He Tongyue, Chen Junxin, Xu Xu, Fortino Giancarlo, Wang Wei
IEEE Trans Neural Syst Rehabil Eng. 2024;32:3603-3614. doi: 10.1109/TNSRE.2024.3462392. Epub 2024 Sep 27.
With the development of digital medical technology, ubiquitous smartphones are emerging as valuable tools for the detection of complex and elusive diseases. This paper exploits smartphone walking recording for early detection of Parkinson's disease (PD) and finds that walking recording empowered by deep learning is a valid digital biomarker for early-recognizing PD patients. Specifically, the inertial sensor data is preprocessed, including normalization, scaling, and rotation, and then the processed data is fed into the proposed deep NeuroEnhanceNet. Finally, determine the individual prediction score using the PD-prone strategy and generate the detection results. The proposed deep NeuroEnhanceNet, specifically designed for inertial sensor data, can focus on both the long-term data characteristics within a single channel and the inter-channel correlations. Our method obtains a low false negative rate of 0.053 for the early detection of PD. We further analyze and compare the effectiveness of digital biomarkers captured from the walking and resting processes for early detection of PD. All the code for this work is available at: https://github.com/heyiyia/NeuroEnhanceNet.
随着数字医疗技术的发展，无处不在的智能手机正成为检测复杂且难以捉摸的疾病的宝贵工具。本文利用智能手机的步行记录来早期检测帕金森病（PD），并发现深度学习赋能的步行记录是早期识别PD患者的有效数字生物标志物。具体而言，对惯性传感器数据进行预处理，包括归一化、缩放和旋转，然后将处理后的数据输入到所提出的深度神经增强网络（NeuroEnhanceNet）中。最后，使用易患PD策略确定个体预测分数并生成检测结果。所提出的深度神经增强网络专门为惯性传感器数据设计，能够同时关注单个通道内的长期数据特征以及通道间的相关性。我们的方法在PD早期检测中获得了0.053的低假阴性率。我们进一步分析和比较了从步行和休息过程中获取的数字生物标志物对PD早期检测的有效性。这项工作的所有代码可在以下网址获取：https://github.com/heyiyia/NeuroEnhanceNet 。
