Factitious Hemoperfusion Based ROI Optimization in PPGi Analysis and its Application for Blood Pressure Determination
Journal Title: Biomedical Journal of Scientific & Technical Research (BJSTR) - Year 2019, Vol 13, Issue 1
Abstract
Photoplethysmography Imaging (PPGi) is an emerging technology to monitor physiological parameters. However, the signal-to-noise ratio in PPGi usually is low due to inappropriate selection of Region of Interest (ROI). Herein, to enhance the signal-to-noise ratio, an optimization approach for ROI selection is proposed based on factitious hemoperfusion. Factitious hemoperfusion is able to amplify instantaneous blood volume and augment the volume of the blood vessel, and such an amplification will facilitate the identification of dense vessel regions as the optimized ROIs for PPGi. Furthermore, a self-adaptive PPGi denoising method is created to purify PPGi data, and an approach calculating Blood Pressure (BP) from PPGi signals is proposed and developed. Through comparing the calculated BP values to the ones measured using a commercial sphygmomanometer, it is found that the BP values resulted from the optimized ROIs are closer to the measured ones (with an error rate less than 5%).The heart rhythms accompanied by systole and diastole can change the blood volume in the vessels under the skin surface, and further alter the light absorption/reflection of vessels. Thus, the changes on the light absorption/reflection of vessels can be detected by a photoelectronic component. Based on this PhotoElectronic Transformation (PET) principle [1] technologies for monitoring physiological parameters (like heart rate [2], Blood Pressure (BP) [3], respiratory rate [4] etc.,) have been developed. Among these technologies, Photoplethysmography Imaging (PPGi) is an emerging trend. PPGi is an evolved photoplethysmography (PPG) [5] with the merits of noncontact and remote sensing [6]. Particularly, for patients with skin injuries, refusing treatments or demanding long-term supervision, PPGi is an ideal preference to monitor their physiological parameters. To acquire correct vital signs, three parts are needed in the PPGi system, which include video collection, image processing and algorithm to extract physiological information. There have been lots of reported works for each of them. For instance, studies about video collection are mainly focused on camera [7], light-source [8] and their spatial distribution [9]; nowadays, phone collected [10] and ambient light excited [11] PPGi signal collections have been realized. Image processing calculation is an inescapable step in PPGi technique, in order to get a correct and valuable waveform, lots of strategies have been exploited to enhance the Signal-to-Noise Ratio (SNR), such as the methods of motion compensation [12,13], Fourier Transform (FFT) [14], wavelet transform [15,16]. To facilitate PPGi’s applications, there are efforts in extracting physiological parameters from PPGi data. For examples, the value of relative oxygen saturation can be estimated by mapping multi-layered blood perfusion and structuring opto-physiological model [17]; the breathing frequency is tracked via differential signal processing algorithm with the maximized signals and the minimized noise [18]; based on head motions the pulse rate variability is extracted from blood volume pulse by fusing partial color channel signals of multiple imagers, source signal separation and the autoregressive model [19], etc. In conclusion, it has been demonstrated that PPGi is a potential and reliable vital sign monitoring technique [20].
Authors and Affiliations
Fubo Deng, Xiaoshan Zhu, Yunfang Jia
Keywords
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- EP ID EP594408
- DOI 10.26717/BJSTR.2019.13.002356
- Views 159
- Downloads 0
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