Remote Photoplethysmography for the Neuro-electrostimulation

Procedures Monitoring

The Possibilities of Remote Photoplethysmography Application for the Analysis of

High Frequency Parameters of Heart Rate Variability

Vladimir Kublanov, Konstantin Purtov and Daniil Belkov

Research and Development Medical and Biological Engineering Center of High Technologies,

Ural Federal University, Mira str. 19, 620002, Yekaterinburg, Russian Federation

Keywords:

Autonomic Nervous System, Remote Photoplethysmography, Blood Volume Pulse, Heart Rate Variability,

Respiration, Remote Sensing.

Abstract:

The paper presents assessments of the remote photoplethysmography (rPPG) capabilities for evaluation of

heart rate variability (HRV) for monitoring the neuro-electrostimulation procedures. In our experiment, 20

minute long videos of 20 people in ofﬁce lighting conditions were analyzed. We checked the accuracy of

well-known methods and some modern methods of rPPG. In this work, we evaluated the accuracy of rPPG

methods in high frequency (HF) band (0.4 - 0.15 Hz), and sub-bands (0.4 - 0.3 Hz), (0.3 - 0.15 Hz). For

the sub-band (0.3-0.15 Hz) HRV signals obtained with rPPG are better correlated with HRV signals obtained

with electrocardiography (ECG). The results have shown that POS method provides the best HRV parameter

evaluation.

1 INTRODUCTION

Due to increased mortality from cardiovascular dis-

eases devices for continuous monitoring of human

physiological parameters were actively developed in

the past decade. Nowadays the size of holter mon-

itors has been signiﬁcantly reduced. Fitness tracker

and other devices for everyday use have appeared.

The main disadvantage of such devices is their inﬂu-

ence on the organism caused by the contact with the

body. Thus, the remote monitoring methods are more

promising.

The cardiovascular system is one of the most im-

portant element of the human body. The heart rate

(HR) and heart rate variability (HRV) are the main

parameters that allow to investigate the cardiovascu-

lar system functioning.

In recent years researchers have presented a num-

ber of new methods for non-contact physiological

monitoring by using the remote photoplethysmogra-

phy technology (rPPG). There are already more than

300 articles about the development of this technology.

In these studies, the possibilities of applying rPPG

technology as an alternative channel for monitoring

the human state were actively investigated. In partic-

ular, there were approaches that use rPPG for mon-

itoring premature babies (N. Blanik and Leonhardt,

2016) and patients under anesthesia (U. Rubins and

Miscuks, 2013).Lots of articles have shown that rPPG

can reliably measure the HR in comparison with the

contact photoplethysmography. It was shown that it

is possible to determine the parameters of pulse rate

variability, blood oxygen saturation, respiratory rate

and other parameters in controlled experiments.

In many studies the video records shorter than 5

minutes were used. For example, in (M.Z.Poh and Pi-

card, 2011), (Y. Sun and Hu, 2012) the investigation

of pulse rate variability parameters in HF ( 0.4 - 0.15

Hz), LF ( 0.15 - 0.04 Hz) bands was conducted. In

such short periods of time subject is able to sit still.

So the inﬂuence of movements was minimal. Such

conditions impose signiﬁcant limitations on the ap-

plicability of rPPG technology.

In other studies (Y. Sun and Greenwald, 2013),

(A.A Kamshilin and Giniatullin, 2013), specialized

light sources were used to enhance the rPPG sig-

nals. In (M.Z.Poh and Picard, 2011), (A. Moco and

de Haan, 2015), sources of ambient light, such as the

sun or a lamp ”Philips HF-3319 EnergyLight White”

in front of the skin were used. These sources provide

a uniform level of illumination. Such sources illu-

minate the face almost regardless of the position of

Kublanov V., Purtov K. and Belkov D.

Remote Photoplethysmography for the Neuro-electrostimulation Procedures Monitoring - The Possibilities of Remote Photoplethysmography Application for the Analysis of High Frequency

Parameters of Heart Rate Variability.

DOI: 10.5220/0006176003070314

the head. However, light sources are usually located

above the head. Due to that, there is some reﬂection

and regions with different luminance.

The studies (M.Z.Poh and Picard, 2011), (Y. Sun

and Greenwald, 2013) evaluate the HRV parameters

in comparison with the contact photoplethysmogra-

phy (PPG). However, the most examined and reliable

way to determine the changes of the autonomic ner-

vous system by means of the HRV evaluation is based

on electrocardiography (ECG).

Earlier, we have presented the possibilities of as-

sessing the VLF and LF parameters assessment by

using the PCA-based rPPG (Kublanov and Purtov,

2015). However, it was shown that this method does

not give a correct estimation of the HF band.

The main idea of this work is to evaluate the pos-

sibilities of rPPG for detecting changes of HRV in the

HF band. To do this, we evaluate the accuracy of the

rPPG methods compared with the parameters of HRV

obtained by ECG.

Besides, we measure the respiratory rate (RR) by

using the motion tracking techniques with preprocess-

ing by Eulerian Video Magniﬁcation method. Such

techniques now are very popular, but they work only

if the person has relatively stable position in time.

2 MATERIALS AND METHODS

2.1 Methods

In recent years researchers have presented a number

of new methods for recovering rPPG signals. Their

full comparison with all the components and condi-

tions is not possible in a single article. Therefore, in

this paper we consider the methods, which:

1. Allow to process a signals in real-time;

2. Work with only one digital RGB camera;

3. Have varying physiological models of signals.

Thus, we choose the following methods: RoverG,

XoverY, CHROM, ICA, PCA, 2SR, POS. These

methods were proposed in the works (M.Z.Poh

and Picard, 2011),(D. McDuff and Picard, 2014),

(M. Lewandowska and Kocejko, 2011), (de Haan

and Jeanne, 2013), (W. Wang and de Haan, 2015),

(W. Wang and de Haan, 2016).

The settings for each of the rPPG method were

used in accordance with recommendations of the au-

thors. For example, the ICA method was imple-

mented in accordance with the speciﬁcations: we split

the signals to the 30 seconds overlap-add windows,

and use the JADE ICA algorithm. The pulse compo-

nent selection was based on the FFT analysis After

the selection, the signals were inverted according to

the rule: µ

peakamp

< µ

troughamp

where µ

peakamp

is the mean absolute peak value,

troughamp

is the mean absolute trough value

Each rPPG method was used on the same skin

areas (pixels) to ensure the correctness of compari-

son. For each subject, the choice of skin boundaries

was made in HSV and YC

color formats. Before

the processing, the color boundaries were determined

manually to get the maximum skin area on the face

without areas with glare, because reﬂectance has great

inﬂuence on the parameters of rPPG signal.

In the ﬁrst image of the video, the face area was

detected by using the Viola-Jones method (Viola and

Jones, 2001). In other images, it was tracked by us-

ing implementation of KCF method (F. Henriques and

Batista, 2015) which allows to work in real-time.

The method presented in a master thesis (Balakr-

ishnan, 2014) was used to RR detection. It allows to

estimate the RR signals by tracking the chest move-

ments with motion preprocessing by Eulerian video

magniﬁcation.

2.1.1 Evaluation Metrics

For comparison of different rPPG methods, we evalu-

ate their performance by the following metrics.

• The Pearson correlation coefﬁcient was used to

evaluate the accuracy of different rPPG methods.

It allow to compare HR and HRV signals mea-

sured by rPPG with reference to PPG and ECG

signals.

• The signal to noise ratio (SNR) was used to as-

sess the quality of measured rPPG signals. It is

derived by the ratio between the energy around

the ﬁrst HR harmonic and the remaining parts in

the 4 - 0.5 Hz frequency band. The location of the

ﬁrst harmonic is determined by the contact PPG-

signal. It is was measured as follows:

SNR = 10 ·lg



signal

)

noise

)



, (1)

where U

signal

is the intensity of the ﬁrst heart rate

harmonic, U

noise

is the intensity of the remaining

parts in the (4 - 0.5 Hz) frequency band.

2.2 Experiment

This work is performed at the Research and Devel-

opment Medical and Biological Engineering Center

of High Technologies, Ural Federal University (Rus-

sian Federation) with partisipation of employees of

the Psychiatry Department, Ural State Medical Uni-

versity (Russian Federation). Ethical committee ap-

proved of this study. Informed consent was obtained

from each subject. Twenty healthy volunteers (males

and females) aged from 20 to 25 took part in this

investigation. Records were made after using the

”SYMPATHOCOR-01” neuro-electrostimulation de-

vice (Kublanov, 2008).

Neuro-electrostimulation process with using the

SYMPATHOCOR-01 device is the procedure when

the device generates the ﬁeld of spatially distributed

current pulses at the subject neck. The neuro-

electrostimulation effect can be measured by different

techniques, such as the ECG. It was decided to check

the accuracy of rPPG methods after the stimulation

procedure.

The experiment is conventionally divided into 3

parts. The ﬁrst and the third parts are the 5 minute rest

periods when the subject does not do anything. In the

second part of the experiment the subjects performed

the ”Bourdon test” which took 10 minutes duration.

The test task is not considered in this study.

2.2.1 Description

Simultaneous recording of the video and human phys-

iological parameters (ECG, PPG, respiratory rate)

was carried out during the experiment. The studies

were all conducted indoors without sunlight. Each

subject sat in front of a monitor of a personal com-

puter (PC). The webcam was placed on the right from

the monitor at a distance of 0.5 meters from a subjects

face. The experiment room was illuminated by two

usual ﬂuorescent light sources placed on the ceiling.

Each experiment was recorded and took 20 minutes.

Subjects were asked to sit in front of the camera and

perform a computer test.

All methods were implemented as a real-time ap-

plication in the Python 2.7. The application uses

the popular open-source packages OpenCV 2.4.11,

numpy, PyQt. The implementation runs on a com-

mon personal computer with computatonal unit Intel

Core i7 4770, 3,4 GHz, and 8 Gb DDR4 RAM.

2.2.2 Video Recording

Each experiment were recorded by using the low-

cost webcam Logitech C920, which allows to cap-

ture RGB frames approximately at 30 frames per sec-

ond (fps) in color (24 bits, 8 bit per color chan-

nel). The camera resolution was 640 × 480 pixels.

Each image frame was saved in raw png format to

the local database with a ﬁlename which contains the

time of the frame capture. The template of a ﬁle-

name format is ”image[%d] yyyy-MM-dd hh-mm-

ss.msec.png”, where %d means the sequential num-

ber of the image. Such a ﬁlename format allows to

accurately determine capture moments.

2.2.3 Contact Measurements

The referenced HR, HRV and RR signals were ob-

tained by the rehabilitation complex ”REACOR”

(MEDICOM MTD, Russian Federation). It pro-

vides a real-time registration of respiratory monitor-

ing, ECG and PPG with 250 Hz sampling rate and

stores the signals in the PC. To analyse and compare

the signals, all data were saved as comma-separated

text ﬁles in the local database. To ensure good elec-

trical contact of the electrodes with the body, gel

”Uniagel” (Geltek-Medica, Russian Federation) was

used. The changes in ﬁnger blood ﬂow are measured

by contact photoplethysmography from left foreﬁn-

ger. RR signal was measured by using the respiration

transducer belt.

3 RESULTS

In this section, the results of measuring HR, HRV

and RR obtained by different rPPG methods are pre-

sented. For convenience, we show the evaluation only

for 7 subjects, but their data are comparable with other

subjects.

3.1 Heart Rate

Figure 1 shows the spectrograms of measured sig-

nals. First column contains spectrograms of PPG sig-

nals. The other columns present the spectrograms

of rPPG signals obtained by the following meth-

ods: PCA, ICA, 2SR, POS, CHROM, XoverY and

RoverG. All spectrograms were calculated for 10-

second time windows with 5-second overlap.

The HR was determined as the maximum power

spectrum in the (4 - 0.5 Hz) frequency band, which

corresponds to the range of 30 to 240 heart beats per

minute. In this case the error of determining HR was

less a 0.1 Hz.

Tables 1 and 2 represent the accuracy of the HR

for these rPPG methods. Table 1 contains the aver-

age value of the absolute difference between rPPG

HR and PPG HR. Table 2 contains the values of stan-

dard deviation for the difference between rPPG HR

and PPG HR.

Currently, ICA and PCA methods are the most

common ways to measure rPPG signals and estimate

the HR. According to the data presented in Table 1,

Figure 1: The spectrograms of PPG and rPPG signals; rows denote the unique subjects.

Table 1: Mean values of absolute difference between HR

rPPG and HR PPG, msec.

Subjects

PCA

ICA

2SR

POS

CHROM

XoverY

RoverG

1 133 302 11 8 8 8 11

2 184 154 74 75 74 76 84

3 372 145 8 8 9 9 16

4 189 181 3 4 4 3 6

5 414 207 23 30 31 18 45

6 301 88 18 9 14 19 13

7 168 77 10 12 10 11 14

Table 2: Standart deviation of absolute difference between

HR rPPG and HR PPG, msec.

Subjects

PCA

ICA

2SR

POS

CHROM

XoverY

RoverG

1 236 292 64 46 45 47 32

2 226 230 184 185 185 187 186

3 414 273 32 33 34 34 46

4 339 265 16 19 19 15 22

5 401 281 74 102 102 47 115

6 255 204 78 36 60 73 49

7 264 160 35 39 34 36 42

HR accuracy obtained by these methods is signif-

icantly worse than HR accuracy obtained by other

methods.

Table 3: Mean value of SNR rPPG signals with 10 sec

overlap-add window, dB.

Subjects

PCA

ICA

2SR

POS

CHROM

XoverY

RoverG

1 7.30 6.85 10.15 10.70 10.65 10.60 8.85

2 7.15 7.05 9.35 9.75 9.80 9.60 8.40

3 6.50 6.85 9.65 10.00 10.05 9.90 8.95

4 6.30 6.25 10.95 11.40 11.30 11.35 8.95

5 5.80 5.95 8.95 9.45 9.15 8.95 7.60

6 6.05 7.35 9.25 10.10 10.15 9.75 9.45

7 6.35 7.10 9.85 9.90 10.3 10.30 9.25

The HR estimates obtained by 2SR, POS,

CHROM, XoverY are highly reliable and slightly dif-

ferent from each other. The RoverG method is one of

the ﬁrst rPPG method. So, it is more inaccurate than

others, but much better than PCA or ICA.

The quality of rPPG signals was evaluated by SNR

metric, which was described in the previous section.

Table 3 presents the results of the average SNR, where

each score was obtained as the average value of SNR

calculated for 10 second intervals with a 5 second

overlap.

According to Table 3, it is evident that CHROM

and POS methods show the largest SNR values. In

our tests for 20 people, the difference between these

methods does not exceed 1 dB. ICA and PCA meth-

ods showed the lowest SNR results. This may be due

to the large time intervals (30 seconds) required for

the correct calculation of these methods.

3.2 Heart Rate Variability

HRV signals are of great interest in the evaluation of

parameters of cardiovascular system. According to

the international standard of research (HRV, 1993),

HRV signal can be determined as the distance be-

tween the peaks of the PPG signal.

To increase the accuracy of peaks localization,

each rPPG signal was interpolated to 250 Hz sampling

rate by using a cubic spline interpolation. It was se-

lected to correspond the accuracy of contact methods.

After that, all signals were ﬁltered by the 5 or-

der Butterworth bandpass ﬁlter with 1 Hz bandwidth

and central frequency selected according to the cur-

rent HR. To calculate the pulse to pulse intervals, the

common PPG peak detection algorithm was used.

All extrasystoles in the HRV ECG signals were re-

moved in accordance with the article (T. Briiggemann

and Schroder, 1996).

Tables 4, 5, 6 present the results of a comparative

analysis of the ECG HRV and rPPG HRV signals in

VLF (0.04 - 0.003 Hz), LF (0.15-0.04 Hz) and HF

(0.4 - 0.15 Hz) bands. The Pearson correlation coefﬁ-

cient was used as the similarity criterion.

According to Tables 4, 5, 6, PCA, ICA methods

show signiﬁcantly poorer scores than other methods.

It can be explained by the fact that the subjects were

working with the PC, and, thus, did not control their

behavior during the video recording.

According to Table 4, in the VLF band the HRV

rPPG signals were determined with high accuracy by

methods POS, CHROM, XoverY and 2SR. The Pear-

son correlation coefﬁcient for these methods in all

studies is higher than 0.9. The most accurate esti-

mates were obtained by POS method.

Table 5 shows that in the LF band the most ac-

curate methods are POS, CHROM, XoverY and 2SR.

The Pearson correlation coefﬁcient in this case varies

from 0.7 to 1.

In the HF band, the maximum values of the Pear-

son correlation coefﬁcient match to POS, CHROM,

XoverY and 2SR. However, these values were

changed in the range from 0 to 0.9. This suggests a

weak correlation between rPPG HRV and ECG HRV

signals in the HF band.

3.3 Respiration Rate

The Pearson correlation coefﬁcients obtained in HF

band were low. Therefore, it was decided to deter-

mine the accuracy of various subbands of HF. First of

all the accuracy of RR selection was checked. The

Table 4: Pearson correlation coefﬁcient between ECG HRV

and rPPG HRV VLF (0.04 - 0.003 Hz) band.

Subjects

PCA

ICA

2SR

POS

CHROM

XoverY

RoverG

1 0.48 0.42 0.95 0.97 0.98 0.98 0.92

2 0.38 0.59 0.89 0.87 0.93 0.87 0.87

3 0.12 0.06 0.97 0.99 0.98 0.96 0.94

4 0.41 0.25 1.00 1.00 0.94 1.00 0.89

5 0.06 0.03 0.93 0.95 0.90 0.93 0.67

6 0.10 0.32 0.93 0.97 0.94 0.94 0.96

7 0.32 0.73 0.95 0.99 0.99 0.98 0.97

Table 5: Pearson correlation coefﬁcient between ECG HRV

and rPPG HRV LF (0.15 - 0.04 Hz) band.

Subjects

PCA

ICA

2SR

POS

CHROM

XoverY

RoverG

1 0.02 0.09 0.76 0.81 0.85 0.81 0.49

2 0.09 0.30 0.69 0.73 0.72 0.69 0.60

3 0.04 0.27 0.86 0.92 0.90 0.84 0.75

4 0.12 0.01 0.95 0.95 0.84 0.97 0.63

5 0.02 0.17 0.72 0.79 0.62 0.70 0.47

6 0.03 0.25 0.67 0.81 0.68 0.78 0.75

7 0.05 0.19 0.68 0.87 0.88 0.83 0.72

Table 6: Pearson correlation coefﬁcient between ECG HRV

and rPPG HRV in HF (0.4 - 0.15 Hz) band.

Subjects

PCA

ICA

2SR

POS

CHROM

XoverY

RoverG

1 0.04 0.03 0.44 0.48 0.50 0.41 0.30

2 0.04 0.12 0.45 0.54 0.39 0.47 0.25

3 0.05 0.03 0.63 0.67 0.66 0.57 0.44

4 0.12 0.01 0.53 0.31 0.38 0.55 0.11

5 0.03 0.06 0.18 0.36 0.13 0.26 0.07

6 0.08 0.16 0.53 0.56 0.42 0.55 0.51

7 0.13 0.07 0.71 0.74 0.76 0.74 0.56

respiration is the main component in HF band. Usu-

ally it has the biggest values in HRV HF spectrogram.

Figure 2 presents the spectrograms of RR signals

measured by different methods. Each row contains

the spectrograms of signals for a single subject. The

contact RR (cRR) signal was obtained by using res-

piration transducer belt placed on the subject chest.

Video RR (vRR) was obtained by using the method

based on Eulerian video magniﬁcation with tracking

points on the subject chest by Lucas-Kanade algo-

rithm. The other columns in the ﬁgure present the

HRV signals that were obtained by ECG and rPPG

techniques in HF band.

Figure 2: The spectrograms of HRV signals obtained by rPPG and ECG, and respiration signals measured by video (vRR)

and respiration belt (cRR).

Table 7: The comparision of RR signals measured by video

and respiration belt.

Subjects

mean

std

cRR

min

vRR

min

cRR

max

vRR

max

1 0.03 0.09 0.15 0.35 0.47 0.50

2 0.07 0.09 0.15 0.33 0.45 0.51

3 0.11 0.10 0.18 0.27 0.42 0.55

4 0.01 0.05 0.33 0.33 0.53 0.56

5 0.04 0.06 0.23 0.27 0.53 0.57

6 0.02 0.05 0.30 0.30 0.45 0.52

7 0.03 0.09 0.15 0.35 0.47 0.55

Table 8: Pearson correlation coefﬁcient between ECG HRV

and rPPG HRV in HF (0.4 - 0.3 Hz) sub-band.

Subjects

PCA

ICA

2SR

POS

CHROM

XoverY

RoverG

1 0.03 0.02 0.43 0.47 0.49 0.43 0.34

2 0.02 0.09 0.41 0.50 0.30 0.46 0.18

3 0.01 0.01 0.41 0.39 0.43 0.32 0.26

4 0.15 0.04 0.47 0.11 0.27 0.46 0.01

5 0.00 0.11 0.06 0.19 0.05 0.04 0.02

6 0.06 0.11 0.39 0.34 0.22 0.32 0.29

7 0.14 0.01 0.66 0.66 0.70 0.67 0.47

According to the whole set of data some of which

are presented in Figure 2, signals vRR match well

with signals cRR. Table 7 shows the comparision of

Table 9: Pearson correlation coefﬁcient between ECG HRV

and rPPG HRV in HF (0.3 - 0.15 Hz) sub-band.

Subjects

PCA

ICA

2SR

POS

CHROM

XoverY

RoverG

1 0.04 0.02 0.47 0.51 0.53 0.43 0.28

2 0.10 0.14 0.47 0.57 0.44 0.47 0.31

3 0.08 0.07 0.74 0.78 0.77 0.69 0.55

4 0.09 0.00 0.60 0.49 0.48 0.64 0.19

5 0.04 0.01 0.34 0.46 0.24 0.40 0.11

6 0.08 0.21 0.62 0.69 0.53 0.67 0.63

7 0.10 0.14 0.73 0.79 0.80 0.78 0.63

accuracy for cRR and vRR signals. The ﬁrst col-

umn corresponds to the mean deviation RR for 60-

second time intervals with 30 second overlap. The

second column shows the values of the standard devi-

ation. Other columns contain the values which corre-

spond to minimal (cRR

min

and vRR

min

) and maximal

(cRR

max

and vRR

max

) measured values.

It can be seen that the HRV ECG signals were

slightly modulated by respiration. In many cases, they

contain other fundamental frequencies. In HRV rPPG

signals the breath in fact is absent. HRV signals ob-

tained by ICA and PCA methods in HF band look like

noise.

We hypothesized that for frequencies below the

mean breathing rate the correlation will be high. The

HF band was divided into two sub-bands 0.4 - 0.3 Hz

and 0.3-0.15 Hz. The Pearson correlation coefﬁcients

for the signals in these sub-bands are shown in Tables

8 and 9 respectively.

According to Tables 8 and 9, the lower frequency

sub-bands of HRV rPPG and HRV ECG are more cor-

related. In this case the value of correlation coefﬁcient

has increased in all studies.

Therefore, RR measured by rPPG methods should

be tested additionally, for example, using the method

of chest movement evaluation.

4 CONCLUSION

One of the main results of this study is the compari-

son of existing real-time rPPG methods. It was shown

that the most common methods ICA and PCA have

the worst assessment of HR and HRV. Recently intro-

duced POS method has the greatest accuracy in HR

and HRV estimation. It allows to detect rPPG signals

even under changing light conditions.

Another conclusion is that the existing rPPG tech-

niques allow to measure the parameters of HRV with

a low-cost camera in VLF (0.04 - 0.003 Hz) and LF

(0.15-0.04 Hz) bands with high-precision accuracy.

The HRV in HF band has the lowest reliability. Our

study showed that the low frequency sub-band of the

HF has a larger correlation with HRV ECG than the

high frequency sub-band.

RR is another parameter which can be checked

by video. It was shown that under normal condi-

tions HRV rPPG signals contain almost no informa-

tion about respiration. Instead of this, the accuracy

of motion detection method proved to be the most

reliable for breathing detection. Therefore, the best

way to determine RR is the estimation of chest move-

ments.

In future works, we plan to investigate the possi-

bilities of rPPG methods in clinical practice, and us-

ing the rPPG as the biofeedback method for neuro-

electrostimulation.

ACKNOWLEDGEMENTS

We would like to thank the volunteers for participa-

tion in this study.

The work was supported by Act 211 Government

of the Russian Federation, contract 02.A03.21.0006.

And partially supported by Russian Foundation for

Assistance to Small Innovative Enterprises (FASIE)

(Russia).

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