Electrical and Computer Systems Engineering & Advanced Engineering Platform, School of Engineering, Monash University, Bandar Sunway, Malaysia.
PLoS One. 2019 Jun 27;14(6):e0219114. doi: 10.1371/journal.pone.0219114. eCollection 2019.
Giancardo et al. recently introduced the neuroQWERTY index (nQi), which is a novel motor index derived from computer-key-hold-time data using an ensemble regression algorithm, to detect early-stage Parkinson's disease. Here, we derive a much simpler motor index from their hold-time data, which is the standard deviation (SD) of the hold-time fluctuations, where fluctuation is defined as the difference between successive natural-log of hold time. Our results show the performance of the SD and nQi tests in discriminating early-stage subjects from controls do not differ, although the SD index is much simpler. There is also no difference in performance between the SD and alternating-finger-tapping tests.
吉安卡尔多等人最近引入了神经 QWERTY 指数(nQi),这是一种使用集成回归算法从计算机按键保持时间数据中得出的新型运动指数,用于检测早期帕金森病。在这里,我们从他们的保持时间数据中推导出一个简单得多的运动指数,即保持时间波动的标准差(SD),其中波动定义为连续自然对数保持时间之间的差异。我们的结果表明,SD 指数和 nQi 测试在区分早期患者和对照组方面的性能没有差异,尽管 SD 指数更为简单。SD 指数和交替手指敲击测试之间的性能也没有差异。