Knowledge Commons of Institute of Automation,CAS
A time-series augmentation method based on empirical mode decomposition and integrated LSTM neural network | |
chenguang li; hongjun yang; long cheng | |
2022-07 | |
会议名称 | the 44th International Engineering in Medicine and Biology Conference |
会议日期 | 2022-07 |
会议地点 | Glasgow |
摘要 | Adequate patients’ data have always been critical for disease assessment. However, large amounts of patient data are often difficult to collect, especially when patients are required to complete a series of assessment movements. For example, assessing the hand motor function of stroke patients or Parkinson’s patients requires patients to complete a series of evaluation movements, and it is often difficult for patients to complete each group of actions multiple times, resulting in a small amount of data. To solve the problem of insufficient data quantity, this study proposes a data augmentation method based on empirical mode decomposition and integrated long short-term memory neural network (EMD-ILSTM). The method mainly consists of two parts: one is to decompose the raw signal by the method of EMD, and the other is to use LSTM for data augmentation of the decomposed signal. Then, the method is tested on the public dataset named Ninaweb, and the test results show that the classification accuracy can be improved by 5.2%by using the augmented data for classification tasks. Finally, clinical trials are conducted to verify that after dimensionality reduction, the augmented data and raw data have smaller intra-class distances and larger inter-class distances, indicating that data augmentation is effective. |
收录类别 | EI |
七大方向——子方向分类 | 机器学习 |
国重实验室规划方向分类 | 智能能力评估 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52115 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
通讯作者 | long cheng |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | chenguang li,hongjun yang,long cheng. A time-series augmentation method based on empirical mode decomposition and integrated LSTM neural network[C],2022. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
A Time-Series Augmen(1725KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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