feasibility of long-distance heart rate monitoring using transmittance photoplethysmographic imaging...

Robert Amelard , Christian Scharfenberger, Farnoud

Kazemzadeh, Kaylen J. Pfisterer, Bill S. Lin, David A. Clausi&

Alexander Wong

Article | OPEN

Feasibility of long-distanceheart rate monitoring usingtransmittancephotoplethysmographicimaging (PPGI)

Scientific Reports 5,

Articlenumber:14637 (2015)

doi:10.1038/srep14637

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Biomedical engineering |

Optics and photonics

Received:

Accepted:

Published online:

27 March 2015

02 September 2015

06 October

2015

Photoplethysmography (PPG) devices arewidely used for monitoring cardiovascularfunction. However, these devices require skincontact, which restricts their use to at-restshort-term monitoring.Photoplethysmographic imaging (PPGI) hasbeen recently proposed as a non-contactmonitoring alternative by measuring bloodpulse signals across a spatial region of interest.

Abstract

Existing systems operate in reflectance mode,many of which are limited to short-distancemonitoring and are prone to temporal changesin ambient illumination. This paper is the firststudy to investigate the feasibility of long-distance non-contact cardiovascularmonitoring at the supermeter level usingtransmittance PPGI. For this purpose, a novelPPGI system was designed at the hardware andsoftware level. Temporally coded illumination(TCI) is proposed for ambient correction, and asignal processing pipeline is proposed for PPGIsignal extraction. Experimental results showthat the processing steps yielded asubstantially more pulsatile PPGI signal thanthe raw acquired signal, resulting instatistically significant increases in correlationto ground-truth PPG in both short-

and long-distance monitoring. The results

support the hypothesis that long-distanceheart rate monitoring is feasible usingtransmittance PPGI, allowing for newpossibilities of monitoring cardiovascularfunction in a non-contact manner.

Photoplethysmography (PPG) is a non-invasivelight-based method that has been used sincethe 1930s for monitoring cardiovascularactivity . These devices have been used to

Introduction

1,2,3

assess cardiovascular factors such as bloodoxygen saturation, heart rate, autonomicfunction, and peripheral vascular disease .Conventional contact PPG devices are fastenedonto peripheral sites including the finger, ear,and toe . Standard devices are comprised ofeither a single or multiple light-emitting diodes(LED) and a photodetector. These devicesmonitor cardiovascular activity by evaluatingthe change in light intensity resulting fromtemporal fluctuations in local blood volume.However, contact PPG devices are limited tosingle patient monitoring, resulting inincreased cost per individual, and limiting thenumber of possible concurrent measurements.Furthermore, these devices produce single-point blood pulse measurements, which do notprovide information about blood perfusionpatterns which may be important for assessingtissue viability . Finally, their contact naturemakes them unsuitable for scenarios in whichskin contact is problematic, such as neonatalmonitoring, burn wound assessment, and sleepstudies, as well as long-term continuousmonitoring due to user comfort and movementartefacts .

To address these issues,photoplethysmographic imaging (PPGI)systems have been proposed for non-contactheart rate monitoring. The goal of PPGIsystems is to extract hemodynamic waveformswithout intrusive tissue contact. Although

2

2

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2

existing designs differ, the primarycomponents are analogous to contact PPGdevices: a light source (LED) and a lightdetector (camera). Such systems rely either onactive or ambient tissueillumination. Both active and ambient PPGIsystems are sensitive to temporal changes inuncontrolled ambient illumination. Somestudies mitigated this effect by using darkroom settings for device validation .Normalising ambient illumination changesusing software has been proposed , howeverthis technique relies on a spectral estimation ofthe ambient illumination, which may fail indifficult lighting conditions.

A potential, yet largely uninvestigated,advantage of PPGI is the ability to performlong-distance monitoring (i.e., monitoring atthe supermeter level). In contrast to existingshort-distance PPGI monitoring, long-distancePPGI monitoring can enable whole-roommonitoring and multi-individual assessment.This can be beneficial in settings such asassessing infants health conditions in aneonatal intensive care unit. Whole-room,multi-individual assessment is difficult orinfeasible with contact PPG devices, since thedevices are either are attached via a cable to amonitor, must store the data on the device, ormust transmit the data wirelessly, resulting inthe need for a specialised networkinfrastructure. Additionally, one contact device

5,6,7,8,9,10 11,12,13,14,15

5,7,8,10

15

can only monitor one individual. This is alsochallenging in PPGI systems. Many existingPPGI systems operate in reflectance mode,where the camera and illumination source arepositioned on the same side of the tissue underinvestigation. These reflectance PPGI systemshave been limited in utility for long-distancemonitoring due to two primary factors. First,the signal that is scattered back to the sensor isrelatively low compared to the incidentillumination. Light-tissue interaction can besummarized by the equation:

where I is the illumination incident on theskin, R and R are specular and diffusereflectance, A is absorption and T istransmittance. Since the illumination sourceand sensor are positioned on the same side ofthe tissue in reflectance systems, the onlycomponent of the sensor irradiance thatcontains blood-related activities is R (some ofwhich may have scattered prior to hemoglobinabsorption), which has experienced scatteringand absorption events due to the highlyscattering nature of skin in visible and nearinfrared wavelengths . Second, divergentillumination sources exhibit an intensitydecrease proportional to the square of thedistance, leading to reduced tissue irradianceat long distances. A second decrease happensfrom the tissue to the sensor, since theillumination source and sensor are positioned

0

s d

d

16,17

on the same side of the tissue in reflectancesystems. These factors render long-distancemonitoring a challenging problem in typicalreflectance-based systems. These constraintsmay be alleviated using a transmittance PPGIsystem, where the LED is positioned behind athin anatomical location (e.g., finger or toe),resulting in a lower number of scatteringevents and high hemoglobin absorption. To thebest of the authors knowledge, this paper isthe first to investigate long-distancetransmittance PPGI monitoring.

This paper presents a pilot study to assess thefeasibility of long-distance cardiovascularmonitoring using transmittance PPGI. For thispurpose, a novel non-contact transmittancePPGI system is proposed which is able tomonitor cardiovascular activity remotely bycorrecting for ambient lighting fluctuationsand extracting the subtle blood pulse signalusing signal processing tools. This systemcomprises a 100fps camera, a high-poweredLED (655nm), a microcontroller to synchroniseframe captures and illumination, and image andsignal processing software on a computer.Figure 1 shows a graphical representation ofthis system, and Figure 2 shows the processingsteps. Temporally coded illumination (TCI) isintroduced to remove ambient lightingartefacts at the acquisition level, thuscorrecting the data prior to further processing,similar to that introduced in our previous

work . The proposed PPGI system used for thisstudy extends substantially beyond ourpreviously reported work by incorporating acomprehensive set of signal processing steps, alarger testing sample size, and more rigorousstatistical and qualitative analysis. The signalprocessing is particularly important for thisstudy, as long-distance cardiovascularmonitoring scenarios result in acquired signalswith low signal-to-noise ratios (SNR).

Figure 1: The proposed non-contact PPGIsystem comprises an LED (655nm), aCMOS camera (100fps), and amicrocontroller (MCU) forsynchronization and TCI implementation.

The MCU timer dictates the TCI code, triggering thecamera to acquire frames, which are transmitted andprocessed on the computer. All drawings werecreated by R.A.

Full size image -/articles/srep14637/figures/1

18

Figure 2: Overview of the processing stepsfor the proposed PPGI system.

Upon acquiring frames, ambient correction usingTCI is performed, which removes temporal changesin ambient illumination from the frames. This isfollowed by denoising to remove camera sensornoise and process noise from the light-tissueinteraction, and smooth motion detrending. Theresulting signal is an extracted stable PPGI signal.


Experimental Setup

For this pilot study, five of the authors data(age were collected using theproposed system. A 3:1 TCI code (alternating 3frames with LED on, followed by 1 frame withLED off) was used with 100fps frame rate and8ms exposure time. The aperture was manuallyadjusted to limit pixel saturation. TheEasyPulse PPG device was used to collect theground-truth PPG signal for validation.Approval for this study was obtained from theinstitutions office of research ethics.

Two experiments were performed to assess the

Results

19

feasibility of long-distance monitoring. InExperiment 1, the camera and LED wereseparated by 20cm, serving as short-distancebase case validation. The participants wereasked to position their fingers between theLED and camera so that their fingers coveredthe beam of the LED. In Experiment 2, thecamera and LED were separated by 1.5m(long-distance), and the participants wereasked to position their fingers at approximately10cm from the LED. Figure 3 shows exampleimages of both experiments before and afterambient correction. For each experiment, a 10swindow was chosen that yielded a cleanground-truth PPG signal for validation. Thenormalised power spectral density (PSD) wascomputed for spectral analysis to demonstrateeach signals dominant frequency components.In an ideal signal, the fundamental heart rateshould be the dominant frequency.Furthermore, to assess temporal signal fidelity,the Pearsons linear correlation coefficient was computed between PPGI and PPG signals.This metric is offset- and scale-invariant,suitable for the problem of comparing unit-lessPPG signals:

Figure 3: Example images for Experiment 1(short-distance monitoring) andExperiment 2 (long-distance monitoring).

The unprocessed frames (first column) containeduncontrolled ambient illumination (windows,overhead lights, etc.) as well as controlled active LEDillumination near the fingers. Ambient correctionusing TCI (second column) removed the contributionof ambient illumination to the scene, yieldingtransmittance due solely to active LED illuminationof which the spectral and power characteristics areknown. See Video 1 and Video 2 for video results ofambient correction with varying illumination.


where X,Y are the finger PPG and PPGI signal, is the covariance between the two signals,and , are the standard deviations of thesame variables. This generates a value , where 1 is perfect linear correlation, 1 is aperfect negative linear correlation, and 0 is

XY

X Y

uncorrelated. A perfectly reconstructed PPGIsignal would yield =1, indicating that the PPGIsignal is a linear scaling of the finger PPGsignal.

The PPGI signals were statistically analysedusing a novel physiologically-motivated t-testto quantify the degree to which the proposedsystem processing improved the signal. Foreach participant, each heart beat instance (i.e.,diastole-to-diastole) was semi-automaticallylabeled using a custom gradient descentapproach. was computed for each such heartbeat instance, yielding n correlation values. Inorder to assess an improvement in correlationafter processing, a two-tailed paired-sample t-test (=0.05) was conducted using thecorrelation values for each heart beat instance,yielding a p-value indicating the probability ofa zero-mean difference between signalcorrelation.

Distance Parameters

It is important to study the effects of distanceon the optical imaging parameters, primarilyresolution and illumination intensity (i.e.,irradiance). By calculating the area imaged by asingle pixel, the spatial resolution of theimaging area can be determined. This can becomputed using the optical magnificationequation:

where h , h are the object and image heights,respectively, and d , d are the distances fromthe lens to the object and imaging sensor,respectively. According to the experimentalsetup, for short- and long-distance monitoring respectively, d =6mm(focal length of the lens), and h =4.8m pixelpitch. Then, the field of view of a single pixel is

for short- and long-distancemonitoring respectively. Thus, to measure a 40mm40mm area around the fingers, theresolution is 250250 pixels for short-distanceand 3333 pixels for long-distance monitoring,yielding acceptable imaging resolution forspatial analysis.

Due to the inverse-square law ofelectromagnetic radiation, long-distancemonitoring will exhibit greatly reducedirradiance relative to the source. Given twoobjects at distances d and d (d >d ) from anillumination source, noting the inverse-squarelaw of intensity , the change inillumination intensity can be computed asfollows:

That is, the irradiance is reduced by a factor of56 between long- and short-distancemonitoring in the experimental setup.

Experiment 1: Short-Distance

o i

o i

i

i

1 2 2 1

The goal of Experiment 1 was to validate thebase case short-distance setup. Figure 4 showsthe effect of processing the PPGI signal forthree participants. The processed PPGI signalcontained amplified pulsatility over theunprocessed (raw) PPGI. Pulsatile signalswere extracted for all five participants (P1through P5), resulting in discernible systolicpeaks of each blood pulse. Much of the noisewas subdued upon processing. The high-frequency information not expected in thenaturally smooth blood pulse waveforms wasdenoised, yielding smooth blood pulses.Furthermore, non-linear trends werecorrected, yielding stable PPGI signals. Thesenon-linear trends resulted in low correlationvalues between the raw PPGI and the ground-truth PPG (e.g., at 1.5s for P3). The processedPPGI stabilised the signal, yielding largercorrelation values at those time windows. Theheart rate was discernible in both raw andprocessed PPGI as a peak frequency powercoincident with the PPG peak. However,although all PPGI signals showed strongspectral similarity to the PPG signal in the PSD,the processed PPGI signal exhibited a strongerpeak at the heart rate, thus subduingextraneous frequencies.

Figure 4: Summary of the results fromExperiment 1 (short-distancemeasurement) across three participants(P1, P2, P3).

The unprocessed acquired signal (row 2) wasprocessed using ambient correction and signalprocessing, yielding a PPGI signal (row 3), whichexhibited a higher correlation to the ground-truthcontact PPG signal (row 1). For each participant, thePPGI power spectral density (row 4) closely matchedthe ground-truth PPG power spectral density, andthe heart rate was easily distinguished by themaximum frequency power peak. The windowedcorrelation (row 5) of the processed PPGI signal withrespect to the PPG signal (red) showed unanimousimprovement over the unprocessed signal (blue),exemplifying the local temporal similarity across theentire signal.


Table 1 summarises the statistical results forthis experiment. The correlation values acrosswhole-signal comparisons were substantially

increased for each participant fromunprocessed (=0.250.21) to processed (=0.840.08) due to recovered stability andpulsatility. This indicates a large correctionacross the entire signal, demonstrating anoverall improvement in signal fidelity.Statistical analysis using the heart beatinstance correlation values showed astatistically significant increase in correlationfor each participant . Thus,the resulting PPGI signal contained strongpulsatile components, enabling temporal inter-beat analysis.

Table 1: Experiment 1statistical results forshort-distancemeasurements.

Full size table

Experiment 2: Long-Distance

The goal of Experiment 2 was to validate long-distance PPGI measurement for long-distancemonitoring. Figure 5 shows the effect ofprocessing the PPGI signal for threeparticipants. The raw PPGI signals were noisy,and pulsatility was not easily discernible in thesignals of P1 and P3. This was emphasised bythe lack of a distinct peak in the PSD. Theprocessed PPGI signals yielded a higher degreeof pulsatility, although the signals were noisier

than the short-distance measurements(discussed later). This pulsatility was reflectedin the PSDs, where there was a distinct peakcoincident with the heart rate for eachparticipant. However, in cases such as P2 andP3, distinct blood pulses were not easilyobserved, making beat-to-beat timing analysisdifficult at long-distances. The correlationplots showed a general increase across eachparticipant with processing, demonstrating theincreased pulsatility. However, there weresome areas that were not improved (e.g., P3 at67 s), largely resulting from unsuppressednoise likely due to amplified movementartefacts (discussed later). This high-frequencynoise did not have a major effect on the PSDwithin the physiologically valid heart raterange.

Figure 5: Summary of the results fromExperiment 2 (long-distancemeasurement) across three participants(P1, P2, P3).

The unprocessed acquired signal (row 2) wasprocessed using ambient correction and signalprocessing, yielding a PPGI signal (row 3), whichexhibited a higher correlation to the ground-truthcontact PPG signal (row 1). The PPGI system was ableto extract some pulsatile information from weaklypulsing (P1) and noisy (P2, P3) signals. As a result, theheart rate was determined through the maximumfrequency power peak (row 4). The windowedcorrelation (row 5) of the processed PPGI signal withrespect to the PPG signal (red) showed an overallimprovement over the unprocessed signal (blue),exemplifying the local temporal similarity across theentire signal.


Table 2 summarises the statistical results forthis experiment. As in Experiment 1, thecorrelation values for whole-signal results

increased from unprocessed (=0.260.14) toprocessed (=0.580.17), indicating recoveredpulsatility and stability for an overall increasein signal fidelity. Statistical analysis using theheart beat instance correlation values showeda statistically significant increase in correlationfor each participant except P3

. However, P3 exhibitedsubstantial improvement upon processing (p=0.053). This emphasises the increased beat-to-beat recovery of pulsatile components. Overall,correlation values were lower than for short-distance monitoring, but heart rate monitoringwas achieved.

Table 2: Experiment 2statistical results forlong-distancemeasurements.

Full size table

Strong results were obtained for short-distance measurement results, producingvisually pulsatile signals and accuratelyextracting heart rate. Statistical significancewas achieved , exemplifyingthe recovery of hemodynamic waveforms froma transmittance PPGI signal. The processed

Discussion

PPGI signal yielded smooth pulsatilecomponents, making temporal beat-to-beatanalysis feasible. For example, heart ratevariability can be used as a measurement forautonomic nervous system activity . Thevariability in correlation values acrossparticipants may be due to factors such asmovement artefacts, and differences inscattering and absorptive media across tissues,such as melanin (skin tone) and adiposetissue .

Long-distance monitoring yielded lowercorrelation values to ground-truth PPG thanthose for short-distance monitoring. Due tothe diffuse nature of light traveling throughhighly scattering tissue , the sensor irradianceis reduced, yielding weaker signals at greaterdistances. Furthermore, the area imaged by asingle pixel increases with larger distances,thus small movements affected a larger numberof pixels than in short-distance monitoring.This is apparent in the results for participantP3 (see Figure 5). The participants fingers wereshaky, yielding high-frequency noise which thesystem was unable to fully remove. Automaticobject tracking may alleviate this source ofnoise, During long-distance monitoring,statistical significance was achieved for heartbeat instance correlation values in all but oneparticipant (P3, p=0.053), indicating therecovery of a PPGI signal.

20,21

22

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Although temporal pulsatility was extracted insome but not all cases, the fundamental heartrate was identifiable across all participants.This indicates that beat-to-beat timing analysisbecomes difficult, but heart rate is identifiableduring long-distance PPGI monitoring. Theseresults validate the feasibility of long-distanceheart rate monitoring, but further work mustbe done to demonstrate beat-to-beat temporalanalysis in long-distance monitoring. Such animaging system could benefit settings in whichthe individual exhibits low motion, such ascardiovascular monitoring during neonatalmonitoring or sleep studies. In both settings,minimizing the amount of tissue contact ispreferable. Such an imaging system couldprovide physiologically relevant information ina non-contact manner. Additionally, theimaging system could monitor multipleindividuals within the cameras field of view byanalysing each individuals signalindependently, thus decreasing the per-individual cost of the system.

The current system is best suited for situationsin which there is minimal movement, such as inneonatal monitoring and sleep studies. In thesecases, less expensive and simpler electrical andphotoplethysmographic techniques may beproblematic, as they require contact with theindividual. In the case of neonatal monitoring,the infants skin may be too fragile for contactprobes, and their size may not be suited for

standard equipment. In the case of sleepstudies, unnatural contact monitoring mayhamper sleep quality, which can affect the data.In both of these cases, non-contact long-distance monitoring can provide essentialphysiological monitoring withoutinconveniencing the user.

The current PPGI system has three primarylimitations. First, very bright ambientillumination may cause pixel saturation whenusing a fixed aperture setting. This can bemitigated with the use of optical filters, andautomatic camera adjustment, such asexposure time and aperture. Second, physicalobstructions may inhibit measurements. Long-distance transmittance PPGI requires anunobstructed view between the illuminatedtissue and the camera. In many situations, thiscan be guaranteed by affixing the camera tothe ceiling. However, there are settings inwhich completely unobstructed views areinfeasible, especially in natural settings. Inthese cases, more advanced setups may beexplored, such as the use of mirrors. Third,large movements may impact results.Automatic object tracking can be performed totrack an area on an object in space over time,yielding spatially-invariant monitoring. Theseproblems are grounds for future work.

Methods

To investigate the feasibility of long-distancePPGI monitoring, a non-contact PPGI system isproposed whose design integrates hardware(active illumination, a camera, andsynchronisation electronics) and software(image and signal processing). Figure 1 depictsa graphical overview of the proposed PPGIsystem. A participants fingers were placedbetween a high-power LED (Philips LUXEONRebel, 655nm, 580mW) and a camera(PointGrey Flea3, 100fps) with a 6mm lens. Thecamera frames and LED illumination patternswere synchronised by a microcontroller (MCU)and custom electronics. Temporally codedillumination (TCI) is proposed for controllingthis illumination sequence. Figure 2 shows thestages of processing to yield the final PPGIsignal. The following subsections providedetailed descriptions of the processing steps.

Light Transport Model

A temporal extension of the Beer-Lambert law(BLL) was used. The BLL shows that theattenuation of light follows an exponentiallydecay as it passes through a homogeneouslight-absorbing medium:

where T is transmittance, I is incident light, Iis transmitted light, is the tabulated molarextinction coefficient for the light-absorbingmedium (e.g., oxyhemoglobin), l is the photon

0

path length, and c is the concentration of themedium. The temporal transmittance can bewritten as a function of time t:

The path length for oxyhemoglobin is thedominant temporally changing parameter inheart beat analysis . The standardmeasurement signal for PPG analysis isabsorbance, which is related to transmittanceas A=log(T). Then, expressing this astemporal absorbance yields:

This model assumes constant ambientconditions. However, I(t) may be affected bytemporal changes in ambient illumination,potentially corrupting the subtle blood pulsesignal. Thus, in the next section, an ambientcorrection method is proposed.

Temporally Coded Illumination (TCI)

A TCI process is proposed to remove the effectof ambient light changes in the data, thusensuring that the reflectance signal is duesolely to the controlled active illumination (e.g.,LED). A dual-mode temporal code was definedby which frames contain irradiance due toeither ambient illumination or a combination ofambient and active illumination. By coding theillumination synchronously with framecaptures, ambient measurements can be

3

performed according to the temporal code, andsubtracted from the actively illuminatedframes. For example, a temporal code of 3:1defines a TCI sequence that measures oneambient frame after every three activelyilluminated frames. Figure 1 shows a graphicalexample of 3:1 TCI.

Mathematically, given an intensity image I(t) attime t, according to the TCI sequence, I(t) caneither be:

where I (t) and I (t) are the irradiance dueto ambient illumination and active illumination,respectively. Then, given a recently acquiredambient frame I (tt), by modeling lightreflectance as a linear combination of ambientand active lighting, the ambient correction canbe applied to the current frame:

With a fast frame rate, t becomes negligible,yielding illumination due solely to controlledactive illumination:

Since the camera operates at 100fps, tapproximately satisfies this condition (t=10ms). Substituting equation (9) into equation (7)yields the ambient-corrected absorbance

amb act

amb

measurement:

Once the ambient frame I (t) was acquired,the previously acquired active frame I(tt)was used in its place to ensure constant framerate for frequency analysis.

TCI was implemented at the hardware level,and requires control of an active light source.Thus an active illumination system was chosen.Camera and illumination synchronisation wasperformed using custom circuitry andmicrocontroller code. Figure 3 shows theresults of ambient correction via TCI on asingle frame. Video 1 and Video 2 demonstrateambient correction across a series of frameswith varying incident light.

Signal Processing

Capturing data using the given light transportmodel and TCI yields an ambient-correctedPPGI signal. However, this signal may still beaffected by various sources of noise. Thissection proposes a signal processing pipelinefor extracting a stable pulsatile PPGI signal.First, a denoising step is proposed, whichreduces the noise based on camera andprocess characteristics. Second, a detrendingstep is proposed, which removes trends inintensity due to external factors such asmovement. This processing was performedusing the average pixel intensity across a

amb

manually selected region of interest across theimaged fingers. See Figure 2 for a graphicaldepiction of this process.

Denoising

The goal of signal denoising is to recover thesmooth blood pulse waveform from a noisysignal. Kalman filtering was used since itsmodel is consistent with PPGI denoising. First,its noise model assumes a combination ofmeasurement noise and process noise. In thissetup, measurement noise models camera readnoise, and process noise models the stochasticprocess of light-tissue interaction. Second,Kalman filtering is a recursive algorithm thatcan operate in real-time, as denoising at time tdepends only on the state and uncertaintymatrix at time t1. Third, Kalman filteringmodels the system state based on Newtonslaws of physics, which describe smooth motion.This is well-suited for denoising PPGI signals,as blood pulse waveforms are inherentlysmooth in nature, with varying temporalacceleration.

Kalman filtering requires as inputs the systemstate and noise statistics. The system state wasdefined using Newtons laws of motion usingthe raw camera intensity values:

24

where, at time point k, I is signal amplitude, is the rate of change of the amplitude, and isacceleration of amplitude change. Themeasurement noise was assessed by imaging adark field and quantifying the standarddeviation of pixel intensities across thesensor. During the experiments, =1.02 for 8bit pixel values and 8ms integration time.Denoising was performed on the raw camerasignal I(t) rather than the computedabsorbance signal A(t) since was learnedusing the untransformed raw camera intensityvalues.

Process noise is more difficult to model due tothe complex and stochastic interaction of lightwith tissue. Thus, a grid search optimisationwas performed to assess the optimal processnoise value using 8ms exposure time. Usinga fixed measurement noise model and atraining data set, a logarithmic grid search wasperformed over various values for . Theparameter value that yielded the highestcorrelation to the ground-truth PPG signal waschosen, resulting in =1.8410 for 8bitpixels. This denoising process was thenperformed on the acquired signal using thestandard recursive predict-update Kalmanoptimisation . For each time point, thedenoised intensity signal was extracted usingan observation matrix:

k

m

m

m

p

p

p2

24

where H=[10] , and is the estimated truesystem state at time point k. Plugging this intoequation (7) yields the denoised absorbancesignal:

Detrending

The denoising process assumes constantincident illumination, which is an invalidassumption during movement. The goal of thisdetrending step is to remove oscillations in thesignal, yielding a PPGI signal with constantmean. It was assumed that movements arerelatively smooth over time. A detrendingalgorithm was used on the denoisedabsorbance signal A(t) from equation (14), inwhich the model assumes a smoothnessprior . In particular, the observed signal A(t) ismodeled as the linear combination of the trueabsorbance signal A (t) and a temporal trendA (t).

Given that A(t) is measured, A (t) can besolved by estimating A (t) assuming a linearmodel, subtracting it from A(t), and solving thisusing regularised least squares, which providesthe following estimate of the true detrendedsignal:

T

25

true

trend

true

trend

where I is the identity matrix, is a relativeweighting term, and D is the discreteapproximation matrix of the second derivative.Figure 6 shows the recovery of a stable signalfrom a signal corrupted by movement usingthis detrending method. is the finalprocessed PPGI signal.

Figure 6: Example of the detrendingprocedure.

A nonlinear trend was estimated in the originalsignal and subsequently removed, resulting in asignal with a stable mean. This process removedvariability in illumination due to movement.


How to cite this article: Amelard, R. et al.Feasibility of long-distance heart ratemonitoring using transmittancephotoplethysmographic imaging (PPGI). Sci.Rep. 5, 14637; doi: 10.1038/srep14637 (2015).

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Additional Information

References

Hertzman, A. B. Photoelectricplethysmography of the fingers and toesin man. Experimental Biology andMedicine 37, 529534 (1937).

1.

Allen, J. Photoplethysmography and itsapplication in clinical physiologicalmeasurement. Physiological Measurement28, R1R39 (2007).

2.

Shelley, K. H. Photoplethysmography:Beyond the calculation of arterial oxygensaturation and heart rate. Anesthesia &Analgesia 105, S31S36 (2007).

3.

Levy, B. I. et al. Impaired tissue perfusiona pathology common to hypertension,obesity, and diabetes mellitus.Circulation 118, 968976 (2008).

4.

Wieringa, F., Mastik, F. & Van der Steen,A. Contactless multiple wavelengthphotoplethysmographic imaging: a firststep toward SpO camera technology.Annals of Biomedical Engineering 33,10341041 (2005).

5.

2

Humphreys, K., Ward, T. & Markham, C.Noncontact simultaneous dualwavelength photoplethysmography: afurther step toward noncontact pulseoximetry. Review of ScientificInstruments 78, 044304 (2007).

6.

Zheng, J. et al. Remote simultaneous dualwavelength imagingphotoplethysmography: a further steptowards 3-d mapping of skin bloodmicrocirculation. In Proc. SPIE, vol. 6850,68500S (2008).

7.

Cennini, G., Arguel, J., Akit, K. & vanLeest, A. Heart rate monitoring viaremote photoplethysmography withmotion artifacts reduction. OpticsExpress 18, 48674875 (2010).

8.

Kamshilin, A. A., Miridonov, S., Teplov, V.,Saarenheimo, R. & Nippolainen, E.Photoplethysmographic imaging of highspatial resolution. Biomedical OpticsExpress 2, 9961006 (2011).

9.

Sun, Y. et al. Motion-compensatednoncontact imagingphotoplethysmography to monitorcardiorespiratory status during exercise.Journal of Biomedical Optics 16, 077010-1077010-9 (2011).

10.

Verkruysse, W., Svaasand, L. O. & Nelson,J. S. Remote plethysmographic imagingusing ambient light. Optics Express 16,2143421445 (2008).

11.

Poh, M.-Z., McDuff, D. J. & Picard, R. W.Non-contact, automated cardiac pulsemeasurements using video imaging andblind source separation. Optics Express

12.

18, 1076210774 (2010).

Sun, Y. et al. Use of ambient light inremote photoplethysmographic systems:comparison between a high-performancecamera and a low-cost webcam. Journalof Biomedical Optics 17, 037005-1037005-10 (2012).

13.

Kong, L. et al. Non-contact detection ofoxygen saturation based on visible lightimaging device using ambient light.Optics Express 21, 1746417471 (2013).

14.

Xu, S., Sun, L. & Rohde, G. K. Robustefficient estimation of heart rate pulsefrom video. Biomedical Optics Express 5,11241135 (2014).

15.

Anderson, R. & Parrish, J. A. The optics ofhuman skin. Journal of InvestigativeDermatology 77, 1319 (1981).

16.

Gibson, A., Hebden, J. & Arridge, S. R.Recent advances in diffuse opticalimaging. Physics in Medicine and Biology50, R1R43 (2005).

17.

Amelard, R., Scharfenberger, C., Wong, A.& Clausi, D. A. Illumination-compensatednon-contact imagingphotoplethysmography via dual-modetemporally-coded illumination. In Proc.SPIE vol. 9316, 931607-1931607-5 (2015).

18.

R-B. Easy Pulse sensor (version 1.1)overview (part 1). http://embedded-lab.com/blog/?p=7336 (2013). (12 Dec2014).

19.

Berntson, G. G. et al. Heart ratevariability: origins, methods, andinterpretive caveats. Psychophysiology 34,623648 (1997).

20.

Stergiou, S. & Balakrishnan, R. Streamingupdates for heart rate variabilityalgorithms. IEEE Transactions onBiomedical Engineering 49, 19311937(2014).

21.

Tuchin, V. Tissue Optics: Light ScatteringMethods and Instruments for MedicalDiagnosis (SPIE Press: Bellingham, ,2007), 2 edn.

22.

Wang, L. V. & Wu, H.-I. BiomedicalOptics: Principles and Imaging (JohnWiley & Sons, 2012).

23.

Kalman, R. E. A new approach to linearfiltering and prediction problems.Journal of Fluids Engineering 82, 3545(1960).

24.

Tarvainen, M. P., Ranta-aho, P. O. &Karjalainen, P. A. An advanced detrendingmethod with application to HRV analysis.IEEE Transactions on BiomedicalEngineering 49, 172175 (2002).

25.

The study was funded by the Natural Sciencesand Engineering Research Council (NSERC) ofCanada and, in part, by the Canada ResearchChairs program.

Affiliations

University of Waterloo, Department ofSystems Design Engineering, Waterloo,N2L3G1, CanadaRobert Amelard, Christian Scharfenberger,Farnoud Kazemzadeh, David A. Clausi&Alexander Wong

University of Waterloo, Department ofKinesiology, Waterloo, N2L3G1, CanadaKaylen J. Pfisterer

University of Waterloo, Department ofMechanical and MechatronicsEngineering, Waterloo, N2L3G1, CanadaBill S. Lin

Contributions

R.A., C.S., F.K. and A.W. designed the system.R.A. built the system. R.A., C.S. and A.W.designed the algorithms. R.A. developed the

Acknowledgements

Author information

data acquisition protocol. All authors wereinvolved in data collection. R.A. and K.P.designed the data analysis protocol. R.A. andB.L. conducted data analysis. All authorsreviewed the manuscript.

Competing interests

The authors declare no competing financialinterests.

Corresponding author

Correspondence to Robert Amelard.

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