Ryu Jihye, Torres Elizabeth
Neurosurgery Department, University of California Los Angeles, Los Angeles, California 90095, USA.
Psychology Department, Rutgers University, Piscataway, New Jersey, USA.
Wearable Technol. 2022 Sep 9;3:e21. doi: 10.1017/wtc.2022.16. eCollection 2022.
Multimodal digital data registered with wearable biosensors have emerged as highly complementary of clinical pencil-and-paper criteria, offering new insights in ways to detect and diagnose various aspects of Parkinson's disease (PD). A pressing question is how to combine both the clinical knowledge of PD and the new technology to create interpretable digital biomarkers easily obtainable with off-the-shelf technology. Several challenges concerning disparity in biophysical units, anatomical differences across participants, sensor positioning, and sampling resolution are addressed in this work, along with identification of optimal parameters to automatically differentiate patients with PD from controls. We combine data from a multitude of biosensors registering signals from the central (electroencephalography) and peripheral (magnetometry, kinematics) nervous systems, inclusive of the autonomic nervous system (electrocardiogram), as the participants perform natural tasks requiring different levels of intentional planning and automatic control. We find that magnetometer data during walking, across a variety of amplitude and timing signals, provide optimal separation of PD from neurotypical controls. We conclude that using multimodal signals within the context of actions that bear different levels of intent, can be revealing of features of PD that would scape the naked eye. Further, we add that clinical criteria combined with such optimal digital parameter spaces offer a far more complete picture of PD than using either one of these pieces of data alone.
可穿戴生物传感器记录的多模态数字数据已成为临床纸笔标准的高度补充，为检测和诊断帕金森病（PD）的各个方面提供了新的见解。一个紧迫的问题是如何将PD的临床知识与新技术相结合，以创建可通过现成技术轻松获得的可解释数字生物标志物。这项工作解决了几个有关生物物理单位差异、参与者之间的解剖差异、传感器定位和采样分辨率的挑战，同时还确定了自动区分PD患者和对照组的最佳参数。在参与者执行需要不同程度的有意计划和自动控制的自然任务时，我们结合了来自多个生物传感器的数据，这些传感器记录来自中枢（脑电图）和外周（磁力测量、运动学）神经系统的信号，包括自主神经系统（心电图）。我们发现，在行走过程中，磁力计数据在各种幅度和时间信号上，能将PD患者与神经典型对照组进行最佳区分。我们得出结论，在具有不同意图水平的动作背景下使用多模态信号，可以揭示PD的特征，而这些特征可能会逃过肉眼的观察。此外，我们补充说，临床标准与这种最佳数字参数空间相结合，比单独使用这些数据中的任何一个能提供更完整的PD情况。
