Signal quality index-based dynamic weighted fusion method for multimodal respiratory rate estimation(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2026年第1期
- Page:
- 99-109
- Research Field:
- 医学信号处理与医学仪器
- Publishing date:
Info
- Title:
- Signal quality index-based dynamic weighted fusion method for multimodal respiratory rate estimation
- Author(s):
- LIU Wenzhe1; ZHENG Yibo2; LIU Qiang2; LIU Yongwei1
- 1. School of Information Engineering, Hebei GEO University, Shijiazhuang 050031, China 2. Hebei Key Laboratory of Optoelectronic Information and Geo-detection Technology, Shijiazhuang 050031, China
- Keywords:
- Keywords: respiratory rate estimation signal quality index electrocardiogram photoplethysmogram multimodal fusion
- PACS:
- R318.04；TP274
- DOI:
- DOI:10.3969/j.issn.1005-202X.2026.01.013
- Abstract:
- Abstract: A dynamic weighted fusion method based on signal quality index (SQI) is proposed for multimodal respiratory rate estimation, aiming to improve the accuracy and robustness of non-invasive continuous respiratory monitoring. This method combines electrocardiogram-derived respiratory signals with the Hilbert envelope signals of photoplethysmogram, and constructs a fusion model by leveraging their physiological complementarity. Additionally, SQI is calculated in real time to quantify the reliability of each modal signal, and the quantified results are used to dynamically assign fusion weights, which enables adaptive adjustment of the contribution ratios of different signal sources, thereby effectively addressing signal quality fluctuations. Experimental validation on the CapnoBase database shows that the proposed SQI-based fusion method maintains a high degree of consistency with reference respiratory signal at the waveform level, achieving a mean Pearson correlation coefficient of 0.818 1 and a significant reduction in waveform reconstruction errors. In terms of respiratory rate estimation, the proposed method exhibits high accuracy and stability, with an average absolute error of only 0.36 breaths/min, and the performance improvement is particularly pronounced in signal interference scenarios. This study validates the effectiveness of SQI-based multimodal fusion in enhancing the anti-interference capability and overall performance of respiratory estimation systems, thus providing an innovative and practical solution for high-reliability continuous respiratory monitoring in smart healthcare and wearable devices.
Last Update: 2026-01-27