Bazgir Omid, Habibi Seyed Amir Hassan, Palma Lorenzo, Pierleoni Paola, Nafees Saba
Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, Texas, USA.
Department of Electrical and Computer Engineering, University of Tabriz, Iran.
J Med Signals Sens. 2018 Apr-Jun;8(2):65-72.
Tremor is one of the most common symptoms of Parkinson's disease (PD), which is widely being used in the diagnosis procedure. Accurate estimation of PD tremor based on Unified PD Rating Scale (UPDRS) provides aid for physicians in prescription and home monitoring. This article presents a robust design of a classification system to estimate PD patient's hand tremors and the results of the proposed system as compared to the UPDRS.
A smartphone accelerometer sensor is used for accurate and noninvasive data acquisition. We applied short-time Fourier transform to time series data of 52 PD patients. Features were extracted based on the severity of PD patients' hand tremor. The wrapper method was employed to determine the most discriminative subset of the extracted features. Four different classifiers were implemented for achieving best possible accuracy in the estimation of PD hand tremor based on UPDRS. Of the four tested classifiers, the Naive Bayesian approach proved to be the most accurate one.
The classification result for the assessment of PD tremor achieved close to 100% accuracy by selecting an optimum combination of extracted features of the acceleration signal acquired. For home health-care monitoring, the proposed algorithm was also implemented on a cost-effective embedded system equipped with a microcontroller, and the implemented classification algorithm achieved 93.8% average accuracy.
The accuracy result of both implemented systems on MATLAB and microcontroller is acceptable in comparison with the previous works.
震颤是帕金森病（PD）最常见的症状之一，在诊断过程中被广泛应用。基于统一帕金森病评定量表（UPDRS）准确估计PD震颤可为医生的处方和家庭监测提供帮助。本文提出了一种用于估计PD患者手部震颤的分类系统的稳健设计，并将该系统的结果与UPDRS进行了比较。
使用智能手机加速度计传感器进行准确且无创的数据采集。我们将短时傅里叶变换应用于52名PD患者的时间序列数据。根据PD患者手部震颤的严重程度提取特征。采用包装法确定所提取特征中最具判别力的子集。为了基于UPDRS在估计PD手部震颤方面实现尽可能高的准确率，实施了四种不同的分类器。在这四种测试分类器中，朴素贝叶斯方法被证明是最准确的。
通过选择所采集加速度信号提取特征的最佳组合，PD震颤评估的分类结果准确率接近100%。对于家庭医疗保健监测，所提出的算法还在配备微控制器的经济高效嵌入式系统上实施，实施的分类算法平均准确率达到93.8%。
与先前的研究相比，在MATLAB和微控制器上实施的两个系统的准确率结果是可接受的。
