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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.
... It was shown that artifacts in the RRi data are observed when using HRM during rest and especially exercise conditions [5,10,23,26,27,30,54,55]. Therefore, agreement between HRV parameters: calculated based on raw (uncorrected) RR intervals data [10,15,47,56,57] or obtained using HRM software [11,13,24,58] or smartphone apps with or without unknown editing procedure [47,51], and HRV measures derived from (edited) RRi recorded using ECG may not be acceptable. Thus, a preprocessing procedureediting of raw RRi time seriesseems to be crucial and recommended before comparing calculated parameters from different devices. ...
... Then, RRi from mentioned files were imported into one .xlsx spreadsheet file to create graphical presentation of raw RRi data series from both devices and identify artifacts to consequently implement manual data editing and precise synchronization between RRi time series [27,30,56,57,58]. The ECG and HRM raw RRi time series start-points were manually matched before conducting artifacts identification and correction procedures [27]. ...
Full-text available
Wearable devices enable RR interval (RRi) measurements during various conditions. We aimed to assess the validity of the Polar H10 for RRi acquisition during pre-rest stabilization, rest, sympathetic nervous system activity stimulation and recovery conditions for heart rate variability (HRV) analysis in ski mountaineers. RRi were simultaneously obtained via electrocardiogram and the Polar H10 with V800 wrist-watch among eleven elite athletes in the supine position. Short-term (5-min) and ultra-short-term (1-min) heart rate (HR) and HRV parameters were analyzed. Mean absolute percentage difference between parameters from different devices ranged from 0% to 5.4%. Intraclass correlation and concordance correlations ranged between 0.76 and 1.00. Limits of agreement (LoA) for short-term measures did not exceed the defined maximum acceptable difference (SWC) through all conditions for mean RRi, HR, time-domain and nonlinear indices, and for frequency-domain (fast-Fourier-related) during the rest condition. LoA exceeded the SWC for mean, minimal and maximal HR, time-domain and nonlinear parameters from select 1-minute samples in all conditions. The Polar H10 provides RRi that could be used for short-and ultra-short-term HRV analysis from stable resting conditions in elite ski mountaineers. Ultra-short-term parameters from exercise should be analyzed with caution especially from later minutes of activity.
... Heart rate variability (HRV) can also be measured using PPG sensors [20]. Whereas high HRV has been associated with good general health, a low HRV has been associated with mental stress, fatigue, and increased morbidity, and has been linked to increased risk of heart failure (HF) after acute myocardial infarction [20,21]. There is high agreement between PPG and ECG measurements of nocturnal resting HR and HRV (r2 = 0.996 and 0.980, respectively) [22], and prior evidence has also shown that PPG estimated HRV parameters (standard deviation of NN intervals [SDNN] and triangular index) could be useful for patient monitoring [23]. ...
... The use of this technology as a wearable has a few limitations, including motion artifacts, inaccuracies from differences in ambient light, high body mass index, skin moisture, hypovolemic states, and importantly darker skin tone [12,14,24,33,34]. Therefore, further developments focused on minimizing the effect of external factors would improve accuracy and encourage more widespread use [21]. ...
Full-text available
Wearable devices stand to revolutionize the way healthcare is delivered. From consumer devices that provide general health information and screen for medical conditions to medical-grade devices that allow collection of larger datasets that include multiple modalities, wearables have a myriad of potential uses, especially in cardiovascular disorders. In this review, we summarize the underlying technologies employed in these devices and discuss the regulatory and economic aspects of such devices as well as the future implications of their use.
... The WHOOP ® strap (Strap 2.0; WHOOP, Inc., Boston, MA, USA) is a commercially available wearable device that provides continuous physiologic data monitoring and training recommendations based on proprietary scientific research [7]. WHOOP ® uses heart rate variability (HRV), along with resting heart rate (RHR) and sleep patterns to determine readiness for activity [8]. HRV measures the irregularity of heart beat rhythm over time and is considered a low-cost, noninvasive measurement of overall competence of the autonomic nervous system [9]. ...
Full-text available
Background Characterizing normal heart rate variability (HRV) and resting heart rate (RHR) in healthy women over the course of a pregnancy allows for further investigation into disease states, as pregnancy is the ideal time period for these explorations due to known decreases in cardiovascular health. To our knowledge, this is the first study to continuously monitor HRV and RHR using wearable technology in healthy pregnant women. Methods A total of 18 healthy women participated in a prospective cohort study of HRV and RHR while wearing a WHOOP® strap prior to conception, throughout pregnancy, and into postpartum. The study lasted from March 2019 to July 2021; data were analyzed using linear mixed models with splines for non-linear trends. Results Eighteen women were followed for an average of 405.8 days (SD = 153). Minutes of logged daily activity decreased from 28 minutes pre-pregnancy to 14 minutes by third trimester. A steady decrease in daily HRV and increase in daily RHR were generally seen during pregnancy (HRV Est. = − 0.10, P < 0.0001; RHR Est. = 0.05, P < 0.0001). The effect was moderated by activity minutes for both HRV and RHR. However, at 49 days prior to birth there was a reversal of these indices with a steady increase in daily HRV (Est. = 0.38, P < 0.0001) and decrease in daily RHR (Est. = − 0.23, P < 0.0001), regardless of activity level, that continued into the postpartum period. Conclusions In healthy women, there were significant changes to HRV and RHR throughout pregnancy, including a rapid improvement in cardiovascular health prior to birth that was not otherwise known. Physical activity minutes of any type moderated the known negative consequences of pregnancy on cardiovascular health. By establishing normal changes using daily data, future research can now evaluate disease states as well as physical activity interventions during pregnancy and their impact on cardiovascular fitness.
... In general, BP tends to be higher in the morning and lower at night, and there is interindividual variation in daytime BP in response to stress [2]. Our study showed that ratio to baseline values for BP were highest at the start of work, lunchtime, start of afternoon work time, and early afternoon. ...
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The aim of this preliminary study was to measure the systolic BP (SBP) and diastolic BP (DBP) and heart rate (HR) of radiological technologists by WD, and evaluate variation among individuals by worktime, day of the week, job, and workplace. Measurements were obtained using a wristwatch-type WD with optical measurement technology that can measure SBP and DBP every 10 minutes and HR every 30 minutes. SBP, DBP, and HR data obtained at baseline and during work time were combined with the hours of work, day of the week, job, and workplace recorded by the participants in 8 consecutive weeks. We calculated the mean, the ratio to baseline and coefficient of variation [CV(%)] for SBP, DBP, and HR. SBP, DBP, and HR values were significantly higher during work hours than at baseline (p<0.03). The ratio to baseline values ranged from 1.02 to 1.26 for SBP and from 1.07 to 1.30 for DBP. The ratio to baseline for SBP and DBP showed CV(%) of approximately 10% according to the day of the week and over the study period. For HR, ratio to baseline ranged from 0.95 to 1.29. The ratio of mean BP to baseline was >1.2 at the time of starting work, middle and after lunch, and at 14:00. The ratio to baseline of SBP were 1.2 or more for irradiation, equipment accuracy control, registration of patient data, dose verification and conference time, and were also working in CT examination room, treatment planning room, linac room, and the office. CV(%) of BP and HR were generally stable for all workplaces. WD measurements of SBP, DBP, and HR were higher during working hours than at baseline and varied by the individuals, work time, job, and workplace. This method may enable evaluation of unconscious workload in individuals.
... Historically, a major challenge for real-time implementation of such measurement systems is that most of the devices are too cumbersome and intrusive for driving research. Recent years have seen enormous progress in wearable electronic devices [95,96] and remote measurement methods [97] that can measure physiological signals without interfering with driving activities. While many screening and diagnostic tools are available to assess fatigue, each method has benefits and limitations which must be considered relative to the population and specific research questions. ...
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Fatigue can be a significant problem for commercial motor vehicle (CMV) drivers. The lifestyle of a long-haul CMV driver may include long and irregular work hours, inconsistent sleep schedules, poor eating and exercise habits, and mental and physical stress, all contributors to fatigue. Shiftwork is associated with lacking, restricted, and poor-quality sleep and variations in circadian rhythms, all shown to negatively affect driving performance through impaired in judgment and coordination, longer reaction times, and cognitive impairment. Overweight and obesity may be as high as 90% in CMV drivers, and are associated with prevalent comorbidities, including obstructive sleep apnea, hypertension, and cardiovascular and metabolic disorders. As cognitive and motor processing declines with fatigue, driver performance decreases, and the risk of errors, near crashes, and crashes increases. Tools and assessments to determine and quantify the nature, severity, and impact of fatigue and sleep disorders across a variety of environments and populations have been developed and should be critically examined before being employed with CMV drivers. Strategies to mitigate fatigue in CMV operations include addressing the numerous personal, health, and work factors contributing to fatigue and sleepiness. Further research is needed across these areas to better understand implications for roadway safety.
... Fitness trackers, smart watches, and even clothing with bio-physiological sensors were developed to help people gather data for health related aspects such as physical activity, sleep phases, heart rate (HR), cardiac arrhythmias, or to measure stress [2,5]. A rising number of those devices are also used in the research area [6], with an appropriate methodology to verify their validity and suitability in those contexts still missing [7]. ...
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Background Numerous wearables are used in a research context to record cardiac activity although their validity and usability has not been fully investigated. The objectives of this study is the cross-model comparison of data quality at different realistic use cases (cognitive and physical tasks). The recording quality is expressed by the ability to accurately detect the QRS complex, the amount of noise in the data, and the quality of RR intervals. Methods Five ECG devices (eMotion Faros 360°, Hexoskin Hx1, NeXus-10 MKII, Polar RS800 Multi and SOMNOtouch NIBP) were attached and simultaneously tested in 13 participants. Used test conditions included: measurements during rest, treadmill walking/running, and a cognitive 2-back task. Signal quality was assessed by a new local morphological quality parameter morphSQ which is defined as a weighted peak noise-to-signal ratio on percentage scale. The QRS detection performance was evaluated with eplimited on synchronized data by comparison to ground truth annotations. A modification of the Smith-Waterman algorithm has been used to assess the RR interval quality and to classify incorrect beat annotations. Evaluation metrics includes the positive predictive value, false negative rates, and F1 scores for beat detection performance. Results All used devices achieved sufficient signal quality in non-movement conditions. Over all experimental phases, insufficient quality expressed by morphSQ values below 10% was only found in 1.22% of the recorded beats using eMotion Faros 360°whereas the rate was 8.67% with Hexoskin Hx1. Nevertheless, QRS detection performed well across all used devices with positive predictive values between 0.985 and 1.000. False negative rates are ranging between 0.003 and 0.017. eMotion Faros 360°achieved the most stable results among the tested devices with only 5 false positive and 19 misplaced beats across all recordings identified by the Smith-Waterman approach. Conclusion Data quality was assessed by two new approaches: analyzing the noise-to-signal ratio using morphSQ, and RR interval quality using Smith-Waterman. Both methods deliver comparable results. However the Smith-Waterman approach allows the direct comparison of RR intervals without the need for signal synchronization whereas morphSQ can be computed locally.
... Relative to the ECG signal, among all the other physiological parameters, it can be considered one of the most suitable signals in the automotive research domain, since its detection ensures comfort and not excessive invasiveness for the driver if compared, for instance, with EEG measurements. Furthermore, ECG-derived parameters can also be established through cutting-edge technologies, which are now available to a large part of the population (i.e., smart devices) with a good level of reliability [14]. In a recent study, Tjolleng et al., developed a three-level classifier based on artificial neural networks relying on six ECG-derived features extracted in time and frequency domains [15]. ...
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Mental workload (MW) represents the amount of brain resources required to perform concurrent tasks. The evaluation of MW is of paramount importance for Advanced Driver-Assistance Systems, given its correlation with traffic accidents risk. In the present research, two cognitive tests (Digit Span Test—DST and Ray Auditory Verbal Learning Test—RAVLT) were administered to participants while driving in a simulated environment. The tests were chosen to investigate the drivers’ response to predefined levels of cognitive load to categorize the classes of MW. Infrared (IR) thermal imaging concurrently with heart rate variability (HRV) were used to obtain features related to the psychophysiology of the subjects, in order to feed machine learning (ML) classifiers. Six categories of models have been compared basing on unimodal IR/unimodal HRV/multimodal IR + HRV features. The best classifier performances were reached by the multimodal IR + HRV features-based classifiers (DST: accuracy = 73.1%, sensitivity = 0.71, specificity = 0.69; RAVLT: accuracy = 75.0%, average sensitivity = 0.75, average specificity = 0.87). The unimodal IR features based classifiers revealed high performances as well (DST: accuracy = 73.1%, sensitivity = 0.73, specificity = 0.73; RAVLT: accuracy = 71.1%, average sensitivity = 0.71, average specificity = 0.85). These results demonstrated the possibility to assess drivers’ MW levels with high accuracy, also using a completely non-contact and non-invasive technique alone, representing a key advancement with respect to the state of the art in traffic accident prevention.
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Continuous measurement of heart rate variability (HRV) in the short and ultra-short-term using wearable devices allows monitoring of physiological status and prevention of diseases. This study aims to evaluate the agreement of HRV features between a commercial device (Bora Band, Biosency) measuring photoplethysmography (PPG) and reference electrocardiography (ECG) and to assess the validity of ultra-short-term HRV as a surrogate for short-term HRV features. PPG and ECG recordings were acquired from 5 healthy subjects over 18 nights in total. HRV features include time-domain, frequency-domain, nonlinear, and visibility graph features and are extracted from 5 min 30 s and 1 min 30 s duration PPG recordings. The extracted features are compared with reference features of 5 min 30 s duration ECG recordings using repeated-measures correlation, Bland–Altman plots with 95% limits of agreements, Cliff’s delta, and an equivalence test. Results showed agreement between PPG recordings and ECG reference recordings for 37 out of 48 HRV features in short-term durations. Sixteen of the forty-eight HRV features were valid and retained very strong correlations, negligible to small bias, with statistical equivalence in the ultra-short recordings (1 min 30 s). The current study concludes that the Bora Band provides valid and reliable measurement of HRV features in short and ultra-short duration recordings.
Chronic pain is devastating and its measurement is elusive, making intervention difficult for sufferers and practitioners alike. The present study explores the possibility of tracking fluctuations in pain over time with wearable sensors of heart-based metrics. We measured mean heart rate and heart rate variability with a wrist-worn sensor in persons with and without chronic pain for one month. Mixed model regressions were used to test the associations, revealing that heart rate measured at night was predictive of pain intensity on the next day in participants with chronic pain (p < .05), but not in healthy controls (p > .05). The results suggest that cardiac biometrics can predict pain intensity in primary chronic pain conditions.
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Background: We aimed to compare and examine the effect of aquatic interventions, Watsu® vs Immersion, on the autonomic nervous system and the range of motion in children with cerebral palsy, due to common belief that use of Watsu is beneficial for the special needs. Material and methods: Twenty-three children (age 7.5±2.8) were randomized to receive Watsu® therapy and Immersion interventions in the cross-over, age-stratified study. Each therapy session lasted 30 minutes twice a week for a total of 10 weeks in two non-consecutive periods. Short-term heart rate variability parameters by using a Polar H7 heart rate sensor with a signal processing software and the passive range of motion by using a universal goniometer was measured at baseline and post-treatment. Results: Watsu® therapy significantly improved the heart rate variability parameter (pNN50, t = 2.312, p = 0.031) and lower flexibility (t = 6.012, p = 0.000) in comparison to immersion. Conclusions: In comparison to immersion, Watsu® therapy was shown to be safe and effective for the autonomic modulation and flexibility of children with cerebral palsy. Therefore, it is recommended as a complementary tool for physical therapy on land.
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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%).