Waveform Analysis for Camera-based Photoplethysmography Imaging

Paul, Michael; Yu, Xinchi; Wu, Bin; Weiss, Christoph; Antink, Christoph Hoog; Blažek, Vladimír; Leonhardt, Steffen

doi:10.1109/embc.2019.8857581

Cited by 4 publications

(2 citation statements)

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“…1). This is typically the PPG amplitude (e.g., [8,20]) but can also include the blood pulse phase and other waveform parameters [1,15,[21][22][23]. The approaches of Verkruysse et al and Kumar et al are particularly noteworthy for this paper: Verkruysse and co-workers [21] defined and generated power maps and phase maps at single frequencies, i.e., they analyzed the Fourier spectrum at the known heart frequency and mapped the signal power.…”

Section: State Of the Artmentioning

confidence: 99%

Spatio-temporal and -spectral feature maps in photoplethysmography imaging and infrared thermography

et al. 2021

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Background Only a small fraction of the information available is generally used in the majority of camera-based sensing approaches for vital sign monitoring. Dedicated skin pixels, for example, fall into this category while other regions are often disregarded early in the processing chain. Methods We look at a simple processing chain for imaging where a video stream is converted to several other streams to investigate whether other image regions should also be considered. These streams are generated by mapping spatio-temporal and -spectral features of video segments and, thus, compressing the information contained in several seconds of video and encoding these in a new image. Two typical scenarios are provided as examples to study the applicability of these maps: face videos in a laboratory setting and measurements of a baby in the neonatal intensive care unit. Each measurement consists of the synchronous recording of photoplethysmography imaging (PPGI) and infrared thermography (IRT). We report the results of a visual inspection of those maps, evaluate the root mean square (RMS) contrast of foreground and background regions, and use histogram intersections as a tool for similarity measurements. Results The maps allow us to distinguish visually between pulsatile foreground objects and an image background, which is found to be a noisy pattern. Distortions in the maps could be localized and the origin could be discovered. The IRT highlights subject contours for the heart frequency band, while silhouettes show strong signals in PPGI. Reflections and shadows were found to be sources of signals and distortions. We can testify advantages for the use of near-infrared light for PPGI. Furthermore, a difference in RMS contrast for pulsatile and non-pulsatile regions could be demonstrated. Histogram intersections allowed us to differentiate between the background and foreground. Conclusions We introduced new maps for the two sensing modalities and presented an overview for three different wavelength ranges. The maps can be used as a tool for visualizing aspects of the dynamic information hidden in video streams without automation. We propose focusing on an indirect method to detect pulsatile regions by using the noisy background pattern characteristic, for example, based on the histogram approach introduced.

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Section: State Of the Artmentioning

confidence: 99%

Spatio-temporal and -spectral feature maps in photoplethysmography imaging and infrared thermography

et al. 2021

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“…However, similar to the conventional PPG waveform, the PPGI waveform can be assumed to feature more information. Only a few works have used morphological analysis to capture this information so far (Trumpp et al 2016, Kamshilin et al 2017, Trumpp et al 2017, Fleischhauer et al 2019, Djeldjli et al 2019, Nishidate et al 2019, Paul et al 2019. These works mostly analyzed the amplitude of single pulses, with the exception of the works of Paul et al (2019) and Djeldjli et al (2019), which employed more complex pulse wave analysis features.…”

Section: Introductionmentioning

confidence: 99%

Pulse decomposition analysis in photoplethysmography imaging

et al. 2020

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Objective: Photoplethysmography imaging (PPGI) has gained immense attention over the last few years but only a few works have addressed morphological analysis so far. Pulse wave decomposition (PWD), i.e. the decomposition of a pulse wave by a varying number of kernels, allows for such analyses. This work investigates the applicability of PWD algorithms in the context of PPGI. Approach: We used simulated and experimental data to compare various PWD algorithms from the literature regarding their robustness against noise and motion artifacts while preserving morphological information as well as regarding their ability to reveal physiological changes by PPGI. Main results: Our experiments prove that algorithms that combine Gamma and Gaussian distributions outperform other choices. Further, algorithms with two kernels exhibit the highest robustness against noise and motion artifacts (improvement in N R M S E ‾ of 14.09 %) while preserving the morphology similarly to algorithms using more kernels. Lastly, we showed that PWD can reveal physiological changes upon distal stimuli by PPGI. Significance: This work proves the feasibility of pulse decomposition analysis in PPGI, particularly for algorithms with a low number of kernels, and opens up novel applications for PPGI. Not only for PPGI but for future research on PWD in general, our findings have importance as they elucidate differences between PWD algorithms and emphasize the importance of using initial values. To support such future research, we have released the algorithms and simulated data to the public.

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