Department of Computer Science, School of Computing, Tokyo Institute of Technology, Yokohama, 226-8502, Japan.
Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Yokohama, 226-8502, Japan.
Sci Rep. 2023 Aug 3;13(1):12638. doi: 10.1038/s41598-023-39862-4.
Data-augmentation methods have emerged as a viable approach for improving the state-of-the-art performances for classifying mild Parkinson's disease using deep learning with time-series data from an inertial measurement unit, considering the limited amount of training datasets available in the medical field. This study investigated effective data-augmentation methods to classify mild Parkinson's disease and healthy participants with deep learning using a time-series gait dataset recorded via a shank-worn inertial measurement unit. Four magnitude-domain-transformation and three time-domain-transformation data-augmentation methods, and four methods involving mixtures of the aforementioned methods were applied to a representative convolutional neural network for the classification, and their performances were compared. In terms of data-augmentation, compared with baseline classification accuracy without data-augmentation, the magnitude-domain transformation performed better than the time-domain transformation and mixed-data augmentation. In the magnitude-domain transformation, the rotation method significantly contributed to the best performance improvement, yielding accuracy and F1-score improvements of 5.5 and 5.9%, respectively. The augmented data could be varied while maintaining the features of the time-series data obtained via the sensor for detecting mild Parkinson's in gait; this data attribute may have caused the aforementioned trend. Notably, the selection of appropriate data extensions will help improve the classification performance for mild Parkinson's disease.
数据增强方法已成为一种可行的方法，可用于使用惯性测量单元的时间序列数据来改善深度学习对轻度帕金森病的分类性能，因为在医学领域中可用的训练数据集数量有限。本研究调查了有效的数据增强方法，以使用通过腿部佩戴的惯性测量单元记录的时间序列步态数据集进行深度学习来分类轻度帕金森病和健康参与者。将四种幅度域变换和三种时域变换数据增强方法以及涉及上述方法组合的四种方法应用于代表性的卷积神经网络进行分类，并比较了它们的性能。在数据增强方面，与没有数据增强的基线分类准确性相比，幅度域变换的性能优于时域变换和混合数据增强。在幅度域变换中，旋转方法对最佳性能改进的贡献最大，精度和 F1 分数分别提高了 5.5%和 5.9%。通过传感器获得的时间序列数据可以在保持其特征的同时改变扩充数据，用于检测步态中的轻度帕金森病；这种数据属性可能导致了上述趋势。值得注意的是，选择适当的数据扩展将有助于提高轻度帕金森病的分类性能。
