POSTER 2017, PRAGUE MAY 23 1

Design of a Multipurpose Photoplethysmography Sensorto Assist Cardiovascular and Respiratory Diagnosis

Durmus Umutcan UGUZ1

1Philips Chair of Medical Information Technology, RWTH Aachen University, 52074 Aachen, Germany

umutcan.uguz@rwth-aachen.de

Abstract. Photoplethysmography (PPG) is a widely used,low-cost optical method for the assessment of peripheralcardiovascular health. By using a photodetector and a lightsource, blood volume changes in the tissue can be acquired.Different properties of pulse wave can be further analyzedfor the estimation of several vital parameters. The vastapplication area of PPG also demands different aspects ofmeasurement techniques to be utilized such as the use of mo-tion sensors, different wavelengths of light sources and skintemperature monitoring. With the ongoing improvements insemiconductor devices and increasing demand for long-timemonitoring, a need for a multipurpose PPG sensor address-ing different requirements of different applications exist. Inthis sensor system, a motion sensor and a temperature sen-sor should be ready to assist PPG measurements, whereasPPG measurements can be conducted with different oper-ation parameters depending on the application. Our ap-proach to answer this challenge is the development of amultipurpose sensor system, SmartPPG which can answerthe requirements of different applications. This paper rep-resents so far achieved hardware design, its produced firstprototype and evaluation of first measurements by challeng-ing existing applications of photoplethysmography in clini-cal medicine. Tests of prototype were conducted via micro-controller MSP432P401R with a user interface designed onMATLAB.

KeywordsPPG, Photoplethysmography, cardiovascular diagnos-tic, pulse oximetry, respiratory rate

1. IntroductionCardiovascular disease (CVD) is among the main cause

of death all over the world, accounting 40% of all deaths atages younger than 75 in Europe [1]. Improvements in themodern medicine have been increasing the life expectancy ofpeople in the last decades. Including the aging society, enor-mous changes in lifestyle and habits of people have also in-creased the cardiovascular related diseases as well as the dis-eases which have cardiovascular complications in the long-

term, such as diabetes mellitus. 75% of deaths in diabeticsare due to coronary artery disease [2].

These chronic diseases have huge negative effects onthe quality of healthcare systems, which gives importance topreventive medicine through early diagnoses and long-timemonitoring. Especially at this point, photoplethysmographyoffers cost-efficient, noninvasive solutions that can be ap-plied to everyday life as the sensors itself do not cause aserious discomfort to the patients. Large scale usage of PPGand its everyday life applications make it susceptible to mo-tion artifacts as the measurements are not taken in a steadyand standardized position. Therefore, the performance of aPPG measurement can and should be further improved withthe help of a motion sensor to overcome these motion arti-facts.

Photoplethysmography has also several applications inclinical diagnosis to assess the cardiovascular health. A ex-ample of this is the venous muscle pump test in which ve-nous refilling time is measured to diagnose chronic venousinsufficiency [3]. The examples for photoplethysmography-based clinical applications can be extended to the arterialoxygen saturation measurement, assessment of arterial com-pliance [4], heart rate variability and respiratory rate [5].

2. Aspects of SmartPPG2.1. Photoplethysmography

Photoplethysmography is a monitoring method to mea-sure venous or arterial blood volume changes by irradiatingthe skin. There are two main modes of photoplethysmogra-phy: transmission or reflection modes. The difference be-tween these modes is the position of photodetector and andthe light source with respect to each other. Transmissionmode can be chosen for thin body parts such as finger tipor ear lobe. On the other hand, reflection PPG offers widerrange of measuring sites on the body.

PPG makes use of the fact that hemoglobin has muchhigher extinction coefficient than the tissues. Small varia-tions in the blood volume, in other words the path for lightbetween photodetector and the light source, causes an AC-signal which overlaps with a DC-signal corresponding to

2 D. U. UGUZ, DESIGN OF A MULTIPURPOSE PPG SENSOR TO ASSIST CARDIOVASCULAR DIAGNOSIS

other sources of light absorption such as surrounding tissue,non-pulsatile arterial and venous blood. In addition, thereare slower oscillations which can be observed in PPG sig-nals, such as Traube-Hering-Mayer waves and respiratory-induced oscillations [6].

PPG signals are also affected by the wavelength of thelight penetrated into the skin since especially the hemoglobinhas a wavelength-dependent extinction coefficient charac-teristic. This turns the choice of wavelength into an opti-mization parameter. For example, mostly near-infrared lightsources are used for single-wavelength PPG, whereas, greenreflective PPG has been shown to be more reliable for mea-surements in which temperature changes take place in the en-vironment [7]. Therefore, different wavelengths can be cho-sen depending on the task. This phenomenon, wavelength-depending extinction coefficient curve of hemoglobin, issuccessfully utilized for the purposes of oxygen saturationmeasurements in pulse oximetry where at least two differentwavelengths of light source are used.

In the reflection mode PPG, another important aspect isthe distance between the light source and the photodetector,which has an effect on the tissue penetration depth. Differenttissue penetration depths, by selecting different distances be-tween the light source and the photodetector, can be chosendepending on both which type of vessels to be measured andwhere to be measured. This is also a challenging point fora sensor system aiming to serve multipurpose photoplethys-mography.

2.2. Motion Artifacts

Motion artifacts are among the main source of noisesin a PPG signal. Moreover, these cannot be filtered outwith classical frequency selective filters as they can lay atthe same frequency range with heart rate [8]. One solutionto overcome this problem could be independent componentanalysis [9] in which measurements are taken in two wave-lengths. Another approach to this problem is to use motiontracking sensors on the body parts for model-based noisecanceling techniques [10], which requires another sensor inthe hardware.

Proposed methods to overcome this problem show thatit is an advantageous tool to have more than one wavelengthPPG and a motion sensor, which improves the performanceof motion artifact reduction prominently. SmartPPG em-ploys both more than one wavelength light sources and amotion sensor to overcome this problem and gives freedomof choice in the noise cancellation method.

2.3. Skin Temperature

Skin temperature measurements is a prominent param-eter as it can be measured noninvasive and continuously.

Since it is regulated by the autonomous nervous system, itcan also be taken into account along with photoplethysmog-raphy because both of them measure parameters affected bysympathetic and parasympathetic nervous system. Due toits duties in heat transfer and temperature regulation of thebody, peripheral vascular system is linked with skin temper-ature. Therefore, it can be used as a diagnostics tool in someapplications such as evaluating acute stress [11].

3. Design on PCBProposed design of SmartPPG lays on a two-sided cir-

cular PCB with a diameter of 21 mm. On the front side ofthe board, light sources and photodetectors are located, aswell as the sensing unit of thermometer. Analog front end,accelerometer, EEPROM and other circuit units such as ca-pacitors, pull-up resistors and I2C-Bus are located on theback side of the board. The final design without housing canbe seen in the Fig.1.

As can be seen in the Fig. 1, there are two indepen-dent LEDs with different wavelengths at the bottom. Thereare two identical photodetector units, one at the top and oneat the center. These photodiodes at different positions offerdifferent tissue penetration depths. On the left and right sideof the photodiodes, there are two in series connected LEDswhich can illuminate larger skin regions to enhance the sig-nal quality.

Fig. 1. Front (on the left) and back (on the right) sides ofSmartPPG before housing is mounted. Diameter of PCBis 21mm and it reaches total thickness of 4.8mm after thehousing is mounted.

3.1. Analog Front End

ADPD105 from Analog Devices is a highly efficientphotometric front end with a 14-bit analog-to-digital con-verter. It offers operation of 2 optical sensing units and3 LEDs with flexible sampling rate, LED current, low-poweroperation, ambient light rejection and I2C-Interface [12].Depending on the configuration, two wavelength measure-ments and two photodetector units can be controlled andread from 16-bit or 32-bit registers over I2C-Bus, up to asampling rate of 3 kHz.

POSTER 2017, PRAGUE MAY 23 3

3.2. Motion Sensor

LIS2HH12TR is an ultra low-power three-axis linearaccelerometer from STMicroelectronics. It offers variablefull scale options and a sampling rate up to 800 Hz. Its 16-bit measurements can be read over I2C-Bus [13].

3.3. EEPROM

M24C16 is a 16-Kbit EEPROM which can be read andwritten over I2C-Bus [14]. The main purpose of this compo-nent is to keep the parameters related to the operation on theboard stored, if needed. Moreover, some information regard-ing to the other components can be also be saved in order tobe used during a possible calibration or regular maintenance.

3.4. Temperature Sensor

MLX90615 is an I2C-compatible, high accuracyinfrared thermometer with a measurement resolution of0.02 K [15]. If needed, it can also be driven in continuousmeasurement mode through PWM-output.

4. Measurement and ValidationIn this section, it is aimed to validate that SmartPPG

is an adequate tool in the assessment of following parame-ters. As the validity or applicability of parameters are outof scope, the measurements were mostly taken from a smallgroup of participants. Each of this parameters can be ob-tained after series of signal processing operations where dif-ferent options and methods can be compared and used. Thispaper does not test the validity of proposed methods butproves that SmartPPG offers adequate quality of measure-ment options to be used later on for different purposes. Inthe setup of this study, the developed sensor was tested on3 participants, all of them are male with ages between 24and 27. Participants were not diagnosed with any cardiovas-cular disease.

4.1. Respiratory Assessments

There is a strong coupling, called as CardiorespiratoryCoupling, between respiratory and cardiovascular system.For example, it can be observed most prominently throughCardiorespiratory Sinus Arrhythmia [16] which explains thevariations in the sinus rhythm of heart with respiration. Thiscoupling mechanism can be observed on PPG measurementsand can be used to measure Respiratory Rate (RR) from PPGmeasurements. The effects of this coupling on PPG wave-form can be investigated in three main ways: variation of

heart rate as an autonomic response to respiration calledrespiratory-induced frequency variation (RIFV), variation inthe perfusion baseline due to the intrathoracic pressure vari-ation called respiratory-induced intensity variation (RIIV)and variation in the peripheral pulse strength due to chang-ing ventricular filling called respiratory-induced amplitudevariation (RIAV) [17]. RIIV, RIAV and RIFV, which can beseen in the Fig. 3, can be used and integrated along the RRestimation algorithms. Our suggested algorithm can be builtas following:

RR =

Average(RIFV,RIAV,RIIV ) if σ <= 1

Median(RIFV,RIAV,RIIV ) if 1 < σ <= 4

RIFV σ > 4

This estimation model depends on the standard devia-tion (σ) of three measured RR estimation processes: RIFS,RIAS, RIIS. A small σ indicates a good estimation in eachparameter and the arithmetical mean can be takes as the re-sult. A mid-level σ suggests that one of the parameters hasprobabably estimated wrong. In this case, the median op-eration would leave out the wrong estimation. A higher σvalue means the estimation quality is so low that no relation-ship between three parameters can be deduced. In this case,RIFS is taken as the final value since the CardiorespiratoryCoupling is a much stable phenomenon even in the caseswhere RIIS and RIAS are dominated by other oscillationslike Traube-Hering-Mayer waves.

To confirm the validity of these respiratory-induced pa-rameters, an initial laboratory experiment was performedwith a single participant (male, healthy, 24-year-old). Theparticipant regulated its respiratory rate with the help of ametronome and a fusion algorithm was tested with differentrespiratory rates: 6, 8, 10, 12, 15 bpm. The results of thisexperiment can be seen in the Fig. 2.

100 200 300 400 500 600 700

Time [s]

6

8

10

12

14

16

18

Respirato

ry R

ate

[bpm

]

Fig. 2. RR estimations using suggested algorithm. During6 minutes long measurement session, the algorithm wastested with 5 different respiratory rates.

4 D. U. UGUZ, DESIGN OF A MULTIPURPOSE PPG SENSOR TO ASSIST CARDIOVASCULAR DIAGNOSIS

2800 3000 3200 3400 3600 3800 4000

samples at 100Hz

-600

-400

-200

0

200

400

600

LS

B [

.]

RIAV, RIIV, RIFV on a PPG waveform

PPG

Pulse Strength

RIAV

RIIV

RIFV

Fig. 3. PPG waveform after low-pass filter and removing baseline. Respiratory-induced frequency variation (RIFV), respiratory-induced intensity variation (RIIV) and respiratory-induced amplitude variation (RIAV) can be seen.

4.2. Arterial Assessments

Heart Rate and Heart Rate Variability

By means of a PPG signal, pulse rate (PR) and pulserate variability (PRV) can be calculated, the former of whichis a good estimator of the heart rate (HR), whereas the lattermay sometimes differ from the heart rate variability (HRV)mostly due to pre-ejection period and respiratory-inducedpulse transit time variability [18]. An example of measuredpulse rate, which later can be used to estimate PRV, can beseen in the Fig. 3. PR measurements on 3 participants for60 seconds long sessions can be seen in the Table 1.

Arterial Oxygen Saturation

Pulse oximeters are among the most popular ap-plications of photoplethysmography, which benefits fromwavelength-dependent extinction coefficient characteristicof oxyhemoglobin and deoxyhemoglobin.

For this purpose, PPG measurements must be madewith at least 2 different wavelengths which are chosenmostly as 660 nm and 940 nm. This requirement is sat-isfied by the multiplexer of ADPD105, red and infraredLEDs on the board. However, empirical calibration is re-quired to derive the model between the arterial oxygen satu-ration (SpO2) and so-called ratio of ratios (RoR), which canbe calculated as in the Equation 1 after a set of approxima-tions [19], since the Beer-Lambert Law does not considerscattering effect of tissue [20]. Therefore, this paper lacks

SpO2 estimations and represents calculated RoR values on3 participants, in the Table 1. As SpO2 is expected to be al-ways higher than 95% at physiological conditions, remark-able differences between sessions are not expected.

RoR =IAC,red

IDC,red/IAC,infrared

IDC,infrared(1)

Perfusion Index and Pleth Variability Index

Perfusion Index (PI) can vary from the measurementsite and is best followed as a trend in long time monitoringas an indicator of peripheral perfusion status. Pleth Variabil-ity Index (PVI), which was suggested to be a useful methodof evaluating fluid status, can also be calculated after PI mea-surements to reflect respiratory-induced changes in PI overtime [21].PI and PVI can be calculated according to Equa-tions 2 and 3 respectively. PI measurements at finger tip of 3subjects can be seen in the Table 1.

PI =AC

DC× 100% (2)

PV I =(PImax − PImin)

PImax× 100% (3)

POSTER 2017, PRAGUE MAY 23 5

Parameter Subject 1 Subject 2 Subject 3

PR 58.602± 6.166 67.691± 8.762 70.905± 3.812

PI 0.252± 0.056 1.3794± 0.229 1.398± 0.204

RoR 0.773± 0.042 0.793± 0.028 0.841± 0.085

AI −1.119± 0.221 −1.003± 0.257 −1.459± 0.188

b/a −0.913± 0.111 −0.889± 0.077 −1.113± 0.057

c/a 0.339± 0.111 0.243± 0.101 0.533± 0.075

d/a −0.232± 0.090 −0.198± 0.066 −0.296± 0.064

e/a 0.081± 0.089 0.044± 0.047 0.097± 0.081

Tab. 1. Pulse Rate (PR), Perfusion Index (PI) and ratio-of-ratios (RoR) measurements of 3 volunteers along withthe b/a, c/a, d/a, and e/a and Aging Index (AI).

Arterial Stiffness and Aging Index

The shape of the PPG signal can be used to derive in-formation as it is defined by the whole cardiovascular sys-tem, unlike the amplitude or the intensity depend mostlyon the properties of peripheral cardiovascular system. Sec-ond derivative of photoplethysmography (SDPPG) was sug-gested by Takazawa (1998) as a tool to assess cardiovascularage. The most prominent properties of SDPPG can be seenin the Fig. 4, which are a, b, c, d and e points. The ratio ofb/a is considered as arterial stiffness index, as studies haveshown that a and b waves lay in the early systolic componentwhere the effect of reflection wave is less [22].

The ratios of b/a, c/a, d/a, and e/a have been shownto age related and thus, SDPPG Aging Index (AI) can bedefined as (b-c-d-e)/a which have been shown to be affectedby cardiovascular health [22].Measurements on 3 subjectscan be seen in the Table 1.

0 100 200 300 400 500 600 700 800 900 1000Normalized Width to 1000 samples

-20

-15

-10

-5

0

5

10

15

20

LS

B [.]

Second Derivative PPG

c

b

d

a

e

Fig. 4. Second derivative of a filtered PPG waveform after widthnormalization.

4.3. Venous Diagnostic

Venous Muscle Pump Test

Venous muscle pump test (VMPT) is a well establishedmethod for evaluating venous valves. In this test, the patientexecutes calf extensions while sitting, which decreases ve-nous blood filling through muscle compressing. In a healthyindividual, empty leg veins are refilled by arterial inflowslowly, whereas it may take place much faster for a pa-tient with incompetent venous valves due to pathologic re-flux [23]. For this evaluation two parameters are used: ve-nous refilling time (VRT) and venous pump power. Aftercalf extensions, the time interval, in which the signal inten-sity turns back to its initial level, is measured. For healthyindividuals, VRT is interpreted longer than 20 seconds [24].

SmartPPG also makes it possible to automatize this testwith its on-board accelerometer. Calf extension required atthe beginning can easily be detected and further be used tostart evaluation of VRT measurement. A sample measure-ment on a participant can be seen in the Fig. 5.

0 10 20 30 40 50 60time [s]

1.885

1.89

1.895

1.9

1.905

1.91

1.915

1.92

1.925

LS

B [.]

×104 Venous Muscle Pump Test

Calf Extensions

Venous Refilling Time

Fig. 5. VMPT without tourniquet, showing VRT is around30 seconds.

Venous Oxygen Saturation

For the measurement of venous oxygen satura-tion (SvO2), the gold standard is the measurement of ex-tracted venous blood which is highly invasive and does notallow continuous monitoring. A noninvasive PPG solution,which also employs small finger cuff, was developed so thatsmall pressure oscillations around the finger would create anartificial pulsation at a certain frequency which should bechosen distant from heart rate and be extracted after signalprocessing [25]. The limitations on the pressure level of thecuff is the main drawback of this method.

Another promising method was suggested by Blazek et.al. (2017), which benefits from venous pulsation created dur-ing the venous muscle pump test (VMPT). By applying mea-surement method of arterial oxygen saturation to these pulsa-tions ratio of ratios (RoR) can again be calculated, this timefor venous oxygen saturation [26]. After applying a widetourniquet to block arterial inflow, VMPT was performed tocreate venous flow and oxygen saturations were calculated

6 D. U. UGUZ, DESIGN OF A MULTIPURPOSE PPG SENSOR TO ASSIST CARDIOVASCULAR DIAGNOSIS

using a commercial pulse oximeter and observed consistentlevel difference in arterial and venous oxygen saturation[26].

What this method promises is the measurement of oxy-gen intake defined as in the Equation 4. A single sessionmeasurement on a 24-year-old subject can be seen in theFig. 6. As mentioned earlier, this paper does not propose amodel between RoR and oxygen saturation which should de-veloped through in vivo experiments. To measure ∆SpO2,the simple linear model in the Equation 5 [20]. Accordingto this model, measurements give mean value of 15.05% for∆SpO2, which can be interpreted as expected at physiolog-ical conditions [27].

∆SpO2 = SpaO2 − SpvO2 (4)

SpO2 = 110 − 25 ×RoR (5)

0 1000 2000 3000 4000 5000 6000samples at 100Hz

1.98

1.985

1.99

1.995

2

2.005

2.01

LS

B [.]

×104 Red and Infrared PPG during active calf extension

1.075

1.08

1.085

1.09

1.095

1.1

1.105

1.11

LS

B[.]

×104

Infrared PPG

Red PPG

Active calf extension

venous pulse arterial pulse

infrared - 940nm

red - 660nm

Fig. 6. Red and Infrared PPG signals during and after active calfextensions. During interrupted arterial perfusion, a ve-nous pulse can detected to be used later on for venousoxygen saturation measurement.

5. ConclusionsPhotoplethysmography is an ever-growing field with

many applications both in clinic and research, therefore, acompact multipurpose sensor module, which can be driveneasily, is often a great need. Photoplethysmography mea-surements, depending on the application, can be made withdifferent tissue depths with different wavelengths. For thepurpose of better signal acquisition and processing, an ac-celerometer can be used. For the further diagnostic purposes,a thermometer can be very useful. In designed sensor sys-tem, many of these requirements are substantially satisfied.SmartPPG can take measurement from two different wave-lengths at two different tissue depths at the same time, whichcorresponds to 4 different information source with a sam-pling frequency that can go up to 3kHz.

AcknowledgementsThe author acknowledges Prof. Dr. Steffen Leonhardt

and Dr.-Ing. Boudewijn Venema for supervising the workwhich was funded by the Federal Ministry for Economic Af-fairs and Energy under The Central Innovation Programme.The project SmartPPG was conducted in cooperation withBernd Marquardt, Ingo Untch und Michael Liesenberg, EL-CAT GmbH, Wolfratshausen. The author also acknowledgesTEV - DAAD joint scholarship program for supporting hisstudies in Germany.

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About Authors. . .

Durmus Umutcan UGUZ

was born in Antalya,Turkey, on May 3, 1992.

Completing his B.Sc. de-gree at Middle East Tech-nical University, Turkey, heis pursuing M.Sc. degreein Electrical Engineering atRWTH Aachen University,Germany. His research in-terests include biomedical signal acquisition and processing.