Remote PPG Imaging by a Consumer-grade Camera under Rest and

Elevation-invoked Physiological Stress Reveals Mayer Waves and

Venous Outflow

Timothy Burton

, Gennadi Saiko

and Alexandre Douplik

2,3 b

Department of Biomedical Engineering, Ryerson University, Toronto, Canada

Department of Physics, Ryerson University, Toronto, Canada

iBest, Keenan Research Centre of the LKS Knowledge Institute, St. Michael Hospital, Toronto, Canada

Keywords: Photoplethysmography, Microvasculature, Hemodynamics, Contactless, Remote, Bioimaging.

Abstract: Introduction: The photoplethysmographic (PPG) signal contains information about microvascular

hemodynamics, including endothelial-related metabolic, neurogenic, myogenic, respiratory, and cardiac

activities. The present goal is to explore the utility of a consumer-grade smartphone camera as a tool to

study such activities. Traditional PPG is conducted using a contact method, but the resultant contact

pressure can affect venous flow distribution and distort perfusion examination. This motivates us to develop

a remote PPG method (rPPG) to study such activities. Methods: We used an imaging setup composed of a

stand-mounted consumer grade camera (iPhone 8) with on-board LED illumination. The camera acquired

1920x1080 video data at 60 frames per second (fps); 90 second videos were captured for a hand in rest and

elevated positions. Spatial averaging was performed to extract rPPG, which was filtered using continuous

wavelet transform to analyse frequency ranges of interest. Results: The data demonstrated a plurality of

observed patterns, which differed between rest and elevation positions. In addition to cardiac and respiratory

activities, we noticed another two distinct low frequency patterns: oscillations that we conclude are likely

Mayer waves, and monotonic reflection increase (gravitational venous outflow). In some cases, these two

patterns are combined. Conclusions: rPPG demonstrated potential for venous compartment examinations.

https://orcid.org/ 0000-0002-5697-7609

https://orcid.org/ 0000-0001-9948-9472

1 INTRODUCTION

Photoplethysmography, or PPG, works by

measuring the changes in absorption of light through

tissue. The origin of the PPG signal is still a topic of

active debate, but the generally accepted origins,

which may also be correlated/overlapping, include

changes in blood volume within the tissue, position

and geometry of red blood cells, and mechanical

motion of capillaries (Kyriacou & Allen, 2021). In a

typical scenario, PPG measurements are collected at

a single wavelength, usually within the infrared

range (700-900 nm), at which hemoglobin absorbs

light at a reduced level. If the tissue oxygenation is

required, then two wavelengths (typically in red and

infrared ranges of spectrum) are used (Wukitsch et

al, 1988).

A conventional contact PPG signal can be

captured using one of two acquisition modes:

transmission or reflection. In transmission (most

used clinically), a light emitter and receiver are

positioned on either side of tissue (such as finger),

and the attenuated light is captured by the receiver

after transiting through the tissue. In contrast, in

reflection-mode PPG, the light emitter and receiver

are both situated on the same side of the tissue.

Emitted light interacts with the tissue, some of

which is backscattered and captured by the detector.

Popularity of this contact modality is increasing in

consumer applications, and can now be found in

multiple smart watches and other wearable devices.

In the most common scenario for the contact

PPG, pulse oxymetry, a wealth of physiological

information can be extracted from this single-point

measurement. In addition to the tissue oxygenation

Burton, T., Saiko, G. and Douplik, A.

Remote PPG Imaging by a Consumer-grade Camera under Rest and Elevation-invoked Physiological Stress Reveals Mayer Waves and Venous Outﬂow.

DOI: 10.5220/0010883100003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING, pages 153-159

ISBN: 978-989-758-552-4; ISSN: 2184-4305

153

(Nitzan & Engelberg, 2009), several other

physiological parameters can be extracted, including

heart rate (Temko, 2017), heart rate variability (Lin

et al., 2014), as well as respiratory rate (Daimiwa et

al., 2014). From this information, other parameters,

including blood pressure (Kurylyak et al., 2013), can

be inferred. However, the analysis of PPG signals

can be challenging, due to characteristics of

biological signals in general. For example, non-

stationarity, which is the time dependence of

statistical properties such as standard deviation and

mean in the signal, necessitates careful selection of

signal processing technique to accurately quantify

signal properties (Usui & Toda, 1991).

Remote PPG (rPPG) is an adaptation of

transmission mode PPG in which a camera is used to

capture the backscattered light from the tissue.The

primary advantage of remote PPG over contact PPG

lies in its contactless nature, rendering it suitable for

applications with sensitive tissue (such as wounds,

burns, neurological conditions, etc.) in addition to

addressing other limitations of contact PPG. For

instance, the contactless property means that it does

not rely on robust skin-to-sensor contact, which is

necessary for a strong PPG signal acquired using the

contact method. Maintaining high fidelity skin to

sensor contact is also made more difficult by the

sensor being mechanically fixed to the skin,

requiring external pressure (e.g., spring-loaded

finger clip) that can have a significant effect on PPG

signal quality and reproducibility. Further, contact

PPG has high sensitivity to motion artifacts, and

therefore requires the patient to stay very still.

In the most typical acquisition scenario, rPPG is

used to capture a single PPG signal over a whole

tissue area, such as the palm (Zheng et al., 2008) or

face (Zheng et al., 2009). However, the utility of

rPPG goes beyond tissue oxygenation. For example,

several PPG devices placed on the skin can be used

to extract pulse wave velocity (PWV), which

demonstrates a significant clinical value (e.g.

baPWV (Katakami et al., 2014)).

With a large enough field of view, the same data

can be collected using video rPPG, which registers

rPPG signals for segments of the field of view (in

contrast to a single signal from the entire field of

view). Several recently proposed imaging modalities

take advantage of the multi-pixel nature of rPPG and

aim to extract additional physiological information

from spatially resolved rPPG signals. For example,

(Saiko et al., 2021) used a high frame rate camera to

analyze pulse wave velocity in peripheral blood

vessels. Similarly, (Burton et al., 2021) used

spatially

resolved PPG signal to extract information

about tissue perfusion.

However, as we go beyond typical PPG utility,

complexity rises. The hemodynamics of the

microcirculation are extremely complex, with

multiple autoregulatory systems at play.

The predominant signal source in the PPG is the

cardiac pulsation caused by the ejection of blood

from the left ventricle during cardiac systole, which

affects the origins previously described. Heart rate

for normal subjects at rest varies from 60-100 beats

per minute (bpm) (John Hopkins Medicine, 2021).

Conservatively extending the lower bound to 50pm

to consider lower resting heart rates that can occur in

certain people, such as athletes (Doyen et al., 2019),

then the corresponding frequency range is 0.83-

1.67Hz. As previously mentioned, respiration can

also be extracted from PPG signals. The normal

respiration rate for a healthy subject is 12 to 20

breaths per minute (Cleveland Clinic, 2021),

corresponding to a frequency range of 0.20Hz-

0.33Hz.

The amplitude of PPG signals is known to be

low, which is attributed to a significant depth from

the originating tissue to the surface of the skin,

which photons must travel to register on the detector

(Moço et al., 2018). In particular, (Moço et al.,

2018) simulated photon propagation in a multi-

layered turbid media, configured to represent the

optical properties of six layers of skin in the palm or

finger pad, and found that the depth origin of the

PPG signal was from approximately 1.5-2mm under

the surface of the skin. This low amplitude signal

further contains oscillations in 0.01-0.02Hz, 0.02-

0.06Hz, 0.06-0.15Hz ranges corresponding to

endothelial related metabolic, neurogenic, and

myogenic activities, respectively (Li, 2006).

Finally, a lesser discussed signal which may be

present in the PPG are Mayer waves, which are

oscillations in blood pressure that typically occur at

a frequency of 0.1Hz (Julien, 2006). The mechanism

for Mayer waves is subject to active debate, but

recent findings advocate that the oscillations are

produced by a sympathetic baroreceptor response to

hemodynamic disturbances (Julien, 2006). Further,

Mayer waves have been demonstrated to have

clinical utility in prediction of hypertension. In a

longitudinal study, Mayer waves were extracted

from electrocardiograms (ECG) and their

characteristic frequency quantified. Five years after

ECG acquisition, investigators followed up with

subjects and observed that lower frequency Mayer

waves corresponded to an increased risk of primary

hypertension (Takalo et al., 1999). A related

mechanism for blood pressure regulation is the

myogenic vascular response (MVR), which is a non-

BIOIMAGING 2022 - 9th International Conference on Bioimaging

154

sympathetic vascular contraction in response to a

localized increase in blood pressure (Estañol et al.,

2016). As mentioned previously, MVR presents at a

similar frequency range to Mayer waves (0.06-

0.15Hz), presenting difficulty in distinguishing

between these two mechanisms.

In summary, rPPG can embed a variety of

signals. The aim of this project is to understand the

utility of consumer-grade cameras as a remote PPG

tool for microvascular hemodynamics investigations.

For these purposes, we designed a test where PPG

signals are captured with hands placed on a table

(baseline) and hands elevated (elevation stress). In

the first case (baseline), we expect that the hand will

be in physiological equilibrium. Thus, the normal

physiological autoregulation systems will be at play.

In the second test, we captured video of the hand

raised from the sitting position, immediately after

that raise. In this case, we expect that the system will

be out of equilibrium (elevation stress), and some

transient changes will occur. As a result, we

collected pilot rPPG data under rest and elevation

stress from a group of healthy subjects.

2 METHODS

2.1 Imaging Setup

In the present work, we used an imaging setup

composed of a stand-mounted consumer grade

camera (iPhone 8). Illumination was provided by the

on-board LED. The camera was configured to

acquire 1920x1080 video data at 60 frames per

second (fps), with auto-exposure and auto-focus

locked to disable automated adjustments during

acquisition. In the rest scenario, subjects were

seated, and hand placed on a table. The acquisition

was repeated for each hand. The camera was

positioned about 10cm above the subject’s hand, and

video captured for 90 seconds. In the elevation stress

scenario, the subject was seated with the chair

positioned perpendicular next to a wall. For the right

hand, the right side of the body was next to the wall,

and opposite for the left hand. The subject then

reached the hand as high as possible and placed it on

the wall. Data was then acquired for 90 seconds,

then repeated for the next hand. Some acquisitions

were also performed while subjects were holding

their breath to investigate the effect of respiration.

2.2 Data Processing

A region of interest (ROI) was manually chosen in

each video to exclude any non-anatomical features

in the video (i.e., the wall or table). Pixels outside of

the ROI were removed from the analysis, and every

second pixel (both row-wise and column-wise) were

removed for memory purposes (post-removal still

provided desired spatial resolution). To calculate the

rPPG, the pixel values were averaged across the ROI

for each frame, creating 60Hz time series for each of

the three colour channels (red, green and blue)

spanning the 90 seconds of acquisition. Filtering

with continuous wavelet transform (MATLAB

functions cwt and icwt) was performed from 0.075-

0.125Hz to isolate signal components that

correspond to Mayer waves and MVR, from 0.83-

1.67Hz for cardiac pulsations, and 0.20-0.33Hz for

respiration. As previously discussed, biological

signals such as PPG are typically non-stationary,

meaning that signal statistics such as mean and

standard deviation vary over time. Therefore, to

accurately capture amplitude at a specific frequency

while minimizing time sensitivity (since some

frequency content may only be transient and

therefore not of interest), an envelope methodology

was used. An envelope traces the extremes of a

signal, as defined by specific criteria (Johnson,

Sethares & Klein 2011); in this case, moving

minimum and maximum, for the lower and upper

envelopes, defined by the lower range of the

frequency band of interest. Once the envelopes are

evaluated, then the point-wise difference between

the upper and lower envelopes define the

instantaneous amplitude at any given time point.

Evaluating the median amplitude across the signal

reduces sensitivity to any transient changes in the

amplitude. This method was implemented using the

k-point moving maximum (MATLAB function

movmax) and k-point moving minimum (MATLAB

function movmin). k was configured to be

proportional to the lower bound of the filtering range

with the relationship (1/frequency)*fps. Therefore,

k=800 in the case of the Mayer wave / MVR signal,

k=72 for the cardiac signal and k=300 for

respiration.

2.3 Subjects

As an initial pilot investigation, data was collected

as described from 6 healthy subjects (5 male, with 1

over the age of 30, and 1 female under the age of 30)

under approval from the Ryerson University REB.

Remote PPG Imaging by a Consumer-grade Camera under Rest and Elevation-invoked Physiological Stress Reveals Mayer Waves and

Venous Outﬂow

155

The videos were captured on both hands, with hands

placed on a table (baseline) and hands elevated

(elevation stress) for a total of 24 videos. 2

additional videos were captured to assess impact on

the signal of breath holding.

3 RESULTS

In the elevation stress signals, 3 of the 6 subjects

exhibited clear visually recognizable low-frequency

waves (see Figure 1).

Figure 1: An example of low frequency waves observed in

the raised hand position. Each subplot represents raw data

collected by red (a), green (b), and blue channels (c),

respectively.

In the elevation stress signals, 4 of the 6 subjects

exhibited a signal baseline change, specifically an

increasing amplitude (Figure 2).

Figure 2: An example of a monotonic reflection increase

observed in the raised hand position. Each subplot

represents raw data collected by red (a), green (b), and

blue channels (c), respectively.

2 subjects exhibited both low frequency waves and

monotonic reflection increase in their elevation

stress signals and are included in previous counts

(Figure 3).

Figure 3: An example of a hybrid behavior (a monotonic

reflection increases plus low frequency oscillations)

observed in the raised hand position. Each subplot

represents raw data collected by red (a), green (b), and

blue channels (c), respectively.

3 subjects exhibited baseline signals without any

distinguishing characteristics, as was expected

during physiological equilibrium. However, in the

remaining three subjects, an unknown high-

amplitude signal dominated the rPPG, which could

either be of either physiologic or external origin.

Due to the unknown providence of this signal, these

abnormal signals were excluded from the upcoming

analyses.

One possible explanation for the observed low

frequency waves is respiration. As previously

described, the expected respiration frequency is

0.20Hz-0.33Hz. While the frequency of these waves

appears to be lower frequency than this range, we

wanted to exclude their effect explicitly. For this

purpose, 2 subjects performed 30-second breath

holding in the elevated stress position. The low

frequency waves did not appear to be affected by

breath holding, leading us to exclude respiration in

favour of Mayer/MVR mechanism.

Figure 4 shows the variation in rPPG amplitudes

for respiration, Mayer/MVR and cardiac pulsations,

both at rest (3 subjects, each with 2 signals) and

elevation (6 subjects, each with 2 signals), across the

RGB channels. As would be expected, the green

colour channel generated the highest-amplitude

signals in the majority of cases, since it is most

sensitive to changes in oxygenated haemoglobin

concentration as compared to red and blue channels.

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Figure 4: rPPG amplitudes of respiration, Mayer/MVR

and cardiac pulsations at rest (3 subjects, each with 2

signals) and elevation (6 subjects, each with 2 signals).

The median is represented by the red line, and the 25th

and 75th percentiles by the bottom and top of the box,

respectively. The whiskers extend to the furthest non-

outlying points (75th percentile + 1.5 x interquartile range

and 25th percentile - 1.5 x interquartile range), and the +

symbol represents outliers.

This observation corresponds with previously

published results (Verkruysse, 2008), serving as a

confirmation of the validity of the approach used

herein.

Figure 5 extends this analysis by directly

comparing the signal amplitudes across the rest and

elevation conditions (N=3, excluding the subjects

with abnormal baseline signals). The ranges

corresponding to cardiac, respiration and

Mayer/MVR all increase in amplitude during

elevation as compared to rest, with Mayer/MVR

exhibiting the largest increase.

Figure 5: Comparison of rPPG amplitudes at frequencies

corresponding to respiration, Mayer waves and cardiac

pulsations at rest and elevation across three subjects.

Amplitudes are shown on the left, and ratios comparing

elevation to rest on the right.

4 DISCUSSION

Here, we present an initial pilot investigation of the

microvasculature hemodynamics captured by a

smartphone camera. Data was collected as described

from six healthy subjects.

The collected data demonstrate the plurality of

observed patterns. In particular, in addition to

cardiac and respiratory activities two distinct

patterns; low frequency oscillations (Figure 1) and

monotonic reflection increase (Figure 2) are clearly

noticeable. In some cases, both these patterns are

combined (Figure 3).

We hypothesize that the low frequency

oscillations can be attributed to Mayer waves. While

Mayer waves share the same frequency range as

myogenic activities (0.06-0.15Hz), their origins are

different. Mayer waves are the sympathetic activity

with baroreflex activation. MVR is local and

independent of the sympathetic nervous

vasoconstriction. The elevation scenario performed

here is similar to a simulation performed by

Remote PPG Imaging by a Consumer-grade Camera under Rest and Elevation-invoked Physiological Stress Reveals Mayer Waves and

Venous Outﬂow

157

(Hammer & Saul, 2005), which found that a

reduction in blood volume (such as that which

occurs due to limb elevation) can perturb the

baroreflex (normally stable), and lead to the blood

pressure oscillations known as Mayer waves.

Therefore, we believe that it is more likely that the

observed low frequency waves are Mayer rather than

MVR, and the amplitude increase during elevation

stress represents a sympathetic response to the

reduction in blood volume. The increase in cardiac

amplitude during elevation stress further confirms

the effectiveness of the stressor, since it matches

previously published results (Hickey, 2015).

Speaking of the monotonic reflection increase

(Figure 2) mechanism, we hypothesize that it is

connected with gravitational venous outflow from

the raised hand. A decreasing concentration of

venous blood leads to decreased light absorption and

therefore increased light reflection and captured

signal intensity. It is particularly visible in the red

channel Figure 3). In this wavelength range, the

absorption of deoxyhemoglobin dominates over that

of oxyhemoglobin. Thus, we expect that this signal

is indicative of gravitational venous outflow.

It is typically assumed that the volume of

bloodheld in the venous compartment stays

relatively constant, leaving the major portion of the

PPG signal coming from the arterial side. However,

our experiments confirm the notion that in some

cases venous blood redistribution can be significant

and contribute to the PPG signal. It was also

observed in experiments with the occluded body

parts (see, for example, (Burton et al., 2021)), where

the oxyhemoglobin can be gradually converted into

deoxyhemoglobin; thus, increasing absorption and

decreasing the reflectance in the red range of

spectrum.

It also should be noted that blood redistribution

effects can be significant and may take up to 30

seconds, even in simple experiments. Thus, a

reasonable equilibration time should be properly

incorporated while planning experiments.

It should be noted that the current study involved

a very small number of subjects. Given the multitude

of observed patterns, these results are very

preliminary. Much broader studies are required to

come to meaningful conclusions, which will be the

focus of our future work. Further, including a

conventional contact PPG sensor on the non-

elevated hand will be considered in the future, as a

reference signal. Extension of our methodology to

imaging PPG, where maps visualize characteristics

of local PPGs across an area of tissue

(Kyriacou &

Allen,

2021), may also be beneficial to explore the

observed effects.

5 CONCLUSIONS

Remote PPG is a versatile tool, which can be used in

hemodynamic analysis.

PPGs can not only capture changes in arterial

blood, as previously asserted, but can also capture

changes in venous blood volume. As venous outflow

from tissue occurs, such as due to gravity when the

tissue is elevated, the volume of venous

(deoxygenated) blood is getting lower. Thus, the

absorption of the tissue decreases, resulting in more

reflection and therefore higher intensity registered

by rPPG. This result was clearly observed in several

volunteers (venous outflow pattern).

Such as skin pressure induced by the contact

PPG can affect venous flow distribution, the

noncontact nature of rPPG makes it an ideal tool for

venous compartment investigations.

ACKNOWLEDGEMENTS

The authors thank all the members of the Ryerson

photonics group for their support. The authors

acknowledge funding from NSERC Alliance

(Douplik & Saiko), NSERC Personal Discovery

(Douplik), NSERC RTI (Douplik), and Ryerson

Healthy Fund (Douplik).

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