Paper Info
Title
A Time-Series Augmentation Method Based on Empirical Mode Decomposition and Integrated LSTM Neural Network
Abstract
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.
Year
DOI
Venue
2022
10.1109/EMBC48229.2022.9871795
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Keywords
DocType
Volume
Hand,Humans,Movement,Neural Networks, Computer,Reconstructive Surgical Procedures,Time Factors
Conference
2022
ISSN
ISBN
Citations 
2375-7477
978-1-7281-2783-5
0
PageRank 
References 
Authors
0.34
6
4
Name
Order
Citations
PageRank
Chenguang Li100.34
Hongjun Yang200.34
Long Cheng3149273.97
Fubiao Huang400.34