An Effective Photoplethysmography Denosing Method Based on Diffusion Probabilistic Model.

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ziqing Xia, Zhengding Luo, Chun-Hsien Chen, Xiaoyi Shen
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引用次数: 0

Abstract

Photoplethysmography (PPG) is commonly used to gather health-related information but is highly affected by motion artifacts from daily activities. Inspired by the strong denoising capabilities and generalization of diffusion probabilistic models, this paper proposes a novel PPG denoising method using a diffusion probabilistic model to reduce the impact of these artifacts. While typical diffusion models handle Gaussian noises, motion artifacts often involve non-Gaussian noise. To address this, the proposed method incorporates noisy PPG signals into both the diffusion and reverse processes, allowing the model to adapt better to complex and non-Gaussian noises. A dataset with clean and noisy PPG signals from 15 subjects performing various motion tasks was collected for evaluation. The results show the proposed model significantly improves PPG signal quality, reducing the Peak-Rejection-Rate (PRR) from 0.24 to 0.03. It also enhances the accuracy of heart rate (HR) estimation and various heart rate variability (HRV) measures, showing robustness and good generalization across different tasks and subjects.

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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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