839 to 0.890 in the best-performing models.
帕金森病导致运动障碍，降低患者的生活质量。其中一个对日常生活活动产生负面影响的症状是运动性震颤，应该进行测量以监测治疗效果。本文提出了一种新的量化运动性震颤的仪器化方法，该方法基于对患者在数字化仪上绘制的圆的分析。这种方法的目的是获得与经过训练的临床医生进行的震颤评分等效的结果。使用最小绝对收缩和选择算子（LASSO）方法对模型进行训练，以便根据从患者绘图中计算出的参数来预测震颤评分。信号参数化是从专家知识和人工神经网络对原始数据的响应中得出的，因此该方法被命名为多模态。拟合的模型最终组合成模型集合，为绘图中捕获的运动性震颤提供综合评分。该方法通过从 64 名帕金森病患者获得的一组临床数据进行了验证。所提出的方法对运动性震颤进行了自动化和客观的量化，结果令人鼓舞，因为在表现最佳的模型中，震颤的视觉评估与模型预测之间的 Pearson 相关系数从 0.839 到 0.890 不等。
