Fu Zhiwen, Lin Congping, Zhang Yiwei
Department of Mathematics and SUSTech International Center for Mathematics, Southern University of Science and Technology, Shenzhen, China.
School of Mathematics and Statistics, Hubei Key Lab of Engineering Modelling and Scientific, Center for Mathematical Sciences, Huazhong University of Science and Technology, Wuhan, China.
Sci Rep. 2025 May 28;15(1):18749. doi: 10.1038/s41598-025-88110-4.
Freezing of gait (FoG) is a common severe gait disorder in patients with advanced Parkinson's disease. The ability to predict the onset of FoG episodes early on allows for timely intervention, which is essential for improving the life quality of patients. Machine learning and deep learning, the current methods, face real-time diagnosis challenges due to comprehensive data processing requirements. Their "black box" nature makes interpreting features and classification boundaries difficult. In this manuscript, we explored a dynamic mode decomposition (DMD)-based approach together with optimal delay embedding time to reconstruct and predict the time evolution of acceleration signals, and introduced a triple index based on DMD to predict and classify FoG. Our predictive analysis shows 86.5% accuracy in classification, and an early prediction ratio of 81.97% with an average early prediction time of 6.13 s. This DMD-based approach has the potential for real-time patient-specific FoG prediction.
冻结步态（FoG）是晚期帕金森病患者常见的严重步态障碍。能够早期预测FoG发作的开始时间有助于及时进行干预，这对于提高患者的生活质量至关重要。机器学习和深度学习作为当前的方法，由于需要全面的数据处理，面临实时诊断挑战。它们的“黑箱”性质使得解释特征和分类边界变得困难。在本手稿中，我们探索了一种基于动态模式分解（DMD）的方法，并结合最优延迟嵌入时间来重构和预测加速度信号的时间演变，并引入了基于DMD的三重指标来预测和分类FoG。我们的预测分析显示分类准确率为86.5%，早期预测率为81.97%，平均早期预测时间为6.13秒。这种基于DMD的方法具有针对患者进行实时FoG预测的潜力。
