Few-shot pulse wave contour classification based on multi-scale feature extraction

• Published in: Scientific reports Vol. 11; no. 1; pp. 3762 - 11
• Main Authors: Lu, Peng, Liu, Chao, Mao, Xiaobo, Zhao, Yvping, Wang, Hanzhang, Zhang, Hongpo, Guo, Lili
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.02.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/985; 639/705/117; 692/699/75; Blood pressure; Cardiovascular diseases; Classification; Datasets; Deep learning; Humanities and Social Sciences; multidisciplinary; Neural networks; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-83134-y

The annotation procedure of pulse wave contour (PWC) is expensive and time-consuming, thereby hindering the formation of large-scale datasets to match the requirements of deep learning. To obtain better results under the condition of few-shot PWC, a small-parameter unit structure and a multi-scale feature-extraction model are proposed. In the small-parameter unit structure, information of adjacent cells is transmitted through state variables. Simultaneously, a forgetting gate is used to update the information and retain long-term dependence of PWC in the form of unit series. The multi-scale feature-extraction model is an integrated model containing three parts. Convolution neural networks are used to extract spatial features of single-period PWC and rhythm features of multi-period PWC. Recursive neural networks are used to retain the long-term dependence features of PWC. Finally, an inference layer is used for classification through extracted features. Classification experiments of cardiovascular diseases are performed on photoplethysmography dataset and continuous non-invasive blood pressure dataset. Results show that the classification accuracy of the multi-scale feature-extraction model on the two datasets respectively can reach 80% and 96%, respectively.