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Abstract and Figures

Background: A growing number of wearable devices claim to provide accurate, cheap and easily applicable heart rate variability (HRV) indices. This is mainly accomplished by using wearable photoplethysmography (PPG) and/or electrocardiography (ECG), through simple and non-invasive techniques, as a substitute of the gold standard RR interval estimation through electrocardiogram. Although the agreement between pulse rate variability (PRV) and HRV has been evaluated in the literature, the reported results are still inconclusive especially when using wearable devices. Aim: The purpose of this systematic review is to investigate if wearable devices provide a reliable and precise measurement of classic HRV parameters in rest as well as during exercise. Materials and methods: A search strategy was implemented to retrieve relevant articles from MEDLINE and SCOPUS databases, as well as, through internet search. The 308 articles retrieved were reviewed for further evaluation according to the predetermined inclusion/exclusion criteria. Results: Eighteen studies were included. Sixteen of them integrated ECG - HRV technology and two of them PPG - PRV technology. All of them examined wearable devices accuracy in RV detection during rest, while only eight of them during exercise. The correlation between classic ECG derived HRV and the wearable RV ranged from very good to excellent during rest, yet it declined progressively as exercise level increased. Conclusions: Wearable devices may provide a promising alternative solution for measuring RV. However, more robust studies in non-stationary conditions are needed using appropriate methodology in terms of number of subjects involved, acquisition and analysis techniques implied.
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Folia Medica I 2018 I Vol. 60 I No. 1
DOI: 10.2478/folmed-2018-0012
Can Wearable Devices Accurately Measure Heart Rate Variability?
A Systematic Review
Konstantinos Georgiou1, Andreas V. Larentzakis2, Nehal N. Khamis3, Ghadah I. Alsuhaibani3,
Yasser A. Alaska3, Elias J. Giallafos4
1 Department of Biological Chemistry, Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
2 1st Propaedeutic Surgical Clinic, Medical School, National and Kapodistrian University of Athens, Athens, Greece
3 Clinical Skills & Simulation Center, Department of Medical Education, College of Medicine, King Saud University, Riyadh, KSA
4 Medical Physics Simulation Center, Medical School, National and Kapodistrian University of Athens, Athens, Greece
Konstantinos Georgiou, Depart-
ment of Biological Chemistry,
Faculty of Medicine, National and
Kapodistrian University of Athens,
75 Mikras Asias Str., Goudi, 11527
Athens, Greece
Tel: +306942066216
Received: 27 Dec 2017
Accepted: 19 Jan 2018
Published Online: 31 Jan 2018
Published: 30 March 2018
Key words: heart rate, heart rate
variability, wearable, photople-
Citation: Georgiou K, Larentzakis
AV, Khamis NN, Alsuhaibani GI,
Alaska YA, Giallafos EJ. Can wear-
able devices accurately measure
heart rate variability? A system-
atic review. Folia Med (Plovdiv)
doi: 10.2478/folmed-2018-0012
Background: A growing number of wearable devices claim to provide accurate,
cheap and easily applicable heart rate variability (HRV) indices. This is mainly ac-
complished by using wearable photoplethysmography (PPG) and/or electrocar-
diography (ECG), through simple and non-invasive techniques, as a substitute of
the gold standard RR interval estimation through electrocardiogram. Although
the agreement between pulse rate variability (PRV) and HRV has been evaluated
in the literature, the reported results are still inconclusive especially when using
wearable devices.
Aim: The purpose of this systematic review is to investigate if wearable devices
provide a reliable and precise measurement of classic HRV parameters in rest as
well as during exercise.
Materials and methods: A search strategy was implemented to retrieve relevant
articles from MEDLINE and SCOPUS databases, as well as, through internet search.
The 308 articles retrieved were reviewed for further evaluation according to the
predetermined inclusion/exclusion criteria.
Results: Eighteen studies were included. Sixteen of them integrated ECG - HRV
technology and two of them PPG - PRV technology. All of them examined wear-
able devices accuracy in RV detection during rest, while only eight of them during
exercise. The correlation between classic ECG derived HRV and the wearable RV
ranged from very good to excellent during rest, yet it declined progressively as
exercise level increased.
Conclusions: Wearable devices may provide a promising alternative solution for
measuring RV. However, more robust studies in non-stationary conditions are
needed using appropriate methodology in terms of number of subjects involved,
acquisition and analysis techniques implied.
Monitoring and analysis of heart rate (HR) pro-
vide valuable information regarding health status
and have been extensively investigated in various
activities of healthy subjects as well as in patients
suffering from various diseases.1,2 Heart rate vari-
ability (HRV) has emerged as a non-invasive tool
to estimate the vagal activity in several conditions,
including monitoring of athletic responses to training.
Decreased HRV has been reported as a predictive
factor for adverse outcomes in disease states and
has been found to be associated with fatigue, stress,
and even burnout during athletic performance.3-5
High HRV is an indication of a better general health
status as it allows better adjustment to external and
internal stimuli.6
Due to the fact that traditional HRV recording
methods, such as using electrocardiography (ECG)
and specialized software, often involve expensive
equipment, which is primarily found in research
laboratories, alternative methods have been used,
yet, with variable results. Photoplethysmography
(PPG) is a simple and low-cost method used to
detect volumetric changes in the peripheral blood
circulation at the skin surface.7
In recent years, several wearable pulse rate moni-
K. Georgiou et al
Folia Medica I 2018 I Vol. 60 I No. 1
tors using PPG technology have been developed and
have become widely available. The general concept
of operation of these small, robust and user-friendly
devices is that they contain sensors which reliably
monitor minor changes in the intensity of light from
high intensity light emitting diodes (LEDs) that is
transmitted through or re ected from the human
tissues. Although they have obvious advantages
over the classical ambulatory ECG recording, the
fact that they use PPG, i.e. a different detecting
approach, raises the question of how much accurate
and reliable are their results when compared to the
gold standard ECG method?
The purpose of this systematic review is to
present the available literature comparing the ECG
derived RR and HRV with that of the wearable
commercially available devices in terms of accuracy
and reliability as well as to reveal their strengths
and limitations in the everyday clinical practice.
This systematic review was conducted by search-
ing medical literature in MEDLINE and SCOPUS.
The search was guided by the Preferred Reporting
Items for Systematic Reviews and Meta-Analyses
(PRISMA) statement in conjunction with the PRIS-
MA explanation and elaboration document.8,9 The
last search was conducted in April 2017. All the
retrieved article titles and abstracts were screened for
relevant manuscripts. Full texts were then retrieved
for the relevant articles and these were thoroughly
read to judge if they meet the inclusion criteria of
the current systematic review.
Medical Subject Heading (MeSH) terms and text
words were used based on the following search
Group A terms: (HRV) OR heart rate variability
Group B terms: ((((smart) OR Smartphone$) OR
wearable$) OR phone$ OR plethysmography OR
photoplethysmography OR impedance)
Group C terms: (((((holter) OR continuous
ECG) OR continuous electrocardiogram) OR con-
tinuous electrocardiography) OR ambulatory ECG)
OR ambulatory electrocardiogram OR ambulatory
Group A, group B, and group C were combined
and humans’ studies and English language limits
were applied.
We also searched the reference lists of retrieved
full text review manuscripts for the relevant articles
(regardless of their publishing date) and included
these in our systematic review.
Well de ned inclusion/exclusion criteria were
applied to lter the retrieved literature. Of the ar-
ticles retrieved through the above described search
strategy only those that met the following criteria
were considered for this systematic review:
Inclusion criteria:
Studies related to human subjects.
Studies reported in full text English language.
Studies related to HRV. Studies on HRV detec-
tion and/or analysis and/or interpretation and/or
ltering were included.
Studies that compare ECG/Holter recordings of
HRV with any other wearable HRV detection and
capture method(s) were included.
Original papers were only included; however,
reference lists from other kind of manuscripts were
used to retrieve any relevant original studies.
Exclusion criteria:
Studies that did not include HRV were excluded.
St udies that did not use commercially available
wearable hardware were excluded.
In addition, we performed Google search for
wearable devices which claimed that they can
measure HRV via plethysmography HR and are
available on the market. Our search spanned the
last 5 years. The devices list (Table 1) was used as
an additional retrograde search tool for any relevant
studies through manufacturers’ commercial websites.
Two of the authors (KEG and AL) independently
applied the above described search strategy to re-
trieve and screen the articles. Any disagreements
were resolved by a third author (AJG) and a nal
decision was made accordingly.
Applying the search strategy described in the methods
section, we retrieved 57 articles from MEDLINE and
269 from SCOPUS. Exclusion of duplicates yielded
308 articles. Of these articles, 272 were excluded
after title and abstract screening according to the
prede ned criteria. Thirty-six articles were selected
for full text review. Full text reading resulted in
exclusion of further 30 articles, which were found to
be either irrelevant (n=28) or non-comparison papers
(n=2). The remaining six articles were included in
the study. Additionally, 12 relevant peer-reviewed
articles were identi ed from the reference list of
the reviews and from manufacturers’ commercial
websites and added to the study. So, 18 articles were
nally included in this systematic review. The ow
diagram of the selection process is shown in Fig. 1.
Wearable Devices Accuracy of HRV Estimation
9Folia Medica I 2018 I Vol. 60 I No. 1
Table 1. Capable to measure HRV wearable models: sensor location, release year and connectivity type
Model Sensor site Company Release Year Connectivity
1 4IIII VIIIIVA chest 4IIII 2013 B & A
2 60beat HR Monitor chest 60beat 2016 B & A
3 HRM Blue chest BlueLeza 2016 B & A
4 Dash Earphones ear Bragi 2014 B
5 TP3 chest Cardiosport 2015 B & A
6 Hexoskin torso Carre Technologies 2014 B & USB
7 Empatica E4 Wristband wrist Empatica 2016 B
8 R2 Smart Fitness HRM Wristband chest Cositea 2016 B & A
9 EQ02 LifeMonitor chest Equivital 2012 B, P & USB
10 Forerunner 935 wrist Garmin 2016 B & A
11 HRM Tri wrist Garmin 2016 B & A
12 Premium Heart Rate Monitor chest Garmin 2016 B & A
13 Vívoactive HR+ wrist Garmin 2015 B & A
14 910XT wrist Garmin 2016 B, P & A
15 920XT wrist Garmin 2016 B, P & A
16 HeartMath emWave Pro ear HeartMath 2015 B & A
17 Athos torso, legs & thighs Mad Apparel 2015 B
18 HxM Smart HR chest Medronic 2017 B & Gateway
19 Alpha 2 wrist Mio 2015 B & A
20 Oxstren hand Oxstren 2015 B
21 H7 Heart Rate Monitor chest Polar 2012 B & WiFi
22 H10 chest Polar 2016 B & WiFi
23 QardioCore chest Qardio 2017 B
24 SmartBand 2 wrist Sony 2016 B
25 Smart Sensor chest Suunto 2017 B & A
26 Spartan Sport wrist Suunto 2017 B & A
27 DGYAO® Mobile HR Monitor ear Top Yao 2016 B
28 Tickr Heart Rate Monitor chest Wahoo Fitness 2015 B & A
29 Tickr X Workout Tracker chest Wahoo Fitness 2017 B & A
30 WHOOP Strap 2.0 wrist Whoop 2016 B
31 Tinké Fitness & Wellness Tracker ngers Zensorium 2016 B & A
B: Bluetooth, A: ANT+, P: proprietary
K. Georgiou et al
Folia Medica I 2018 I Vol. 60 I No. 1
Figure 1. Flow diagram.
Table 2 summarizes the quantitative and quali-
tative methodology used and the categorization
(measurement, position, location, sample number,
technology used) as well as the main ndings of
the included studies.
The total number of subjects involved in the
included studies was 686, with one study hav-
ing 339 subjects.10 All 18 articles have examined
healthy subjects except one: Vasconcellos et al.
(2015) who studied obese adolescents.11 Also, one
study examined children.12 In most of the articles
(n=16), a chest device was used,10-25 while a n-
ger device was used in two studies26,27. Sixteen
of the studies utilized a similar to ECG - HRV
technology10-25 and the remaining two integrated
PPG - PRV technologies26,27.
All studies examined the HRV/PRV at rest as
baseline. Overall, agreement between the several
indices of HRV and PPG as measured by Holter
and wearable devices, respectively, was very good
to excellent (ranging from 0.85 to 0.99). The RR
interval correlation ranged from 0.91 to 0.999.
Moreover, in two studies,12,15 the error rate in
detection of R waves was evaluated and found to
range between 0.28 and 0.4%. This was estimated
as an accepted ratio. Regarding time domain indi-
ces of HRV, correlations ranged from 0.98 to 0.99,
while in the frequency domain the correlation was
Wearable Devices Accuracy of HRV Estimation
11Folia Medica I 2018 I Vol. 60 I No. 1
Table 2. Results
# Study Rest Supine Upright
/ Sitting
Walking /
Running /
Comparison Details
1 Akintola et
al., 2016
Y Y Y Y Chest
18 ECG Correlation
depends on
Average artifact % 19%. AMV
(r: 0.967), SDNN (0.393), RMSSD
(0.285), SDANN (0.680), pnn50
2 Esco & Flatt
Y Y Y N Finger
30 PPG Good agreement LOA Ln RMSSD constant error ± SD:
-0.13 ± 2.83 for the supine values,
-0.94 ± 3.47 for the seated values,
-1.37 ± 3.56 for the standing values. (r
values from 0.98 to 0.99).
3 Flatt &
Esco, 2013
Y Y N N Chest
25 ECG Total agreement No signi cant difference, correlation
nearly perfect for RMSSD (r: 0.99).
4 Gamelin et
al, 2006
Y Y Y N Chest
20 ECG Good agreement Supine vs. standing: differences for
uncorrected & corrected RR coef cient
correlation: 0.88 & 0.91 for supine &
standing. No differences except RMS-
SD, SD1 in standing. Detection Error
Rate of R waves: 0.4%.
5 Gamelin et
al, 2008
Y Y N N Chest
12 ECG No difference Correlation between ECG & Polar RR
intervals (corrected & uncorrected) was
0.80. No signi cant differences for
Time Domain, FFT & Poincare plot
except for SD2. R waves detection er-
ror rate: 0.28%.
K. Georgiou et al
Folia Medica I 2018 I Vol. 60 I No. 1
6 Giles et al,
Y Y Y N Chest
18 ECG Accurate. Good
No signi cant differences for SDNN,
7 Heathers,
Y N Y Y Finger
20 PPG Good agreement
at rest.
Mildly reduced
agreement during
Experiment 1: Close agreement – small
overall bias & acceptable limits of
agreement. RR & PP correlation coef-
cient (0.988 – 0.999). All HRV from
SPRV > than ECG.
Experiment 2: Close agreement be-
tween RR & PP (highest at rest, r:
0.993 – 0.997, slightly attenuated in
exercise (0.965 – 0.998).
8Hernando et
al, 2016
Y N Y Y Chest (Belt) 23 ECG As exercise
increases, correla-
tion decreases
19/23 had high correlation at rest (r>
0.8). Discrepancy increased from 1.67%
(at rest) 4.8% at the exercise peak. As
exercise increases, reliability & agree-
ment indices drop below 0.5.
9Hong et al,
N N N Y Chest
18 ECG As exercise
increases, cor-
relation decreases
Coef cient correlation of HRV (r2: 0.965
or higher), HF, LF
10 Kingsley et
al, 2005
Y N Y Y Chest (Belt) 8 ECG High agreement
higher at rest.
Reduced agree-
ment at exercise.
Short relationship between RR internals
during exercise. Similar results obtained
for the RR internal but signi cant differ-
ences occurred for HRV indices.
11 Nunan et al,
Y Y N N Chest (Belt) 33 ECG Accurate / Good
No signi cant differences for HRV indi-
ces – LOA for mean RR, LFnn, HFnn,
12 Nunan et al,
Y Y N N Chest (Belt) 33 ECG Good agreement
Not for all indi-
Correlation coef cient: 0.99, 0.86, 0.85
for mean RR, LFnn, HFnn respectfully.
Near perfect correlation for SDNN,
RMSSD (0.99, 0.37). Good correlation
for LF (0.92), HF (0.94), LF/HF (0.87).
All measures of HRV ranged from 0.85-
Wearable Devices Accuracy of HRV Estimation
13Folia Medica I 2018 I Vol. 60 I No. 1
LOA: level of agreement
13 Plews et al,
Y N Chest (Belt) 29 ECG Acceptable agree-
Almost perfect correlation all HRV indi-
ces (RMSSD)
14 Romagnoli et
al, 2014
Y N Y Y Chest (Vest) 12 ECG Good agreement
for RR / unequal
results for HRV.
Mean RR, SDNN & SD2, excellent LOA.
RMSSD, HF, LF/HF, HF & SD1 worst
15 Vanderlei et
al, 2008
Y N Y Y Chest (Belt) 15 ECG As exercise
increases, correla-
tion decreases.
HRV indices assessed: PNN50, RMSSD,
LFnu, HFnu, LF/HF.
16 Vasconcellos
et al, 2015
Y Y N Chest (Belt) 14 ECG Very good cor-
Moderate to strong agreement (0.68-0.98)
for HR, RR, pNN50, rMSSD, LF, HF,
LF/HF. In general moderate agreement
for frequencies domain measures.
17 Wallen et al,
Y Y N N Chest (Belt) 339 ECG Good to excellent
328 subjects. Gender & age dependent:
females especially > 60 yrs old present
reduced correlation). ICC coef cients in
women than men more pronounced in
SDNN.HRV from Polar Gender + Age
dependent. ICC > 0.8 on all HRV param-
eters among men. Agreement moderate
on all HRV among men.
18 Weippert et
al, 2010
Y Y N Y Chest (Belt) 19 ECG Limited agree-
Good correlation (narrow LOA & small
bias). ICC for HRV frequencies were
high, however in most cases LOA
showed unacceptable discrepancies.
K. Georgiou et al
Folia Medica I 2018 I Vol. 60 I No. 1
found to range from 0.85 to 0.94. Finally, in the
non-linear multivariate analysis, the correlation was
found to be > 0.9.
Eight out of the 18 examined studies used an exer-
cise protocol, with a total of 131 participants. All of
them reported that although there was an excellent
correlation between HRV and PPG as measured by
Holter and wearable devices at rest, this seemed to
decrease up to 0.85 as the level of exercise and/or
motion increased. Overall, RR agreement was moder-
ate to excellent ranging from 0.786 to 1. Regarding
time domain, HRV parameters correlation was found
to range from 0.786 (at the exercise peak) to 1 (rest
and exercise 1st phase). Similar pattern occurred
in the frequency domain HRV parameters where
the correlation was found to range from 0.8 to 1.
Also, RR to pulse pressure (PP) wave correlation
was ranging from 0.8 to 0.998.
In recent years, technology advances have been used
to capture HRV through wearable devices during
daily activities. The accuracy of these devices versus
classical methods like ECG is still under evaluation.
This systematic review aims to present the avail-
able relevant literature and discuss their ndings,
their limitations as well as to provide possible
explanations for these ndings.
It is important to realize that the basic difference
between PPG and ECG is the captured signal per
se: the electrical activity of the heart is depicted
by ECG, whereas the PPG is a mechanical signal
measuring the propagation of the peripheral pulse
wave. Therefore, the time of propagation of the
PP wave from the heart to the distal arterioles is
called pulse transit time (PTT). It is a measure
of the time that elapsed between the R-wave of
QRS complex in the ECG and the arrival point
to PPG device.28 Several studies have shown that
PTT seems to be a surrogate marker of ANS in
parallel to HRV29 and that PTT is dependent on the
properties of the pulse wave velocity, the vascular
path from the heart to the location of the detector
and is negatively correlated with blood pressure,
arterial stiffness and age.7
Therefore, we will discuss HRV and PPG fun-
damentals as well as the factors affecting them
in order to elaborate PPG versus ECG for HRV
HRV analysis is a widely available and accurate
non-invasive technique used as a quantitative assess-
ment tool of the autonomic nervous system (ANS)
function.30 Studies have shown that reduced HRV
indices, as assessed by the RR interval analysis, is
associated with increased cardiovascular morbidity
and mortality in patients with various diseases as
well as in the general population.31 In addition, heart
rate and HRV analysis have been used for estimation
of mental stress and athlete’s tness levels, fatigue
and overload.5
The quanti cation of ANS function is feasible
by calculating several HRV parameters according
to time-domain, frequency-domain and nonlinear
analysis of consecutive RR intervals.1 These indices
represent different components of the sympathetic
and/or parasympathetic system of the ANS. For
instance, the high frequency (HF) component de-
rived by the frequency domain analysis represents
the parasympathetic activity, while the LF/HF ratio
represents the balance of sympathetic to parasym-
pathetic activity.32
However, it is important to realize that these
HRV indices depends on the recording quality, the
subject’s activity during the recording, the exclu-
sion of artifacts, the detection of arrhythmic beats
and the recording duration ranging from seconds
(short term HRV) to even days (long term). Some
of these indices, like the root mean square of
standard deviation (RMSSD) of RR interval can be
calculated from a 10-second session while others
need more than one-hour recording time.33
Despite being accepted as gold standard methods
for RR interval monitoring and analysis, both the
classical ECG and the ambulatory Holter monitoring
have several drawbacks regarding the proper and
accurate detection of RR intervals. For example,
patients with tremor or elderly patients with fragile
skin have bad quality of recordings with a lot of
noise and artifacts.34 Similarly, other factors such
as surface electromyography, increased electrode
impedance, respiration induced baseline drift, and
electrode contact movement can cause noise and
motion artifacts.7 Additionally, morphological varia-
tions in the ECG waveform and heterogeneity in the
QRS complex can often make dif cult to identify
the RR interval.35 Another limitation can be the
need for the presence of a specialized technician/
doctor, thus increasing the cost and accordingly
decreasing the wide applicability. Finally, a reported
drawback in ECG wearable devices that do not
record standard ECG derivations is their inability
Wearable Devices Accuracy of HRV Estimation
15Folia Medica I 2018 I Vol. 60 I No. 1
to distinguish some arrhythmias and ectopic beats.10
Photoplethysmography (PPG), a cheap, simple
and widespread technology has been used as an
alternative approach to obtain HRV indices.7 The
PPG based devices have a sensor that uses infrared
emitter and a detector. This emitter is integrated
to a probe which is comfortable to wear in stable
places of the body that are rich in microcirculation.
Thus, the blood volume changes in the microvascu-
lar bed which are synchronous to the heartbeat can
be traced without the inconveniences of electrode
installation or the need to undress the examinee.1
The simplicity of the technique, cost-effectiveness,
easy signal acquisition and remote monitoring are
the main and obvious advantages of the PPG versus
the gold standard ECG. Therefore, PPG is often
used in conditions of measurements where mobil-
ity, simplicity, time ef ciency, exibility and low
cost are of paramount importance, e.g., in exercise
monitoring, every day motion, monitoring of the
elderly, or with disabled patients, etc.7,36
In the relevant literature of the wearable PPG
devices the terms ‘heart rate’ and ‘pulse rate’ are
frequently used interchangeably. Also, the term
‘pulse rate variability’ (PRV), which is derived
from PPG, has been suggested as a potential ana-
log of HRV.37,38
As PRV is further processed identically to HRV,
the derived parameters can be extracted from both
methods such as the standard deviation of normal
to normal (SDNN) R–R intervals (NN), root mean
square of successive differences between adjacent
NN intervals (RMSSD), proportion of NN50 in
total NN intervals (pNN50), low frequency (LF)
power, high frequency (HF) power and LF/ HF
ratio (LF/HF).
Recently, latest technology smart phones ap-
plications with wearable devices26,27 use PPG for
assessing HRV as an alternative to ECG monitoring
in clinical research. More information is available
in Table 2.
PPG uses low intensity infrared or green light, which
are more strongly absorbed by the blood than the
surrounding tissues. It has been shown that 530 nm
light (green) PPG showed higher accuracy of pulse
rate detection than the 645 (red) and 470 (blue) nm
light for monitoring HR.39
A at skin surface with rich microvasculature is
required to rmly attach the PPG sensor to obtain
an accurate measurement. As such, the usual mea-
surement sites for wearable PPGs are the wrist and
the chest. Most of the wearable devices are placed
on the wrist and considerably fewer of them on
the chest.
However, it is worth noting that there is also a
bunch of quasi-wearable devices attached to either
the ear lobe or the nger which compared HRV
to PRV parameters.
The ear is chosen as a measuring site because
it is a natural anchoring point, and it is discrete
since the device can be partially hidden by hair.
Weinschenk et al.28 compared PPG to ECG HRV
measurements in deep breathing test in 343 female
subjects in resting conditions and using appropri-
ate parameters they found an excellent correlation.
Finger tips have also been used as a measuring
site but only in stationary conditions: it has been
shown that HRV derived from ngertip PPG had
an excellent correlation to ECG in stationary con-
ditions.38 However, a comparative study of nger
derived PRV and HRV in healthy subjects40 using
a stationary equipment found a poor correlation
and suggested that nger derived PRV might not
be suitable to substitute ECG derived HRV, as has
been mentioned by other authors, too41.
The use of a smart phone camera as PPG sensor
can provide an acceptable agreement for some HRV
indices when compared with ECG,22 but there are
increasing differences in HRV and HR detection
in the setting of movements or exercise42. This is
because the application algorithm is cancelled in
the ltering process of inaccurate signals as well
as due to the dif culty of stabilizing the ngertip
on the camera while exercising.
Several parameters have to be considered when
interpreting PPG measurements. These include:
1. Motion artifact: Special attention must be ex-
ercised during data PPG acquisition to eliminate
motion-induced artifacts.7 The contact force between
the site and the sensor should be considered as PPG
is vulnerable to such type of artifacts. However,
despite the importance of this factor, we found only
one study in 16 male ischemic patients measuring
the accuracy of a smart phone derived pulse rate
versus ECG, where an excellent correlation was
found at rest, which was slightly deteriorated during
2. Respiration: Since respiration alters the intra-
thoracic pressure and causes blood ow variations
K. Georgiou et al
Folia Medica I 2018 I Vol. 60 I No. 1
in both the veins the DC component of the PPG
waveform shows minor changes with respiration.43
Thus, it has been shown that the short-term variabil-
ity (RMSSD, SD1, and HF) and LF/HF agreement
between PRV and HRV deteriorates as a result of
the vulnerability to respiratory changes.44
3. Age, gender and environmental factors: Normal
HRV values for various age and gender groups are
still not available in the literature. However, it is
well known that the elderly have increased arterial
stiffness which leads to faster pulse transmission in
the periphery and thus pulse transit time (PTT) dif-
ferences observed between HRV and PRV could be
attributed to aging.45 The reviewed articles, except
one,23 involved young population (mean range from
20.9 to 39.2 years).
Regarding gender in uence, in our review, just
one study showed that measuring HRV at rest
was age and gender dependent, the correlation
with ECG being lower in women than men and
further decreasing in older women.10 As there is
no strong evidence provided so far that age and/or
gender can play a role in the studied correlations,
further studies are needed to investigate these two
variables in different populations while using ap-
propriate sampling and prospective study design
with a longitudinal follow-up.28
Environmental factor effects such as temperature
was investigated in one of the studies. This study
concluded that ambient temperature could induce a
difference in the short-term variables that re ect the
parasympathetic activity between HRV and PRV.38
4. Software analysis: Some proprietary software
systems for collection and analysis of RV data
exist like the PPT5 or the IthleteTM software ap-
plication14,26,27 or freely available software may
be used e.g. Kubios (
According to guidelines2, manual editing should
be preferred instead of automated data analysis as
automatic lters are known to be unreliable and may
potentially introduce errors. In our review only six
studies used automated analysis only whether the
rest used both manual and automated analysis.
5. Statistical analysis: The Bland-Altman plot must
be used to compare the agreement among a new
measurement technique with a gold standard, as
even a gold standard does not imply to be without
error. This plot allows the identi cation of any
systematic difference between the measurements.47
In our review only 17 studies used this technique
while four studies did not apply the Bland-Altman
analysis and therefore only the correlation, but
not agreement between the two methods, could be
determined from these publications.
6. Sampling rate: The sampling rate is a matter of
difference between the two approaches. Sampling
rate of PPG is usually 20 Hz much less than that
of ECG which is 125 to 250 Hz. This obviously
implies weaker ability of the PPG devices for events
There are several studies examining the correlation
between HRV and PRV with inconclusive results.40
This may be due to different experimental settings
or to the absence of standardization of the meth-
ods of analysis used.44 It is worth noting that the
disagreement between the two methods does not
apply to the same extent to all HRV parameters.
Additionally, PPG is susceptible to motion artifacts.
As such, the accuracy of PRV as obtained from PPG
should be interpreted with caution.36
Our search revealed that the comparison studies
performed in stationary conditions have generally
revealed that PRV is a good surrogate of HRV.
This is in line with other studies not involving
wearable devices, which also found an acceptable
agreement between HRV and PRV at sitting and
resting positions.48
PRV becomes stronger at a standing than at a
supine position, as it re ects the mechanical cou-
pling between respiration and thoracic vasculature
tone. Therefore, when a subject changes his/her
position from supine to upright, even in resting
conditions, a PRV divergence from HRV becomes
apparent. Additionally, HRV indices derived from
PPG data are very sensitive to different factors
including noise, artifacts, stature, atherosclerosis,
location of sensor and sampling rate. It is probably
due to these reasons that some studies comparing
the two methods found differences among normal
healthy subjects7,36,45 as well as in patients40.
There are many non-stationary situations where
autonomic balance signi cantly changes like in
stress, during motion or exercise. Unfortunately, in
such situations where PRV would be more useful
as a surrogate measurement of HRV, its clinical
value is questionable: a moderate agreement was
observed in some studies about the factors affecting
the measurements when the subject is exercising or
having mental stress i.e. increased noise produc-
Wearable Devices Accuracy of HRV Estimation
17Folia Medica I 2018 I Vol. 60 I No. 1
tion, contraction of muscles which are in contact
with the sensors, sweating, increased intrathoracic
pressure altering the venous return, increased pe-
ripheral vasoconstriction and the respiratory effort
during exercise.
RR interval variables of Bioshirt ECG were
compared to those from conventional ECG and
found that R-peak detecting capabilities of these
two devices were largely similar. However, as
the level of exercise was increasing, the correla-
tion was decreasing due to artifact production. It
must also be noted that a disadvantage of chest
band wearable devices during intense exercise is
the discomfort that a subject senses as the chest
expands with deep breathing.18
Hernando et al. (2016)17 observed that although
an agreement between the detected R-peaks and
the RR intervals from the Polar wearable and ECG
existed, as the exercise intensity level was increased,
the discrepancy of the RR pairs Bland-Altman plot
also increased. They noticed a good correlation in
some but not in all of the HRV indices, due to
the disagreement of the relative error of the Polar
derived high frequency with that of the ECG, as
the level of the exercise increases.17
Akintola et al. (2016) used a chest wearable
device detecting ECG and HRV and reported enor-
mous amount of artifacts during daily activities in
18 healthy subjects and they concluded that this is
a limitation of the wearable device used.13
In agreement with all of the above, another
study showed that the limits of agreement were
deteriorating as the exercise was intensi ed, implying
an in uence of adrenergic input, respiratory effort
and unreliable algorithm detection and recording
RR ability.19
In contrast to the many negative results reported
during intense exercise, there are other research-
ers who reported an overall stronger agreement.49
Also, some other research groups reported that PPG
yielded higher HRV values.36 However, all these
studies involved only a sample size of few subjects.
Unfortunately, most of the ndings from our
review showed that the correlation was fading out
as the level of exercise and/ or motion increases.
Furthermore, the data from the reviewed studies
are not able to support an in-depth quantitative
analysis due to the differences in their methodology.
As wearable healthcare technology and the research
of light propagation in human tissues are progressing,
it is expected that PPG applications will expand.
For instance, there is a growing interest to remotely
depict PPG through imaging such as contactless
video-photoplethysmography (vPPG)50 or imaging
It is essential to develop advanced wearable
devices with higher accuracy, to minimize motion
artifacts as well as improved algorithms to better
detect and identify errors that may occur during
exercise and higher intensity motion.
Additionally, as the availability of wearable
devices is expanding, more research is obviously
warranted to establish age- and sex-dependent
normal PRV values as well as to standardize
both acquisition protocols and analytical methods
in order to get reliable and accurate results, thus
permitting these methods to become a valid sur-
rogate for HRV parameters.
Our systematic review revealed that wearable de-
vices, especially those using PPG, may provide a
promising alternative solution for measuring HRV.
However, it is evident that more robust studies in
non-stationary conditions are needed with appropriate
methodology in terms of number of subjects involved,
acquisition and analysis techniques implied, before
being able to recommend any of the commercially
available devices. Therefore, so far wearable devices
can only be used as a surrogate for HRV at resting
or mild exercise conditions, as their accuracy fades
out with increasing exercise load.
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Folia Medica I 2018 I Vol. 60 I No. 1
Возможности переносных приборов для точного измерения вари-
абельности сердечного ритма. Систематическое обозрение
Константинос Георгиу1, Андреас В. Ларенцакис2, Нехал Н. Камис3, Гада И. Алсунхаибани3,
Ясер А. Аласка3, Елиас Дж. Джиалафос4
1 Кафедра биохимии, Медицинский факультет, Национальный Афинский университет им. Каподистрия, Афины, Греция
2 Первая клиника хирургической пропедевтики, Медицинский факультет, Национальный Афинский университет им.
Каподистрия, Афины, Греция
3 Центр клинических умений и симуляции, Медицинский факультет, Медицинский колледж, Университет короля Сауда,
Эр-Рияд, Саудовская Аравия
4 Симуляционный центр медицинской физики, Медицинский факультет, Национальный Афинский университет им. Капо-
дистрия, Афины, Греция
Адрес для корреспонденции:
Константинос Георгиу, Кафедра
биохимии, Медицинский фа-
культет, Национальный Афин-
ский университет им. Капо-
дистрия, ул. „Микрас Асиас” 75,
Гоуди, 11527 Афины, Греция
Тел: +306942066216
Дата получения: 27 декабря
Дата приемки: 19 января 2018
Дата онлайн публикации: 31
января 2018
Дата публикации: 30 марта
Ключевые слова: сердечный
ритм, вариабельность сер-
дечного ритма, переносные,
Образец цитирования:
Georgiou K, Larentzakis AV,
Khamis NN, Alsuhaibani GI,
Alaska YA, Giallafos EJ. Can
wearable devices accurately
measure heart rate variability?
A systematic review. Folia Med
(Plovdiv) 2018;60(1):7-20
doi: 10.2478/folmed-2018-0012
Введение: Утверждается, что всё большее количество переносных приборов
обеспечивает установление точных, недорогих и легко применимых показате-
лей вариабельности сердечного ритма (ВСР). В основном это обеспечивается
приборами переносной фотоплетизмографии (ФПГ) и/или электрокардиогра-
фии (ЭКГ) с помощью простых и неинвазивных методов в качестве заместите-
лей золотого стандарта оценки интервала RR с помощью электрокардиограм-
мы. Хотя соответствие между вариабельностью частоты пульса (ВЧП) и ВСР
было исследовано в литературе, полученные результаты по-прежнему неубе-
дительны, особенно в отношении использования переносных устройств.
Цель: Целью настоящего систематического обозрения является установление
возможностей переносных приборов обеспечивать надёжное и точное изме-
рение классических параметров ВСР как в состоянии покоя, так и во время
физической нагрузки.
Материалы и методы: Была использована стратегия сбора данных для на-
хождения соответствующих статей в базах данных MEDLINE и SCOPUS, а также
в Интернете. Найденные 308 статей были рассмотрены для дальнейшей оцен-
ки в соответствии с заранее определёнными критериями включения / исклю-
Результаты: Восемнадцать исследований были включены. В шестнадцати
из них применялась технология ЭКГ – ВСР, а в двух из них - технология ФПГ
– ВЧП. Все они исследовали точность переносных приборов при измерении
СР во время отдыха, а только восемь из них во время физической нагрузки.
Корреляция между классической ВСР, полученной от ЭКГ, и переносной ВСР
варьировалась от очень хорошей до превосходной во время отдыха, но она
постепенно снижалась по мере увеличения нагрузки.
Заключение: Переносные приборы могут обеспечить альтернативное реше-
ние для измерения ВСР. Тем не менее, необходимы более надёжные исследо-
вания в нестационарных условиях с использованием соответствующей ме-
тодологии в отношении количества участвующих в исследовании субъектов,
использованных методов измерения и анализа.
... For example, Yuda and colleagues (16) outlined the steps between the electrical impulse within the heart to the mechanical pulse at peripheral site, suggesting that there is room for error between the two signals. More specifically, the authors, as well as others (1,20,21), have indicated that pulse transit time (PTT) is one measurable factor that can, in part, explain the differences between HRV and PRV. The PTT, defined as the time from the electrical R wave to the foot of the PPG-derived pulse wave (see Figure 1), can vary beat-by-beat due to autonomic, respiratory, or other modulations (e.g., vascular branching, aging), introducing meaningful heterogeneity to the HRV-PRV relationship. ...
... One specific factor that the authors mentioned is defined as PTT, with the speed of the pulse wave influenced by arterial radius, wall thickness and elasticity, and blood density (16). Further, these factors may be subject to change within various populations and situations (e.g., age, sex, activity, posture), thereby creating disconnect between heart rate and pulse rate (19,20,33). In support of this, the present study confirms that PRV and HRV may not be equivalent with greater PTT variance. ...
Full-text available
Background: Heart rate variability (HRV) is a common measure of autonomic and cardiovascular system function assessed via electrocardiography (ECG). Consumer wearables, commonly employed in epidemiological research, use photoplethysmography (PPG) to report HRV metrics (PRV), although these may not be equivalent. One potential cause of dissociation between HRV and PRV is the variability in pulse transit time (PTT). This study sought to determine if PPG-derived HRV (i.e., PRV) is equivalent to ECG-derived HRV and ascertain if PRV measurement error is sufficient for a biomarker separate from HRV. Methods: The ECG data from 1,084 subjects were obtained from the PhysioNet Autonomic Aging dataset, and individual PTT variances for both the wrist (n=42) and finger (n=49) were derived from Mol et al. A Bayesian simulation was constructed whereby the individual arrival times of the PPG wave were calculated by placing a Gaussian prior on the individual QRS-wave timings of each ECG series. The standard deviation of the prior corresponds to the PTT variances. This was simulated 10,000 times for each PTT variance. The root mean square of successive differences (RMSSD) and standard deviation of N-N intervals (SDNN) were calculated for both HRV and PRV. The Region of Practical Equivalence bounds (ROPE) were set a priori at 0.2% of true HRV. The Highest Density Interval (HDI) width, encompassing 95% of the posterior distribution, was calculated for each PTT variance. Results: The lowest PTT variance (2.0 SD) corresponded to 88.4% within ROPE for SDNN and 21.4% for RMSSD. As the SD of PTT increases, the equivalence of PRV and HRV decreases for both SDNN and RMSSD. Thus, between PRV and HRV, RMSSD is nearly never equivalent and SDNN is only somewhat equivalent under very strict circumstances. The HDI interval width increases with increasing PTT variance, with the HDI width increasing at a higher rate for RMSSD than SDNN. Conclusions: For individuals with greater PTT variability, PRV is not a surrogate for HRV. When considering PRV as a unique biometric measure, our findings reveal that SDNN has more favorable measurement properties than RMSSD, though both exhibit a non-uniform measurement error.
... There were previous studies that analyzed resting-state HRV features in IGD, and they reported that the high frequency (HF)-HRV index, which reflects prefrontal control, was reduced in subjects with IGD (26). Notably, HRV can be applied to wearable devices and has the advantage of being able to be measured not only in a resting state but also in an active state (27). Measuring changes in HRV according to changes in certain conditions is useful in that it can reflect autonomic reactivity to certain stimuli (28). ...
Full-text available
Background The diminished executive control along with cue-reactivity has been suggested to play an important role in addiction. Hear rate variability (HRV), which is related to the autonomic nervous system, is a useful biomarker that can reflect cognitive-emotional responses to stimuli. In this study, Internet gaming disorder (IGD) subjects’ autonomic response to gaming-related cues was evaluated by measuring HRV changes in exposure to gaming situation. We investigated whether this HRV reactivity can significantly classify the categorical classification according to the severity of IGD. Methods The present study included 70 subjects and classified them into 4 classes (normal, mild, moderate and severe) according to their IGD severity. We measured HRV for 5 min after the start of their preferred Internet game to reflect the autonomic response upon exposure to gaming. The neural parameters of deep learning model were trained using time-frequency parameters of HRV. Using the Class Activation Mapping (CAM) algorithm, we analyzed whether the deep learning model could predict the severity classification of IGD and which areas of the time-frequency series were mainly involved. Results The trained deep learning model showed an accuracy of 95.10% and F-1 scores of 0.995 (normal), 0.994 (mild), 0.995 (moderate), and 0.999 (severe) for the four classes of IGD severity classification. As a result of checking the input of the deep learning model using the CAM algorithm, the high frequency (HF)-HRV was related to the severity classification of IGD. In the case of severe IGD, low frequency (LF)-HRV as well as HF-HRV were identified as regions of interest in the deep learning model. Conclusion In a deep learning model using the time-frequency HRV data, a significant predictor of IGD severity classification was parasympathetic tone reactivity when exposed to gaming situations. The reactivity of the sympathetic tone for the gaming situation could predict only the severe group of IGD. This study suggests that the autonomic response to the game-related cues can reflect the addiction status to the game.
... We can attribute this decrease in performance of PRV metrics for cognitively challenging and movement conditions to motion artifacts. Earlier studies involving commercial wearable PPG devices have also reported similar decreases in PRV accuracy [87]. ...
Full-text available
The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit provides a one-stop pipeline consisting of (i) a sensing and data acquisition layer that can be configured in a modular manner per research needs, and (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study with 16 participants, PhysioKit shows strong agreement with research-grade sensors on measuring heart rate and heart rate variability metrics data. Furthermore, we report usability survey results from 10 small-project teams (44 individual members in total) who used PhysioKit for 4–6 weeks, providing insights into its use cases and research benefits. Lastly, we discuss the extensibility and potential impact of the toolkit on the research community.
... Heart rate detection contains two main manners: contact and non-contact, in which electrocardiograph (ECG) is the gold standard for heart rate detection [26]. The contact manner applies electrodes or sensors that are attached to the subject, so it is limited by the requirement for direct contact with the human body, prone to discomfort, and the complexity of the operation. ...
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In this work, an assistance system based on the Artificial Intelligence of Things (AIoT) framework was designed and implemented to provide convenience for visually impaired people. This system aims to be low-cost and multi-functional with object detection, obstacle distance measurement, and text recognition achieved by wearable smart glasses, heart rate detection, fall detection, body temperature measurement, and humidity-temperature monitoring offered by an intelligent walking stick. The total hardware cost is approximately $66.8, as diverse low-cost sensors and modules are embedded. Meanwhile, a voice assistant is adopted, which helps to convey detection results to users. As for the performance evaluation, the accuracies of object detection and text recognition in the wearable smart glasses experiments are 92.16% and 99.91%, respectively, and the maximum deviation rate compared to the mobile app on obstacle distance measurement is 6.32%. In addition, the intelligent walking stick experiments indicate that the maximum deviation rates compared to the commercial devices on heart rate detection, body temperature measurement, and humidity-temperature monitoring are 3.52%, 0.19%, and 3.13%, respectively, and the fall detection accuracy is 87.33%. Such results demonstrate that the proposed assistance system yields reliable performances similar to commercial devices and is impressive when considering the total cost as a primary concern. Consequently, it satisfies the fundamental requirements of daily life, benefiting the safety and well-being of visually impaired people.
... Finally, technical difficulties led to missing data for HRV. Since this study was designed, technology has advanced so that more accessible, cheaper 'wearable', devices have become more widely available with improved accuracy, comparable to that of classic ECG (Georgiou et al., 2018). ...
Objectives: Compassion-focused therapy (CFT) is an evolution-informed biopsychosocial approach that seeks to cultivate attachment and care motivational systems and their psychophysiological regulators. These can counteract some of the harmful effects of social threat, inferiority, shame, self-criticism and depression, which are common in people with psychosis and undermine their well-being, social trust and ability to feel safe. This study aimed to test the acceptability of a novel manualized individual CFT intervention for psychosis (CFTp). Design: A non-concurrent, multiple-baseline, case series design, with three phases: baseline, intervention and follow-up. Methods: The 26-session CFTp intervention was provided for a sample of eight people with distressing psychotic experiences and a psychosis-related diagnosis. The study aimed to assess acceptability of CFTp and to test clinically reliable improvements while receiving the intervention, compared to a baseline period. Results: Seven of eight participants completed the therapy, and clinically reliable improvements were found at both the single-case and group level of analysis. At the single-case level, over half the participants showed improvements in depression (5/7), stress (5/7), distress (5/7), anxiety (4/7) and voices (3/5). One participant showed a deterioration in anxiety (1/7) and dissociation (1/7). At the group level (n = 7), there were significant improvements in depression, stress, distress, voices and delusions. The improvements in voices, delusions and distress were sustained at 6- to 8-week follow-up, but depression and stress dropped slightly to trend-level improvements. Conclusions: CFTp is a feasible and acceptable intervention for psychosis, and further investigation is warranted with a randomized controlled trial.
... To differentiate between normal sinus rhythm and AF paroxysm, it is recommended to search for irregularities in the time series of beat-tobeat RR intervals or the absence of the P-wave in an ECG [3]. RR-intervals are sometimes the preferred medium due to their noise robustness [4] and the ability of inexpensive wearable devices to record heart rate [5]. Methods and algorithms designed for the task usually involve pattern matching [6], classical machine learning [7], or deep neural networks [8]. ...
Atrial fibrillation (AF) is one of the most common arrhythmias with challenging public health implications. Automatic detection of AF episodes is therefore one of the most important tasks in biomedical engineering. In this paper, we apply the recently introduced method of compressor-based text classification to the task of AF detection (binary classification between heart rhythms). We investigate the normalised compression distance applied to $\Delta$RR and RR-interval sequences, the configuration of the k-Nearest Neighbour classifier, and an optimal window length. We achieve good classification results (avg. sensitivity = 97.1%, avg. specificity = 91.7%, best sensitivity of 99.8%, best specificity of 97.6% with 5-fold cross-validation). Obtained performance is close to the best specialised AF detection algorithms. Our results suggest that gzip classification, originally proposed for texts, is suitable for biomedical data and continuous stochastic sequences in general.
... of GPS [8] and when using a chest strap for recording beat-to-beat intervals for HRV analysis [9]. The 100 ...
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The Polar Team Pro computer software program simultaneously analyzes heart rate variability for multiple athletes and requires little to no expertise from the user. Still, there remains the need to examine the accuracy and reliability of this candidate software when compared to the recognized gold standard HRV computer software program (Kubios HRV). Twenty-one (n =21) healthy female soccer players volunteered to be participants. An ultra-short recording period (60-second) of R-R intervals (ms) was collected in the supine position in a controlled laboratory setting over two data collection periods spaced one month apart. R-R intervals were exported into both the candidate and reference computer software for analysis. The square root of the mean squared differences of successive beat-to-beat intervals (rMSSD) were calculated along with mean R-R interval length. rMSSD values derived from the candidate software were compared to both the raw and artefact corrected values from the reference software. After performing artefact correction, mean (SD) rMSSD values were not statistically different between software (candidate = 61.2 ± 31.0 ms ‘vs’ reference = 63.1 ± 31.1 ms, p = 0.214). Mean (SD) R-R intervals were significantly different (candidate = 893.4 ± 119.8 ms ‘vs’ reference = 882.3 ± 111.3 ms, p = 0.003). Excellent reliability in artefact corrected rMSSD (r = 0.95, p < 0.001) and mean R-R interval length (r = 0.99, p < 0.001) was observed. The candidate software showed strong agreement and excellent reliability in calculating rMSSD when compared to the gold standard after artefact correction was applied.
... Traditionally, HRV-related parameters are derived from ECG signals. Recently, wearable devices with PPG sensors have been considered an alternative HRV measurement approach [19][20][21]. A previous study compared the pulse rate variability obtained from PPG with HRV information collected from a reference ECG device and found significant correlations of more than 82% for both time and frequency parameters [22]. ...
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Traditionally, the subjective questionnaire collected from game players is regarded as a primary tool to evaluate a video game. However, the subjective evaluation result may vary due to individual differences, and it is not easy to provide real-time feedback to optimize the user experience. This paper aims to develop an objective game fun prediction system. In this system, the wearables with photoplethysmography (PPG) sensors continuously measure the heartbeat signals of game players, and the frequency domain heart rate variability (HRV) parameters can be derived from the inter-beat interval (IBI) sequence. Frequency domain HRV parameters, such as low frequency(LF), high frequency(HF), and LF/HF ratio, highly correlate with the human’s emotion and mental status. Most existing works on emotion measurement during a game adopt time domain physiological signals such as heart rate and facial electromyography (EMG). Time domain signals can be easily interfered with by noises and environmental effects. The main contributions of this paper include (1) regarding the curve transition and standard deviation of LF/HF ratio as the objective game fun indicators and (2) proposing a linear model using objective indicators for game fun score prediction. The self-built dataset in this study involves ten healthy participants, comprising 36 samples. According to the analytical results, the linear model’s mean absolute error (MAE) was 4.16%, and the root mean square error (RMSE) was 5.07%. While integrating this prediction model with wearable-based HRV measurements, the proposed system can provide a solution to improve the user experience of video games.
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Any reliable biomarker has to be specific, generalizable, and reproducible across individuals and contexts. The exact values of such a biomarker must represent similar health states in different individuals and at different times within the same individual to result in the minimum possible false-positive and false-negative rates. The application of standard cut-off points and risk scores across populations hinges upon the assumption of such generalizability. Such generalizability, in turn, hinges upon this condition that the phenomenon investigated by current statistical methods is ergodic, i.e., its statistical measures converge over individuals and time within the finite limit of observations. However, emerging evidence indicates that biological processes abound with nonergodicity, threatening this generalizability. Here, we present a solution for how to make generalizable inferences by deriving ergodic descriptions of nonergodic phenomena. For this aim, we proposed capturing the origin of ergodicity-breaking in many biological processes: cascade dynamics. To assess our hypotheses, we embraced the challenge of identifying reliable biomarkers for heart disease and stroke, which, despite being the leading cause of death worldwide and decades of research, lacks reliable biomarkers and risk stratification tools. We showed that raw R-R interval data and its common descriptors based on mean and variance are nonergodic and non-specific. On the other hand, the cascade-dynamical descriptors, the Hurst exponent encoding linear temporal correlations, and multifractal nonlinearity encoding nonlinear interactions across scales described the nonergodic heart rate variability more ergodically and were specific. This study inaugurates applying the critical concept of ergodicity in discovering and applying digital biomarkers of health and disease.
Background: Recent studies have linked a low Heart Rate Variability (HRV) with COVID-19 infection, indicating that this parameter can be a marker of the onset of the disease, its severity, and predictor of mortality in infected people. Given the large offer of wearable devices that capture physiological signals of the human body easily and non-invasive, several studies used this equipment to measure the HRV of individuals and related these measures to the infection by COVID-19. Objective: The objective of this study was to assess the utility of HRV measurements obtained from wearable devices as predictive indicators for COVID-19 infection, as well as the onset and worsening of symptoms in affected individuals. Methods: A systematic review was conducted, searching the following databases up to the end of January 2023: Embase, PubMed, Web of Science, Scopus, and IEEE. Studies had to include (I) measures of HRV in patients with COVID-19 and (II) measurements involving the use of wearable devices. We also conducted a meta-analysis of these measures to reduce the possible biases and increase the statistical power of the primary research. Results: The main result was the association between low HRV and the onset and worsening of COVID-19 symptoms. In some cases, it was possible to predict the onset of COVID-19 before a positive clinical test. The meta-analysis of studies reported shows that a reduction in HRV parameters is associated with COVID-19 infection. Individuals with COVID-19 presented a reduction in the SDNN and RMSSD indices compared to healthy individuals. The decrease in the SDNN index (Standard Deviation of the Normal-to-Normal interbeat interval) was 3.25 ms (95% CI -5.34 to -1.16), and the decrease in the RMSSD index (Root Mean Square Successive Difference) was 1.24 ms (95% CI -3.71 to 1.23). Conclusions: Wearable devices that measure changes in HRV, such as smart watches, rings, and bracelets, provide information that allows identifying the COVID-19 infection during the presymptomatic period as well as its worsening, through an indirect and non-invasive self-diagnosis. Clinicaltrial: International Prospective Register of Systematic Reviews (PROSPERO; CRD42023399705).
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Healthy biological systems exhibit complex patterns of variability that can be described by mathematical chaos. Heart rate variability (HRV) consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs). A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis. This article briefly reviews current perspectives on the mechanisms that generate 24 h, short-term (~5 min), and ultra-short-term (<5 min) HRV, the importance of HRV, and its implications for health and performance. The authors provide an overview of widely-used HRV time-domain, frequency-domain, and non-linear metrics. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2 min to 24 h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. Non-linear measurements quantify the unpredictability and complexity of a series of IBIs. The authors survey published normative values for clinical, healthy, and optimal performance populations. They stress the importance of measurement context, including recording period length, subject age, and sex, on baseline HRV values. They caution that 24 h, short-term, and ultra-short-term normative values are not interchangeable. They encourage professionals to supplement published norms with findings from their own specialized populations. Finally, the authors provide an overview of HRV assessment strategies for clinical and optimal performance interventions.
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It is generally accepted that the activities of the autonomic nervous system (ANS), which consists of the sympathetic (SNS) and parasympathetic nervous systems (PNS), are reflected in the low- (LF) and high-frequency (HF) bands in heart rate variability (HRV)—while, not without some controversy, the ratio of the powers in those frequency bands, the so called LF-HF ratio (LF/HF), has been used to quantify the degree of sympathovagal balance. Indeed, recent studies demonstrate that, in general: (i) sympathovagal balance cannot be accurately measured via the ratio of the LF- and HF- power bands; and (ii) the correspondence between the LF/HF ratio and the psychological and physiological state of a person is not unique. Since the standard LF/HF ratio provides only a single degree of freedom for the analysis of this 2D phenomenon, we propose a joint treatment of the LF and HF powers in HRV within a two-dimensional representation framework, thus providing the required degrees of freedom. By virtue of the proposed 2D representation, the restrictive assumption of the linear dependence between the activity of the autonomic nervous system (ANS) and the LF-HF frequency band powers is demonstrated to become unnecessary. The proposed analysis framework also opens up completely new possibilities for a more comprehensive and rigorous examination of HRV in relation to physical and mental states of an individual, and makes possible the categorization of different stress states based on HRV. In addition, based on instantaneous amplitudes of Hilbert-transformed LF- and HF-bands, a novel approach to estimate the markers of stress in HRV is proposed and is shown to improve the robustness to artifacts and irregularities, critical issues in real-world recordings. The proposed approach for resolving the ambiguities in the standard LF/HF-ratio analyses is verified over a number of real-world stress-invoking scenarios.
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Purpose: To establish the validity of smartphone photoplethysmography (PPG) and heart rate sensor in the measurement of heart rate variability (HRV). Methods: 29 healthy subjects were measured at rest during 5 min of guided breathing (GB) and normal breathing (NB) using Smartphone PPG, heart rate chest strap and electrocardiography (ECG). The root mean sum of the squared differences between R-R intervals (rMSSD) was determined from each device. Results: Compared to ECG, the technical error of estimate (TEE) was acceptable for all conditions (average TEE CV% (90% CI) = 6.35 (5.13; 8.5)). When assessed as a standardised difference, all differences were deemed "Trivial" (average std. diff (90% CI) = 0.10 (0.08; 0.13). Both PPG and HR sensor derived measures had almost perfect correlations with ECG (R = 1.00 (0.99; 1:00). Conclusion: Both PPG and heart rate sensor provide an acceptable agreement for the measurement of rMSSD when compared with ECG. Smartphone PPG technology may be a preferred method of HRV data collection for athletes due to its practicality and ease of use in the field.
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Objective To evaluate the accuracy of a smartphone application measuring heart rates (HRs), during an exercise and discussed clinical potential of the smartphone application for cardiac rehabilitation exercise programs. Methods Patients with heart disease (14 with myocardial infarction, 2 with angina pectoris) were recruited. Exercise protocol was comprised of a resting stage, Bruce stage II, Bruce stage III, and a recovery stage. To measure HR, subjects held smartphone in their hands and put the tip of their index finger on the built-in camera for 1 minute at each exercise stage such as resting stage, Bruce stage II, Bruce stage III, and recovery stage. The smartphones recorded photoplethysmography signal and HR was calculated every heart beat. HR data obtained from the smartphone during the exercise protocol was compared with the HR data obtained from a Holter electrocardiography monitor (control). Results In each exercise protocol stage (resting stage, Bruce stage II, Bruce stage III, and the recovery stage), the HR averages obtained from a Holter monitor were 76.40±12.73, 113.09±14.52, 115.64±15.15, and 81.53±13.08 bpm, respectively. The simultaneously measured HR averages obtained from a smartphone were 76.41±12.82, 112.38±15.06, 115.83±15.36, and 81.53±13 bpm, respectively. The intraclass correlation coefficient (95% confidence interval) was 1.00 (1.00–1.00), 0.99 (0.98–0.99), 0.94 (0.83–0.98), and 1.00 (0.99–1.00) in resting stage, Bruce stage II, Bruce stage III, and recovery stage, respectively. There was no statistically significant difference between the HRs measured by either device at each stage (p>0.05). Conclusion The accuracy of measured HR from a smartphone was almost overlapped with the measurement from the Holter monitor in resting stage and recovery stage. However, we observed that the measurement error increased as the exercise intensity increased.
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The purpose of this study was to determine the agreement between a smartphone pulse finger sensor (SPFS) and electrocardiography (ECG) for determining ultra-short-term heart rate variability in 3 different positions. Thirty college-aged men (n = 15) and women (n = 15) volunteered to participate in this study. Sixty-second heart rate measures were simultaneously taken with the SPFS and ECG in supine, seated, and standing positions. The log transformed root mean square of successive R-R interval differences (lnRMSSD) was calculated from the SPFS and ECG. The lnRMSSD values were 81.5 ± 11.7 using ECG and 81.6 ± 11.3 using SPFS (p = 0.63, Cohen's d = 0.01) in the supine position, 76.5 ± 8.2 using ECG and 77.5 ± 8.2 using SPFS (p = 0.007, Cohen's d = 0.11) in the seated position, and 66.5 ± 9.2 using ECG and 67.8 ± 9.1 using SPFS (p < 0.001, Cohen's d = 0.15) in the standing position. The SPFS showed a possibly strong correlation to the ECG in all 3 positions (r values from 0.98 to 0.99). In addition, the limits of agreement (constant error ± 1.98 SD) were -0.13 ± 2.83 for the supine values, -0.94 ± 3.47 for the seated values, and -1.37 ± 3.56 for the standing values. The results of the study suggest good agreement between the SPFS and ECG for measuring lnRMSSD in supine, seated, and standing positions. Although significant differences were noted between the 2 methods in the seated and standing positions, the effect sizes were trivial.
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In this paper, classical time– and frequency-domain variability indexes obtained by pulse rate variability (PRV) series extracted from video-photoplethysmography signals (vPPG) were compared with heart rate variability (HRV) parameters extracted from ECG signals. The study focuses on the analysis of the changes observed during a rest-to-stand manoeuvre (a mild sympathetic stimulus) performed on 60 young, normal subjects (age: $24\pm 3$ years). The objective is to evaluate if video-derived PRV indexes may replace HRV in the assessment of autonomic responses to external stimulation. Video recordings were performed with a GigE Sony XCG-C30C camera and analyzed offline to extract the vPPG signal. A new method based on zero-phase component analysis (ZCA) was employed in combination with a fully-automatic method for detection and tracking of region of interest (ROI) located on the forehead, the cheek and the nose. Results show an overall agreement between time and frequency domain indexes computed on HRV and PRV series. However, some differences exist between resting and standing conditions. During rest, all the indexes computed on HRV and PRV series were not statistically significantly different (p > 0.05), and showed high correlation (Pearson's r > 0.90). The agreement decreases during standing, especially for the high-frequency, respiration-related parameters such as RMSSD (r = 0.75), pNN50 (r = 0.68) and HF power (r = 0.76). Finally, the power in the LF band (n.u.) was observed to increase significantly during standing by both HRV ($28\pm 14$ versus $45\pm 16$ (n.u.); rest versus standing) and PRV ($26\pm 12$ versus $30\pm 13$ (n.u.); rest versus standing) analysis, but such an increase was lower in PRV parameters than that observed by HRV indexes. These results provide evidence that some differences exist between variability indexes extracted from HRV and video-derived PRV, mainly in the HF band during standing. However, despite these differences video-derived PRV indexes were able to evince the autonomic responses expected by the sympathetic stimulation induced by the rest-to-stand manoeuvre.
This study aimed to comprehensively investigate the reliability of multiple heart rate variability (HRV) parameters, and to explore the influence of artefact removal and breathing condition on HRV reliability. Resting HRV was collected using Polar Team monitors on forty-one participants (age: 19.9±1.2 years; 28 females, 13 males) during two separate days. Within each session, participants performed 10 minutes each of spontaneous and controlled breathing (randomized order). Kubios HRV analysis software was used to analyze 180s data epochs using "low" or "strong" artefact removal. Relative reliability was assessed using intraclass correlation coefficients (ICC2,1) and absolute reliability was quantified using mean-normalized standard error of measurement (SEM%). Time domain and nonlinear parameters produced poor to good inter-session agreement (ICC:0.34-0.68; SEM%: 11.0-39.0) with "low" artefact removal, regardless of breathing condition. Frequency domain parameters demonstrated fair inter-session agreement during controlled breathing (ICC:0.40-0.45; SEM%: 26.0-70.0), but poor agreement during spontaneous breathing (ICC:0.07-0.13; SEM%: 32.0-81.0). Minimal differences in ICCs were observed between "low" and "strong" artefact removal. Thus, this study provides three practical applications: 1) HRV monitoring is most reliable when using time domain and nonlinear parameters, regardless of breathing or filtering condition, but no single parameter is especially reliable. The large variation and poor inter-session reliability of frequency indices during spontaneous breathing are improved by controlling breathing rate; 2) "Low" artefact removal appears superior to more aggressive artefact removal; and 3) Inter-session differences in HRV measurements <30% may be indicative of normal daily variation rather than true physiologic changes.
Electrocardiographic artifacts are defined as electrocardiographic alterations, not related to cardiac electrical activity. As a result of artifacts, the components of the electrocardiogram (ECG) such as the baseline and waves can be distorted. Motion artifacts are due to shaking with rhythmic movement. Examples of motion artifacts include tremors with no evident cause, Parkinson’s disease, cerebellar or intention tremor, anxiety, hyperthyroidism, multiple sclerosis, and drugs such as amphetamines, xanthines, lithium, benzodiazepines, or shivering (due to hypothermia, fever (rigor due to shaking), cardiopulmonary resuscitation by chest compression (oscillations of great amplitude) and patients who move their limbs during the test, causing sudden irregularities in the ECG baseline that may resemble premature contractions or interfere with ECG wave shapes, or other supraventricular and ventricular arrhythmias. When the skeletal muscles experience shaking, the ECG is “bombarded” by apparently random electrical activity.
Background and purpose Acute stress in surgery is ubiquitous and has an immediate impact on surgical performance and patient safety. Surgeons react with several coping strategies; however, they recognise the necessity of formal stress management training. Thus, stress assessment is a direct need. Surgical simulation is a validated standardised training milieu designed to replicate real-life situations. It replicates stress, prevents biases, and provides objective metrics. The complexity of stress mechanisms makes stress measurement difficult to quantify and interpret. This systematic review aims to identify studies that have used acute stress estimation measurements in surgeons or surgical trainees during real operations or surgical simulation, and to collectively present the rationale of these tools, with special emphasis in salivary markers. Methods A search strategy was implemented to retrieve relevant articles from MEDLINE and SCOPUS databases. The 738 articles retrieved were reviewed for further evaluation according to the predetermined inclusion/exclusion criteria. Results Thirty-three studies were included in this systematic review. The methods for acute stress assessment varied greatly among studies with the non-invasive techniques being the most commonly used. Subjective and objective tests for surgeons' acute stress assessment are being presented. Conclusion There is a broad spectrum of acute mental stress assessment tools in the surgical field and simulation and salivary biomarkers have recently gained popularity. There is a need to maintain a consistent methodology in future research, towards a deeper understanding of acute stress in the surgical field.
Heart rate variability (HRV) analysis during exercise is an interesting non-invasive tool to measure the cardiovascular response to the stress of exercise. Wearable heart rate monitors are a comfortable option to measure RR intervals while doing physical activities. It is necessary to evaluate the agreement between HRV parameters derived from the RR series recorded by wearable devices and those derived from an ECG during dynamic exercise of low to high intensity.23 male volunteers performed an exercise stress test on a cycle ergometer. Subjects wore a Polar RS800 device while ECG was also recorded simultaneously to extract the reference RR intervals. A time-frequency spectral analysis was performed to extract the instantaneous mean heart rate (HRM), and the power of low frequency (PLF) and high frequency (PHF) components, the latter centred on the respiratory frequency. Analysis was done in intervals of different exercise intensity based on oxygen consumption. Linear correlation, reliability and agreement were computed in each interval.The agreement between the RR series obtained from the Polar device and from the ECG is high throughout the whole test, although the shorter the RR is, the more differences there are. Both methods are interchangeable when analysing HRV at rest. At high exercise intensity, HRM and PLF still presented a high correlation (ρ>0.8) and excellent reliability and agreement indices (above 0.9). However, the PHF measurements from the Polar showed reliability and agreement coefficients around 0.5 or lower when the level of the exercise increases (for levels of O2 above 60%).