SIRS prediction method based on PPG signal

Xiaodong Zhang; Xiaojun Xia; Shuai Wang

doi:10.1117/12.2559762

31 December 2019 SIRS prediction method based on PPG signal

Xiaodong Zhang, Xiaojun Xia, Shuai Wang

Proceedings Volume 11384, Eleventh International Conference on Signal Processing Systems; 113840I (2019) https://doi.org/10.1117/12.2559762
Event: Eleventh International Conference on Signal Processing Systems, 2019, Chengdu, China

Abstract

Photoplethysmographic (PPG) signal is an important body sign data, this paper establishes a physiological model by combining linear dynamics method with important physiological variables (mean arterial pressure and heart rate) extracted from photoplethysmographic (PPG), and verifies the relationship between PPG and SIRS: the reduction in the coupling of mean arterial pressure and heart rate characteristics obtained from PPG signals is significantly associated with systemic inflammatory response syndrome(SIRS) symptoms, which remains conspicuous even though after adjusting clinical intervention. Through PPG signal analysis of 270 adult ICU patients from PhysioNet database, power spectrum and transfer function analysis of the method are carried out, and verifies that the method proposed in this paper can be used to reveal the changes associated with SIRS, which provides a possibility for long-term continuous monitoring or detection of SIRS risk for ICU patients under non-invasive conditions.

Citation Download Citation

Xiaodong Zhang, Xiaojun Xia, and Shuai Wang "SIRS prediction method based on PPG signal", Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 113840I (31 December 2019); https://doi.org/10.1117/12.2559762

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