Informatica 42 (2018) 33–42 33
Continuous Blood Pressure Estimation from PPG Signal
Gašper Slapničar and Mitja Luštrek
Joef Stefan Institute, Jamova cesta 39, 1000 Ljubljana
E-mail: gasper.slapnicar@ijs.si, mitja.lustrek@ijs.si
Matej Marinko
Faculty of Mathematics and Physics, Jadranska cesta 19, 1000 Ljubljana
E-mail: matejmarinko123@gmail.com
Keywords: photoplethysmography, blood pressure estimation, regression analysis, m-health
Received: November 11, 2017
Given the importance of blood pressure (BP) as a direct indicator of hypertension, regular monitoring is
encouraged for healthy people and mandatory for patients at risk from cardiovascular diseases. We propose
a system in which photoplethysmogram (PPG) is used to continuously estimate BP. A PPG sensor can be
easily embedded in a modern wearable device, which can be used in such an approach. The PPG signal is
first preprocessed in order to remove major noise and movement artefacts present in the signal. A set of
features describing the PPG signal on a per-cycle basis is then computed to be used in regression models.
The predictive performance of the models is improved by first using the RReliefF algorithm to select a
subset of relevant features. Afterwards, personalization of the models is considered to further improve the
performance. The approach is validated using two distinct datasets, one from a hospital environment and
the other collected during every-day activities. Using the MIMIC hospital dataset, the best achieved mean
absolute errors (MAE) in a leave-one-subject-out (LOSO) experiment were 4.47 mmHg for systolic and
2.02 mmHg for diastolic BP, at maximum personalization. For everyday-life dataset, the lowest errors in
the same LOSO experiment were 8.57 mmHg for systolic and 4.42 mmHg for diastolic BP, again using
maximum personalization. The best performing algorithm was an ensemble of regression trees.
Povzetek: Krvni tlak je neposreden pokazatelj hipertenzije. Razvili smo sistem, ki krvni tlak ocenjuje
iz fotopletizmograma (PPG), kakršen je že vgrajen v večino modernih senzorskih zapestnic. Signal PPG
smo sprva predprocesirali in segmentirali na cikle. Predprocesiranje odpravi večino šuma, ki se pogosto
pojavlja zaradi gibanja. Iz očiščenega signala smo nato izračunali množico značilk, ki smo jih uporabili
v regresijskih modelih. Sistem smo izboljšali z uporabo algoritma RReliefF za izbor relevantnih značilk
in z uporabo dela podatkov vsake osebe za učenje personaliziranih napovednih modelov. Sistem smo
vrednotili na dveh podatkovnih množicah, eni iz kliničnega okolja in drugi zbrani med rutinskimi dnevnimi
aktivnostmi posameznikov. V poizkusu smo model vsakič naučili na vseh osebah razen eni in ga nato
testirali na izpuščeni osebi. Z uporabo klinične podatkovne množice smo v omenjenem poizkusu dosegli
najnižji povprečni absolutni napaki (MAE) 4.47 mmHg za sistolični in 2.02 mmHg za diastolični krvni
tlak, pri največji stopnji personalizacije. Za množico, zbrano med dnevnimi aktivnostmi, smo dosegli
najnižji napaki 8.57 mmHg za sistolični in 4.42 mmHg za diastolični krvni tlak, ponovno pri največji
stopnji personalizacije. Najbolje se je obnesel ansambel regresijskih dreves.
1 Introduction
World Health Organization (WHO) listed cardiovascular
diseases as the most common cause of death in 2015, re-
sponsible for almost 15 million deaths combined [1]. Hy-
pertension is one of the most common precursors of such
diseases and can be easily detected with regular blood pres-
sure (BP) monitoring, which is especially critical for pa-
tients already suffering from hypertension or related cardi-
ovascular diseases, as it can indicate potential vital threats
to their health.
While regular BP monitoring is important, it is also trou-
blesome, as devices using inflatable cuffs are still consi-
dered the “golden standard”. The cuff placement is cri-
tical, as the sensor must be located directly above the
main artery in the upper arm area, at approximately heart
height [4]. These requirements impose relatively strict mo-
vement restrictions on the subject and require substantial
time commitment, thus causing low subject adherence to
regular monitoring. Furthermore, when done by the subject
him/herself in a home environment, this process can cause
stress, which in turn influences the BP values, making the
measurements less reliable. This problem is usually not al-
leviated by having the medical personnel perform the me-
asurement, as this can again cause anxiety in the subject,
commonly known as the “white coat syndrome”.
34 Informatica 42 (2018) 33–42 G. Slapničar et al.
Our work focuses on photoplethysmogram (PPG) analy-
sis and the development of a robust non-obtrusive method
for continuous BP estimation. It will be implemented and
used in an m-health system based on a wristband with an
embedded PPG sensor. This will allow the user to wear
the device without any interference or limits imposed upon
their daily routine, allowing for truly continuous measuring
without stressing the user and thus potentially influencing
the BP values.
The rest of the paper is organized as follows. Section
2 gives a brief overview of the related work. Section 3
explains the methodology we have used, focusing on sig-
nal pre-processing and machine learning features. Section
4 elaborates on the experimental setup and results, and
Section 5 concludes with a summary and plans for future
work.
2 Related work
Photoplethysmography is a relatively simple technique ba-
sed on inexpensive technology, which is becoming incre-
asingly popular in wearable devices for heart rate estima-
tion. It is based on the illumination of the skin and mea-
surement of changes in its light absorption [5]. In its basic
form it only requires a light source to illuminate the skin
(typically a light-emitting diode – LED light) and a photo-
detector (photodiode) to measure the amount of light either
transmitted through, or reflected from the skin. Thus PPG
can be measured in either transmission or reflectance mode.
Both modes of operation are shown in Figure 1.
Figure 1: Transmission and reflectance mode in which the
PPG signal can be obtained. LED is the light source while
PD is the photodetector [6].
With each cardiac cycle, the heart pumps blood towards
the periphery of the body. This produces a periodic change
in the amount of light that is either absorbed or reflected
from the skin to the photodetector, as the tissue changes its
tone based on the amount of blood in it.
Exploring the recent applications of PPG, we can see that
it is becoming more widely used in BP estimation. One of
two common approaches are typically used:
1. BP estimation using two sensors (PPG + Electrocardi-
ogram (ECG))
2. BP estimation using the PPG sensor only
The first approach requires the use of two sensors, typi-
cally an ECG and a PPG sensor, in order to measure the
time it takes for a single heart pulse to travel from the he-
art to a peripheral point in the body. This time is com-
monly known as pulse transit time (PTT) or pulse arrival
time (PAT), and its correlation with BP changes is well es-
tablished.
The more recent approach is focused on the PPG signal
only; however, the relationship between the PPG and BP is
only postulated and not as well established as the relations-
hip between the PTT and BP. This approach is, however,
notably less obtrusive, especially since PPG sensors have
recently become very common in most modern wristbands.
BP is commonly measured in millimeters of mercury
(mmHg), which is a manometric unit routinely used in me-
dicine and many other scientific fields. A mercury mano-
meter is a curved tube containing mercury, which is closed
at one end while pressure is applied on the other end. 1
mmHg of pressure means that the pressure is large enough
to increase the height of the mercury in the tube for 1 mm.
To put the values discussed in this paper into perspective,
the normal healthy adult BP is considered to be around 120
mmHg (16 kPa) for systolic and 80 mmHg (11 kPa) for
diastolic BP [2].
One of the earliest PPG-only attempts was conducted by
Teng et. al. in 2003 [3]. The relationship between the arte-
rial BP (ABP) and certain features of the PPG signals was
analyzed. Data were obtained from 15 young healthy sub-
jects in a highly controlled laboratory environment, ensu-
ring constant temperature, no movement and silence. The
mean differences between the linear regression estimations
and the measured BP were 0.21 mmHg for systolic (SBP)
and 0.02 mmHg for diastolic BP (DBP). The corresponding
standard deviations were 7.32 mmHg for SBP and 4.39
mmHg. Using mean errors instead of mean absolute errors
as the evaluation metric is questionable, since it does not
reflect the actual performance of the derived model and the
error can be extremely low, even if the actual predictions
are high above and under the actual observed BP values.
A paper was published in 2013 in which the authors used
data from the Multiparameter Intelligent Monitoring in In-
tensive Care (MIMIC) waveform database [7, 8] to extract
21 time domain features and use them as an input vector for
artificial neural networks (ANNs) [9]. The results are not
quite as good as with the linear regression model descri-
bed earlier; however, the data was obtained from a higher
number and variety of patients in a less controlled environ-
ment. Mean absolute errors of less than 5 mmHg for both
SBP and DBP were reported. While the environment was
less controlled compared to the previous work, the patients
were still within a hospital setting and hospital equipment
was used for data collection. Furthermore, only an undis-
closed subset of all the available data from MIMIC was
used.
Another research was conducted in 2013 in which the
authors used a smartphone camera to capture the PPG sig-
nal using the camera flash as the light source and the phone
camera as the photodiode [10]. PPG features were again
extracted and fed to a neural network, which estimated SBP
Continuous Blood Pressure Estimation from. . . Informatica 42 (2018) 33–42 35
and DBP. All the data processing and BP evaluation was
done in a cloud in order to reduce the computational burden
on the device. It is not clear how many subjects participa-
ted in the experiment, however, they reported the maximum
error not exceeding 12 mmHg. The error metric is not ex-
plained in detail, however, based on the given results table,
we can presume that MAE was used. Such a method re-
quires some user effort, as the user must place and hold his
finger over the camera and LED light. This prevents any
other activities during this time.
It is clear that the PPG-only approach has potential, ho-
wever, a robust unobtrusive method that works well on a
general case is yet to be developed.
3 Methodology
The proposed system consists of two main modules, na-
mely the signal pre-processing and machine learning mo-
dule. The former is responsible for cleaning the PPG signal
of most noise and then segmenting it into cycles, where one
PPG cycle corresponds to a single heart beat. The latter ex-
tracts features describing the PPG signal on a per-cycle ba-
sis, selects a subset of relevant features using the RReliefF
algorithm [12], and finally feeds the subset into regression
algorithms, which build the prediction models.
3.1 Signal pre-processing
PPG sensors must be very sensitive in order to detect tiny
variations in light absorption of the tissue. This also ma-
kes them highly susceptible to movement artefacts. This
problem is especially obvious when dealing with PPG col-
lected via a wristband, as the contact between the sensor
and the skin can be compromised during arm movements.
This is partially alleviated by using green light, which is
less prone to artefacts, however, major artefacts often re-
main in the signal. Subsequently, substantial effort is di-
rected towards PPG pre-processing.
3.1.1 Cleaning based on established medical criteria
In the first phase, both the BP and PPG signal are roughly
cleaned based on established medical criteria [13]. A 5-
second sliding window is used to detect segments with ex-
treme BP values or extreme changes of the BP in a short
time period. Thresholds for extreme values and changes
are selected based on established medical criteria in related
work [13] and are given in Table 1. Some thresholds were
slightly modified, since the criteria given in the referenced
paper seem too strict for some subjects encountered in our
datasets. We have thus loosened the criteria in accordance
with empirical observations in our datasets (e.g., the ori-
ginal criteria excludes all data with SBP > 180, while we
observed some segments with SBP over 180 mmHg).
After the cleaning of the clinical dataset, 85% of data
is kept on average, while 15% is discarded. This is very
subject dependent, as for some subjects nearly all the data
Criterion Threshold
SBP > 250 or < 80
DBP > 150 or < 40
SBP – DBP < 20
∆SBP or ∆DBP in 5 sec > 50
Table 1: Established medical criteria and thresholds for
rough signal cleaning. ∆ signifies a change in BP value.
5-second segments meeting any of these criteria are remo-
ved from the signal.
is removed (e.g., sensor anomaly which shows 0 ABP al-
most all the time), while for majority of subject most of the
data is kept. For everyday-life dataset, which contains a lot
more noise, only 40% of data is kept, while 60% is discar-
ded. This is the result of some subjects having long noisy
segments of the PPG signal. It should be noted, that these
percentages are also subject of the parameters for trade-off
between the required quality and the amount of signal kept,
which are discussed in 3.1.3.
3.1.2 Peak and cycle detection
In order to do further cleaning and subsequent feature ex-
traction, PPG cycle detection is mandatory. This is not tri-
vial, as substantial noise in the PPG signal poses a signifi-
cant problem, as mentioned earlier.
This problem was tackled in several steps. First, a filte-
ring transformation, which enhances the systolic upslopes
of the pulses in the PPG signal, is used. It is designed to
use the derivative of the PPG signal at lower frequencies,
in order to detect the abrupt upslopes of the systolic pulse
compared to the diastolic or dicrotic pulse in the PPG sig-
nal. This is based on a low-pass differentiator (LPD) filter,
which removes high frequency components and performs
differentiation. Once the steepest points in the PPG signal
are located, the following peak is chosen as the PPG sys-
tolic peak. Afterwards, a time-varying threshold for peak
detection is applied, which ensures that potential double
peaks or diastolic peaks close to the systolic ones are not
chosen. The procedure is explained in detail in a paper by
Lzaro et al. [14].
After the peaks are detected, finding the cycle start-end
points is simpler, as the dominant valleys between the de-
tected peaks must be found. An example of detected peaks
and cycle locations using the described method is shown in
Figure 2.
3.1.3 Cleaning based on ideal templates
After cycles are successfully detected, the second cleaning
phase begins. A 30-second sliding window is used.
First, the most likely length of a cycle L in the current
window is determined using autocorrelation analysis. A
copy of the PPG signal in the current window is taken and
36 Informatica 42 (2018) 33–42 G. Slapničar et al.
Figure 2: The upper subplot shows a PPG segment. Lower
subplot shows the LPD filtering transformation of the same
PPG segment. Peaks of the transformation in the lower
subplot correspond to the steepest systolic upslopes of the
PPG in the upper subplot, and are denoted as nA. Actual
detected PPG peaks are denoted as n∗A.
shifted sample by sample up to a certain length that con-
tains at least two heart beats. When the copy is shifted by
the number of samples corresponding to exactly one cycle,
the autocorrelation reaches its first peak, and this number
of samples is chosen as L.
Presuming that the majority of cycles within a 30-second
window are not morphologically altered, we can create an
“ideal cycle template” for this window. Such a template is
created by always taking the next L samples from each cy-
cle starting point and then computing the mean cycle. Each
individual cycle is then compared to the computed template
and its quality is evaluated using three signal quality indi-
ces (SQIs), which are defined as follows [15]:
1. SQI1: First L samples of each cycle are taken and then
each cycle is directly compared to the template using
a correlation coefficient.
2. SQI2: Each cycle is interpolated to length L and then
the correlation coefficient with the template is compu-
ted.
3. SQI3: The distance between each cycle and the
template is computed using dynamic time warping
(DTW).
Finally the thresholds for each SQI are empirically deter-
mined. Each cycles’ SQIs are evaluated and if they reach
the required quality threshold, that cycle is kept, otherwise
it is removed. If more than half the cycles in the current
30-second window are under the thresholds, the whole win-
dow is discarded as too noisy. An example of this cleaning
is shown in Figure 3.
Once the PPG signal is cleaned and only high-quality
cycles with minimal morphological anomalies remain, fea-
tures can be extracted from each cycle.
3.2 Machine learning
In order to derive the relationship between the PPG and BP,
features describing the PPG signal were computed and then
the relevant subset of these features was selected to be used
in the regression algorithms.
3.2.1 Features
In accordance with the related work [3, 9, 10], several time-
domain features were computed from the PPG signal, and
the set of features was further expanded with some from the
frequency [13] and complexity-analysis domains. Most fe-
atures focus on describing the morphology of a given PPG
cycle, as shown in Figure 4.
Feature Description
Tc Cycle duration
Ts Time from start of cycle to systolic peak
Td Time from systolic peak to end of cycle
Tnt Time from systolic peak to diastolic rise
Ttn Time from diastolic rise to end of cycle
S1 Area under the curve (AUC) from start of
cycle to max upslope point
S2 AUC from max upslope point to systolic
peak
S3 AUC from systolic peak to diastolic rise
S4 AUC from diastolic rise to end of cycle
AUC syst S1 + S2
AAC syst Area above the curve (AAC) from start of
cycle to systolic peak
AUC diast S3 + S4
AAC diast AAC from systolic peak to end of cycle
Table 2: Elaborations of some of the used features shown
in Figure 4.
In addition to the features focusing on the PPG cycle
morphology, which were highlighted thus far, the following
features were computed and considered:
1. AI – Augmentation Index: a measure of wave re-
flection on arteries.
AI =
diastolic rise amplitude
systolic peak amplitude
2. LASI – Large Artery Stiffness Index: an indicator of
arterial stiffness, which is denoted as Tnt in Figure 4
and Table 2.
3. Complexity analysis: signal complexity and mobility
are computed for the 30-second PPG segment contai-
ning the current cycle. Mobility represents an estimate
of the mean frequency and is proportional to the stan-
dard deviation of the power spectrum. Complexity gi-
ves an estimate of change in frequency by comparing
the signal similarity to a pure sine wave. They are
Continuous Blood Pressure Estimation from. . . Informatica 42 (2018) 33–42 37
Figure 3: Example of the cleaning algorithm in the second phase of the signal pre-processing. Comparing the top (uncle-
aned) and bottom (cleaned) PPG signal, we see that the obvious artefact segments are removed.
given by Najarian and Splinter [11] as follows (presu-
ming a zero-mean signal):
S0 =
√∑N
i=1 x
2
i
N
,
S1 =
√∑N−1
j=2 d
2
j
N − 1
,
S2 =
√∑N−2
k=3 g
2
k
N − 2
,
where x is the PPG signal, d is the first order derivative
of x and g is the second order derivative of x.
Mobility =
√
var(d)
var(x)
=
S1
S0
Complexity =
mobility(d)
mobility(x)
=
√
S22
S21
− S
2
1
S20
,
4. FFT features: amplitudes and phases of the
frequency-domain representation of the 30 second
PPG segment containing the current cycle. The length
of the window was chosen such that it contains enough
cycles (expected 1 cycle per second) for the frequen-
cies in the segment to be reliably determined.
Considering all the time and frequency-domain featu-
res along with the complexity-analysis features, and the
amount of instances (cycles) available, we are often dea-
ling with a very large matrix of training data. The number
of rows (instances) is on the order of magnitude 105 and
the number of columns (features) is on the order of magni-
tude 102, thus dimensionality reduction through selection
of a subset of relevant features is feasible, but not manda-
tory. More importantly, feature selection allows us to de-
termine which features are useful for the learning process,
and which are irrelevant, allowing us to obtain a smaller
subset containing only the relevant features.
3.2.2 Feature selection
The RReliefF algorithm was chosen for feature selection.
It is a modification of the ReliefF algorithm, suitable for
regression problems with continuous target variables. The
algorithm was applied to a subset of 10% of all data cho-
sen randomly. This was repeated 10 times. All the features
with non-zero relevance, as chosen by the algorithm, were
considered in each iteration and their importance was sa-
ved. Looking at the final scores of the algorithm across all
the iterations, we notice that quite a few features are con-
sidered irrelevant, while the same features are commonly
chosen as important for both SBP and DBP, as shown in
Figure 5. Noting the fact that the same features were se-
lected in each of the 10 iterations, we can assume that the
relevant features are not dependent on the selected subset
of the available data.
As mentioned, all the features with non-zero importance
were taken, as more than half were discarded as irrelevant
by the RReliefF algorithm. Among the non-zero impor-
tance features, some features from each of the groups men-
tioned earlier (temporal, frequency and complexity analy-
sis) are present. Most area-based features were marked
as irrelevant, while certain times (Tc, Ts and Td), both
complexity-analysis (signal complexity and signal mobi-
lity) as well as some frequency-domain (amplitudes and
phases at low frequencies) features were marked as impor-
tant. These non-zero importance features were then used in
the regression algorithms.
The relevant features were determined using the larger
and more varied dataset from the MIMIC database. The
same subset of features was also used with the smaller
everyday-life dataset. Both datasets are described in more
detail in the following section.
Since the feature selection procedure only slightly im-
proved the results, we have not considered experiments
with other or additional feature selection methods.
38 Informatica 42 (2018) 33–42 G. Slapničar et al.
Figure 4: Time and area based features that describe the morphology of the PPG signal on a per-cycle basis. The features
are listed and elaborated in Table 2.
4 Experiments and results
In an effort to make the proposed method as general as pos-
sible, two datasets were considered for the experimental
evaluation. The data from all subjects, which met the requi-
rements of having both the PPG and BP signals recorded,
were always used in the experiments.
4.1 Data
The first dataset is from the publicly accessible MIMIC da-
tabase, which is commonly used for experiments and com-
petitions in the signal processing field. The original version
contains data from 72 hospitalized patients. All patients
with both the PPG and BP signal were originally conside-
red, however, after the filtering and pre-processing, only
41 patients had enough high-quality data remaining to be
used in the experiments. All the data was collected in a
hospital environment using hospital measuring equipment,
including an ABP measuring device. The ABP is measu-
red by inserting a cathether in an artery, making it highly
invasive, however, it offers the most precise BP monitoring.
The second dataset was collected at Jožef Stefan Insti-
tute (JSI) using the Empatica E4 wristband for the PPG
and a digital cuff-based Omron BP monitoring device for
the ground truth BP, as is common in such experimental
settings in related work. This device is reported to be cli-
nically validated according to the British Hypertension So-
ciety and the Association for the Advancement of Medi-
cal Instrumentation (AAMI) protocols [17], which means
that the mean errors do not exceede 5 mmHg. The col-
lection procedure at JSI was conducted in accordance with
the standardized clinical protocol. The correct placement
of the cuff on the upper arm area with the sensor above the
main artery was ensured. The measurements were done in
an upright sitting position, making sure the cuff was located
at approximately heart height. The recommended protocol
was followed as best as possible, however, in an ideal si-
tuation the ground truth BP should be measured as ABP
within an artery. Due to the invasive nature of ABP measu-
rement, this is not feasible in an everyday-life situation, so
the digital cuff-based monitor was used as a good replace-
ment. An upper-arm cuff-based monitor was chosen over a
wrist-based one, as the latter is less accurate and extremely
sensitive to body position.
In the first completed phase of the data collection, 8 he-
althy subjects were considered, 5 male and 3 female. Each
subject wore the wristband PPG measuring device for se-
veral hours during their everyday activities. They measu-
red their BP every 30 minutes or more often. Finally, only
parts of the PPG signal 3 minutes before and after each
BP measurement point were taken into consideration, as
the measured BP value is only relevant for a short time.
Ideally, the BP would be measured more often, however,
this would place further stress on the subjects and was not
possible during their everyday routine. Furthermore, addi-
tional physiological variations (e.g., breathing rate) could
be obtained from the PPG and used for the BP estimation,
however, this was not yet considered but might be a subject
of future work.
Continuous Blood Pressure Estimation from. . . Informatica 42 (2018) 33–42 39
Figure 5: The output of RReliefF algorithm, which shows
the feature importance for each of the considered features.
4.2 Experimental setup
Two experimental setups were considered, 5-fold cross va-
lidation and LOSO. The purpose of the first was to establish
initial observations about the selected features and perfor-
mance of different regression algorithms. The second ex-
periment was conducted to evaluate the generalization per-
formance of the algorithms and subsequently determine po-
tential requirement for personalization.
4.2.1 K-fold cross validation
The MIMIC dataset consisted of roughly 200 000 instances
post filtering, which correspond to 41 patients. The instan-
ces were obtained by uniformly taking 20 3-minute seg-
ments from the whole recording for a given patient. Each
instance (cycle) in a given 3-minute segment was assigned
the mean SBP and mean DBP of this segment. This simu-
lates the patients measuring their BP periodically, but not
more than once in 3 minutes.
K-fold cross validation (k = 5) was conducted, where in-
stances were first shuffled randomly and then all the data
was split into nearly equal folds. Then k – 1 folds were
taken for learning and the remaining fold was used for tes-
ting. This was repeated k times. The random shuffling of
instances makes it so that instances belonging to a given
subject might appear in both training and testing sets. This
was taken into account (a sort of implicit personalization),
as this experiment was merely a starting point to determine
the initial performance of the algorithms and was later com-
plemented by a Leave-one-subject-out experiment.
Several regression algorithms were compared using the
full set of features. The algorithm that performed best using
all the features was additionally evaluated using only the
subset of best features as selected by the RReliefF algo-
rithm. The predictive performance of these options in 5-
fold cross validation is discussed in detail in the Results
section.
4.2.2 Leave-one-subject-out
Due to increased computational complexity of a leave-
one-subject-out experiment compared to k-fold cross-
validation, data was additionally sub-sampled, by taking
500 uniformly selected cycles from each patient’s data.
During the initial attempt, a regression model was trai-
ned in each iteration on all the subjects, except the one
left out. It was ensured that no instances from the testing
subject appeared in the training set. This yielded poor re-
sults. Notable improvements can be made by using a small
amount of each patient’s data for training, most likely due
to each patient having a subtly unique cardiovascular dyn-
amic and relation between PPG and BP. This was additio-
nally confirmed by doing cycle morphology analysis, du-
ring which it was established that similar cycle shapes do
not necessarily signify similar BP values. Due to the men-
tioned factors, personalization of the trained models was
considered in an attempt to improve the predictive perfor-
mance of the models.
In the second attempt, the regression models were again
trained using all the subjects except the one left out. This
time, however, the models were further personalized by
using some instances from the left out subject. The instan-
ces of the left out subject were grouped by their BP values.
These groups were then sorted from lowest to highest BP.
Afterwards, every n-th group (n = 2, 3, 4, 5, 6) of instances
was taken from the testing data and used in training in order
to personalize the model to the current patient. This ensu-
res personalization with different BP values, as taking just
a single group of instances gives little information, since
the BP will be constant within this group. Given the fact
that the MIMIC data consists of roughly 5x the number of
patients compared to everyday-life data, the personaliza-
tion data for it was multiplied 5 times, making it noticeable
within the large amount of training data from the remaining
patients.
During both attempts, several regression algorithms
were once again considered, as given in Tables 3 and 4. The
MAE was used as the evaluation metric. All models were
compared with a dummy regressor, which always predicted
the mean BP value of the same combination of general and
personalization data as the other models used for training.
Finally, the regressor with the lowest MAE was chosen as
40 Informatica 42 (2018) 33–42 G. Slapničar et al.
best.
For successful personalization, the user should measure
their PPG continuously and also make a few periodic mea-
surements of their BP using a reliable commercial device.
This allows the model to personalize to the user, learning
from a small sample of their labeled data, thus improving
its predictive performance.
4.3 Results
Using the personalization approach, notable improvements
have been made over the dummy regressor in both experi-
ments. The results are discussed in detail in the following
sections.
4.3.1 K-fold cross validation results
MAE with corresponding standard deviations in the 5-fold
cross validation experiment for the MIMIC data are given
in Table 3, while the results for the everyday-life data are
given in Table 4.
Algorithm MAESBP [mmHg]
Dummy (predicts mean) 19.70 ± 16.07
Linear regression 18.47 ± 15.91
Ensemble (all feat.) 5.83 ± 7.74
Ensemble (relevant feat.) 4.90 ± 6.59
Algorithm MAEDBP [mmHg]
Dummy (predicts mean) 8.73 ± 6.77
Linear regression 8.14 ± 7.98
Ensemble (all feat.) 2.92 ± 4.09
Ensemble (relevant feat.) 2.21 ± 3.70
Table 3: MAE of different algorithms for SBP and DBP
estimation in 5-fold cross validation using the MIMIC hos-
pital dataset.
Algorithm MAESBP [mmHg]
Dummy (predicts mean) 11.46 ± 7.51
Linear regression 11.21 ± 8.00
Ensemble (all feat.) 9.12 ± 7.90
Ensemble (relevant feat.) 7.87 ± 7.47
Algorithm MAEDBP [mmHg]
Dummy (predicts mean) 5.01 ± 3.99
Linear regression 5.01 ± 8.00
Ensemble (all feat.) 4.38 ± 3.74
Ensemble (relevant feat.) 3.84 ± 3.63
Table 4: Mean absolute errors of different algorithms for
SBP and DBP estimation in 5-fold cross validation using
the JSI-collected everyday-life dataset.
Ensemble of shallow regression trees has shown the best
predictive performance in the 5-fold cross validation for
both SBP and DBP using both datasets. We also notice
a slightly better performance when only the relevant fea-
tures, as given by RReliefF, are used in comparison to the
default feature set.
As the ensemble of regression trees has shown the best
performance, its hyperparameters were optimized using
Bayesian optimization. All the available hyperparameters
were optimized using the MATLAB built-in Bayesian Op-
timization Workflow [16]. It optimizes both the hyperpara-
meters of the ensemble as well as the hyperparameters of
the weak learners, which are chosen to be shallow Regres-
sion Trees. The optimization is ran for 30 iterations, trying
to minimize the objective cross-validation loss function.
Bootstrap aggregation was chosen as superior over gradient
boosting strategy, and the optimal number of weak learners
was determined to be 77. The maximum number of splits
in the weak learner was determined to be 1, meaning that
the regression trees are in fact regression stumps.
4.3.2 Leave-one-subject-out results
Figure 6: MAE for SBP and DBP for the MIMIC dataset,
at different amounts of personalization.
Continuous Blood Pressure Estimation from. . . Informatica 42 (2018) 33–42 41
The lowest error using the MIMIC data was again achie-
ved using the hyperparameter tuned Ensemble of regres-
sion trees algorithm with RReliefF selected subset of fea-
tures. The highest amount of personalization (50%) gave
the best results. 50% personalization corresponds to 10 BP
measurements conducted by the subject, given the fact that
20 segments with 20 different BP values were taken. Obtai-
ning 10 BP measurements by the subject, in order to per-
sonalize the model, seems like a reasonable requirement.
Figure 7: MAE for SBP and DBP for the everyday-life da-
taset, at different amounts of personalization.
The JSI-collected everyday-life data has proven to be
more problematic, as there were only a few different BP
values recorded in the first phase of data collection. Furt-
hermore, due to the high amount of movement artefacts,
a lot of data was removed by the cleaning algorithm, lea-
ving a very small amount of usable data with a very low
variation in BP. This further enhanced the performance of
the dummy regressor, which achieved much lower MAE
compared to the MIMIC dataset, however, improvements
were again achieved by using personalization, as shown in
Figure 7.
5 Conclusion
We have developed a system for BP estimation using only
the PPG signal, and have evaluated its performance on two
distinct datasets using two experimental setups.
The first module of the system deals with signal pre-
processing, removing most movement artefacts and ano-
malies from the PPG signal. It then detects PPG cycles
corresponding to heart beats and feeds them to the second
module, which computes a number of features describing
each cycle. This is followed by feature subset selection
using the RReliefF algorithm and finally the features are
fed into several regression algorithms. Predictive models
were created and evaluated on a hospital MIMIC dataset as
well as an everyday-life dataset collected at JSI. The lowest
MAE achieved for the MIMIC hospital dataset in 5-fold
cross validation were 4.90± 6.59 mmHg for SBP and 2.21
± 3.70 mmHg for DBP. The best performing algorithm was
an Ensemble of shallow regression trees. Its hyperparame-
ters were optimized using Bayesian optimization. Finally,
the same models were evaluated on the same dataset using
the leave-one-subject-out validation, achieving the lowest
MAE of 4.47 ± 5.85 mmHg for SBP and 2.02 ± 2.94
mmHg for DBP, again using the same hyperparameter-
tuned Ensemble and the subset of features selected by the
RReliefF algorithm. These results were achieved using
the maximum, 50% personalization. Similar trends can be
observed for the everyday-life JSI-collected dataset. The
lowest MAE in 5-fold cross validation were 7.87 ± 7.47
mmHg for SBP and 3.84 ± 3.63 mmHg for DBP. Ensem-
ble of shallow regression trees with optimized parameters
prevailed again. In LOSO validation, the lowest MAE of
of 8.57 ± 7.93 mmHg for SBP and 4.42 ± 3.61 mmHg for
DBP were achieved.
5.1 Interpretation of results
Comparing the results of the 5-fold cross-validation to
those of the LOSO evaluation, we first notice, that the best
performing algorithm is the same. In each fold in the 5-
fold cross validation, 80% of randomly shuffled instances
were taken for training, which translates to 80% persona-
lization for each subject. This is the reason behind the lo-
wer MAE in the 5-fold cross-validation, however, similar
MAE was also achieved with higher amounts of persona-
lization in the LOSO experiment. The developed system
shows promising results and could be used by both regu-
lar people and hypertensive patients during their everyday
routine, by wearing an unobtrusive wristband. It could in-
form them of their current medical condition regarding BP.
Further testing with more field-collected data is required
to more accurately determine its performance, however, it
already achieves low MAE when personalization is consi-
dered.
42 Informatica 42 (2018) 33–42 G. Slapničar et al.
5.2 Future work
We plan to expand our data collection experiment at JSI,
which will give us more data and more variety within the
collected BP data. Once enough data is collected, we plan
to upgrade the machine learning part of our pipeline using
deep-learning algorithms. These are well-suited for pro-
blems dealing with signal analysis and represent the state
of the art approach in signal processing in recent years, ma-
king them a suitable candidate for our domain.
Acknowledgement
The HeartMan project has received funding from the Eu-
ropean Unions Horizon 2020 research and innovation pro-
gramme under grant agreement No 689660. Project part-
ners are Jožef Stefan Institute, Sapienza University, Ghent
University, National Research Council, ATOS Spain SA,
SenLab, KU Leuven, MEGA Electronics Ltd and European
Heart Network.
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