Printy Blake P, Renken Lindsey M, Herrmann John P, Lee Isac, Johnson Bryant, Knight Emily, Varga Georgeta, Whitmer Diane
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:2686-9. doi: 10.1109/EMBC.2014.6944176.
Advanced hardware components embedded in modern smartphones have the potential to serve as widely available medical diagnostic devices, particularly when used in conjunction with custom software and tested algorithms. The goal of the present pilot study was to develop a smartphone application that could quantify the severity of Parkinson's disease (PD) motor symptoms, and in particular, bradykinesia. We developed an iPhone application that collected kinematic data from a small cohort of PD patients during guided movement tasks and extracted quantitative features using signal processing techniques. These features were used in a classification model trained to differentiate between overall motor impairment of greater and lesser severity using standard clinical scores provided by a trained neurologist. Using a support vector machine classifier, a classification accuracy of 0.945 was achieved under 6-fold cross validation, and several features were shown to be highly discriminatory between more severe and less severe motor impairment by area under the receiver operating characteristic curve (AUC > 0.85). Accurate classification for discriminating between more severe and less severe bradykinesia was not achieved with these methods. We discuss future directions of this work and suggest that this platform is a first step toward development of a smartphone application that has the potential to provide clinicians with a method for monitoring patients between clinical appointments.
现代智能手机中嵌入的先进硬件组件有潜力成为广泛可用的医疗诊断设备，尤其是当与定制软件和经过测试的算法结合使用时。本试点研究的目标是开发一款智能手机应用程序，该程序可以量化帕金森病（PD）运动症状的严重程度，特别是运动迟缓。我们开发了一款iPhone应用程序，该程序在引导运动任务期间从一小群PD患者中收集运动学数据，并使用信号处理技术提取定量特征。这些特征被用于一个分类模型中，该模型经过训练，使用训练有素的神经科医生提供的标准临床评分来区分严重程度不同的整体运动障碍。使用支持向量机分类器，在6折交叉验证下实现了0.945的分类准确率，并且通过受试者操作特征曲线下面积（AUC>0.85）表明，几个特征在更严重和不太严重的运动障碍之间具有高度的区分性。使用这些方法未能实现区分更严重和不太严重运动迟缓的准确分类。我们讨论了这项工作的未来方向，并建议该平台是朝着开发智能手机应用程序迈出的第一步，该应用程序有可能为临床医生提供一种在临床预约之间监测患者的方法。
