Thanawattano Chusak, Pongthornseri Ronachai, Anan Chanawat, Dumnin Songphon, Bhidayasiri Roongroj
National Electronics and Computer Technology Center (NECTEC), National Science and Technology Development Agency (NSTDA), 112 Thailand Science Park, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand.
Department of Medicine, Faculty of Medicine, Chulalongkorn Center of Excellence for Parkinson Disease and Related Disorders, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, 10330, Thailand.
Biomed Eng Online. 2015 Nov 4;14:101. doi: 10.1186/s12938-015-0098-1.
Parkinson's disease (PD) and essential tremor (ET) are the two most common movement disorders but the rate of misdiagnosis rate in these disorders is high due to similar characteristics of tremor. The purpose of the study is to present: (a) a solution to identify PD and ET patients by using the novel measurement of tremor signal variations while performing the resting task, (b) the improvement of the differentiation of PD from ET patients can be obtained by using the ratio of the novel measurement while performing two specific tasks.
35 PD and 22 ET patients were asked to participate in the study. They were asked to wear a 6-axis inertial sensor on his/her index finger of the tremor dominant hand and perform three tasks including kinetic, postural and resting tasks. Each task required 10 s to complete. The angular rate signal measured during the performance of these tasks was band-pass filtered and transformed into a two-dimensional representation. The ratio of the ellipse area covering 95 % of this two-dimensional representation of different tasks was investigated and the two best tasks were selected for the purpose of differentiation.
The ellipse area of two-dimensional representation of the resting task of PD and ET subjects are statistically significantly different (p < 0.05). Furthermore, the fluctuation ratio, defined as a ratio of the ellipse area of two-dimensional representation of resting to kinetic tremor, of PD subjects were statistically significantly higher than ET subjects in all axes (p = 0.0014, 0.0011 and 0.0001 for x, y and z-axis, respectively). The validation shows that the proposed method provides 100 % sensitivity, specificity and accuracy of the discrimination in the 5 subjects in the validation group. While the method would have to be validated with a larger number of subjects, these preliminary results show the feasibility of the approach.
This study provides the novel measurement of tremor variation in time domain termed 'temporal fluctuation'. The temporal fluctuation of the resting task can be used to discriminate PD from ET subjects. The ratio of the temporal fluctuation of the resting task to the kinetic task improves the reliability of the discrimination. While the method is powerful, it is also simple so it could be applied on low resource platforms such as smart phones and watches which are commonly equipped with inertial sensors.
帕金森病（PD）和特发性震颤（ET）是两种最常见的运动障碍，但由于震颤特征相似，这些疾病的误诊率很高。本研究的目的是：（a）提出一种通过在静息任务期间使用震颤信号变化的新测量方法来识别PD和ET患者的解决方案，（b）通过在执行两项特定任务时使用新测量方法的比率来提高PD与ET患者的鉴别能力。
35名PD患者和22名ET患者被要求参与该研究。他们被要求在震颤优势手的食指上佩戴一个六轴惯性传感器，并执行三项任务，包括动态、姿势和静息任务。每个任务需要10秒完成。在执行这些任务期间测量的角速率信号经过带通滤波并转换为二维表示。研究了覆盖不同任务的二维表示的95%的椭圆面积的比率，并选择两个最佳任务用于鉴别目的。
PD和ET受试者静息任务的二维表示的椭圆面积在统计学上有显著差异（p<0.05）。此外，PD受试者的波动比率（定义为静息震颤与动态震颤的二维表示的椭圆面积之比）在所有轴上均显著高于ET受试者（x、y和z轴的p值分别为0.0014、0.0011和0.0001）。验证表明，所提出的方法在验证组的5名受试者中提供了100%的敏感性、特异性和鉴别准确性。虽然该方法必须用更多受试者进行验证，但这些初步结果表明了该方法的可行性。
本研究提供了一种在时域中称为“时间波动”的震颤变化的新测量方法。静息任务的时间波动可用于区分PD和ET受试者。静息任务与动态任务的时间波动比率提高了鉴别的可靠性。虽然该方法功能强大，但也很简单，因此可以应用于通常配备惯性传感器的低资源平台，如智能手机和手表。
