IEEE Trans Biomed Eng. 2023 Jan;70(1):182-192. doi: 10.1109/TBME.2022.3187309. Epub 2022 Dec 26.
Parkinson's disease (PD) is the second most prevalent neurodegenerative disease disorder in the world. A prompt diagnosis would enable clinical trials for disease-modifying neuroprotective therapies. Recent research efforts have unveiled imaging and blood markers that have the potential to be used to identify PD patients promptly, however, the idiopathic nature of PD makes these tests very hard to scale to the general population. To this end, we need an easily deployable tool that would enable screening for PD signs in the general population. In this work, we propose a new set of features based on keystroke dynamics, i.e., the time required to press and release keyboard keys during typing, and used to detect PD in an ecologically valid data acquisition setup at the subject's homes, without requiring any pre-defined task. We compare and contrast existing models presented in the literature and present a new model that combines a new type of keystroke dynamics signal representation using hold time and flight time series as a function of key types and asymmetry in the time series using a convolutional neural network. We show how this model achieves an Area Under the Receiving Operating Characteristic curve ranging from 0.80 to 0.83 on a dataset of subjects who actively interacted with their computers for at least 5 months and positively compares against other state-of-the-art approaches previously tested on keystroke dynamics data acquired with mechanical keyboards.
帕金森病（PD）是世界上第二大常见的神经退行性疾病。及时诊断将使针对神经保护性治疗的临床试验成为可能。最近的研究努力揭示了成像和血液标志物，这些标志物有可能被用于及时识别 PD 患者，然而，PD 的特发性性质使得这些测试很难推广到一般人群。为此，我们需要一种易于部署的工具，以便在一般人群中进行 PD 症状筛查。在这项工作中，我们提出了一组基于按键动力学的新特征，即打字时按下和释放键盘键所需的时间，并用于在主题家中的生态有效数据采集设置中检测 PD，而无需任何预定义的任务。我们比较和对比了文献中提出的现有模型，并提出了一种新的模型，该模型结合了一种新类型的按键动力学信号表示，使用保持时间和飞行时间序列作为键类型和时间序列中的不对称性的函数，使用卷积神经网络。我们展示了该模型如何在至少与主动与计算机交互 5 个月的主题数据集上实现 0.80 到 0.83 的接收者操作特征曲线下面积，并且与之前在使用机械键盘采集的按键动力学数据上测试的其他最先进方法相比具有优势。
