Photoplethysmogram at green light: Where does chaos arise from?

Nina Sviridova, Tiejun Zhao, Kazuyuki Aihara, Kazuyuki Nakamura and Akimasa Nakano

Chaos, Solitons & Fractals, 2018, vol. 116, issue C, 157-165

Abstract: Photoplethysmography has been routinely used for health monitoring in the past decades. Even though in daily medical practice, photoplethysmograms (PPGs) are recorded at red and near infra-red light (rPPGs), during the last decade, PPGs obtained at green light (gPPGs) have been widely used in wearable devices, such as wristbands and smartwatches, thus providing highly usable and accessible daily health monitoring. It is well recognized that PPG signals obtained at NIR light contain sufficient information about a person's health; furthermore, they are well-studied and highly complex. By contrast, green light PPGs have not been sufficiently studied; thus, it is not clear whether the provided information is as valuable as that obtained by rPPGs, as green light photoplethysmography is formed at papillary dermis and higher skin levels. However, the gPPG signal is recognized to be more robust to motion artifacts compared with rPPGs. As rPPG dynamics has been recognized as chaotic, this study is aimed at investigating the properties of gPPGs and compare them with those of rPPGs to determine whether gPPG dynamics is chaotic as well. The motivation for this question was not only to investigate whether gPPGs can be used for the same range of applications as rPPGs but also to obtain insight into whether the complexity of rPPGs is solely due to processes in deeper layers of the tissue or it is influenced by the higher-skin layers processes that create gPPGs. The obtained results demonstrated that gPPGs, as well as rPPGs, are chaotic, which implies that processes in the upper layers create chaos in PPG data, whereas the contribution of arterial blood volume changes is not completely clear.

Keywords: Photoplethysmogram; Chaos; Nonlinear time series analysis; Nonlinear dynamics; Cutaneous blood circulation (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:116:y:2018:i:c:p:157-165

DOI: 10.1016/j.chaos.2018.09.016

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