A method for photoplethysmography signal quality assessment fusing multi-class features with multi-scale series information

• Published in: Sheng wu yi xue gong cheng xue za zhi Vol. 40; no. 3; pp. 536 - 543
• Main Authors: Qi, Yusheng, Zhang, Aihua, Ma, Yurun, Wang, Huidong, Li, Jiaqi, Chen, Cheng
• Format: Journal Article
• Language: Chinese
• Published: China Sichuan Society for Biomedical Engineering 25.06.2023
• Subjects: Accuracy; Artificial neural networks; Deep learning; Long short-term memory; Machine learning; Neural networks; Physiology; Quality assessment; Quality control; Signal quality; Multi-scale time series information; Photoplethysmography; Quality assessment; Multi-class features
• ISSN: 1001-5515
• DOI: 10.7507/1001-5515.202211054

Photoplethysmography (PPG) is often affected by interference, which could lead to incorrect judgment of physiological information. Therefore, performing a quality assessment before extracting physiological information is crucial. This paper proposed a new PPG signal quality assessment by fusing multi-class features with multi-scale series information to address the problems of traditional machine learning methods with low accuracy and deep learning methods requiring a large number of samples for training. The multi-class features were extracted to reduce the dependence on the number of samples, and the multi-scale series information was extracted by a multi-scale convolutional neural network and bidirectional long short-term memory to improve the accuracy. The proposed method obtained the highest accuracy of 94.21%. It showed the best performance in all sensitivity, specificity, precision, and F1-score metrics, compared with 6 quality assessment methods on 14 700 samples from 7 experiments. This paper provide