Concordia Institute for Information Systems Engineering, Concordia University, Montreal, H3G 1M8, QC, Canada.
Departments of Electrical and Computer Engineering, and Mechanical and Aerospace Engineering, New York University, New York, 10003, NY, USA.
Sci Rep. 2020 Feb 10;10(1):2195. doi: 10.1038/s41598-020-58912-9.
The global aging phenomenon has increased the number of individuals with age-related neurological movement disorders including Parkinson's Disease (PD) and Essential Tremor (ET). Pathological Hand Tremor (PHT), which is considered among the most common motor symptoms of such disorders, can severely affect patients' independence and quality of life. To develop advanced rehabilitation and assistive technologies, accurate estimation/prediction of nonstationary PHT is critical, however, the required level of accuracy has not yet been achieved. The lack of sizable datasets and generalizable modeling techniques that can fully represent the spectrotemporal characteristics of PHT have been a critical bottleneck in attaining this goal. This paper addresses this unmet need through establishing a deep recurrent model to predict and eliminate the PHT component of hand motion. More specifically, we propose a machine learning-based, assumption-free, and real-time PHT elimination framework, the PHTNet, by incorporating deep bidirectional recurrent neural networks. The PHTNet is developed over a hand motion dataset of 81 ET and PD patients collected systematically in a movement disorders clinic over 3 years. The PHTNet is the first intelligent systems model developed on this scale for PHT elimination that maximizes the resolution of estimation and allows for prediction of future and upcoming sub-movements.
全球老龄化现象增加了与年龄相关的神经运动障碍患者的数量，包括帕金森病（PD）和特发性震颤（ET）。病理性手震颤（PHT）被认为是此类疾病最常见的运动症状之一，它会严重影响患者的独立性和生活质量。为了开发先进的康复和辅助技术，准确估计/预测非平稳 PHT 至关重要，但尚未达到所需的精度水平。缺乏能够充分代表 PHT 的时频谱特征的大规模数据集和可推广的建模技术一直是实现这一目标的关键瓶颈。本文通过建立一个深度递归模型来预测和消除手部运动中的 PHT 成分来满足这一未满足的需求。更具体地说，我们通过结合深度双向递归神经网络，提出了一种基于机器学习的、无假设的、实时的 PHT 消除框架，即 PHTNet。该 PHTNet 是在一个运动障碍诊所 3 年来系统收集的 81 名 ET 和 PD 患者的手部运动数据集中开发的。PHTNet 是第一个针对 PHT 消除开发的此类规模的智能系统模型，它最大限度地提高了估计的分辨率，并允许预测未来和即将到来的子运动。
