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Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model


Abstract and Figures

Conventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.
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Optical and Quantum Electronics (2021) 53:93
1 3
Non‑invasive cuff‑less blood pressure estimation using
ahybrid deep learning model
SenYang1· YapingZhang1· Siu‑YeungCho1· RicardoCorreia2· StephenP.Morgan2
Received: 8 July 2020 / Accepted: 14 December 2020 / Published online: 25 January 2021
© The Author(s) 2021
Conventional blood pressure (BP) measurement methods have different drawbacks such as
being invasive, cuff-based or requiring manual operations. There is significant interest in
the development of non-invasive, cuff-less and continual BP measurement based on physi-
ological measurement. However, in these methods, extracting features from signals is chal-
lenging in the presence of noise or signal distortion. When using machine learning, errors
in feature extraction result in errors in BP estimation, therefore, this study explores the use
of raw signals as a direct input to a deep learning model. To enable comparison with the
traditional machine learning models which use features from the photoplethysmogram and
electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical
characteristics (age, height, weight and gender) is developed. This hybrid model performs
best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute
error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet
the Grade A and Grade B performance requirements of the British Hypertension Society
Keywords Blood pressure (BP)· Cuff-less· Photoplethysmogram (PPG)·
Electrocardiogram (ECG)· Deep learning
1 Introduction
Blood pressure (BP) is one of the most important and commonly measured clinical param-
eters and accurate measurement is crucial for therapeutic decisions. The World Health
Organization (WHO) estimates that 1.13 billion people worldwide have hypertension
which is a major cause of premature death. However, fewer than 1 in 5 people with hyper-
tension have the problem under control (World Health Organisation 2019). One of the
global targets for noncommunicable diseases is to reduce the prevalence of hypertension by
* Stephen P. Morgan
1 International Doctoral Innovation Centre, University ofNottingham Ningbo China, 199 Taikang
East Road, Ningbo, China
2 Optics andPhotonics Research Group, University ofNottingham, Nottingham, UK
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S.Yang et al.
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25% by 2050 (baseline 2010). Regular BP monitoring is thus essential for prevention and
control in the general population and for hypertensive patients.
Despite its importance, the existing non-invasive regular BP measure methods have
downsides that can be ascribed to their measuring devices. The most popular cuff-based
BP measurement requires user to follow protocols to obtain accurate BP values. Some cuff-
based devices, e.g., mercury sphygmomanometer, require frequent calibration. Other dis-
advantages include movement artefacts during physical activity (Ogedegbe and Pickering
2010) and discomfort during cuff inflation. In addition, for some people, the act of going
to the doctor triggers a response making their BP soar which clinicians recognize as white-
coat syndrome (Stergiou etal. 2018).
Due to the aforementioned factors, developing a cuff-less, continual or near real-time
periodic, robust, comfortable, and wearable BP measurements system is desirable. Using
physiological signals to conduct non-invasive and cuff-less BP measurement emerged in
the past decade (Li etal. 2018; Liang et al. 2018a; Sharifi etal. 2019; Yoon etal. 2009).
Two typical physiological signals used in BP estimation are the photoplethysmogram
(PPG) and electrocardiogram (ECG). Pulse arrival time (PAT) is the time interval between
the R-wave peak of the ECG and the systolic peak of the PPG. When the PAT is longer, it
indicates a lower BP, while a shorter PAT indicates a higher BP, but the precise relation-
ship is uncertain due to the complexity of the cardiovascular system. This method requires
a calibration protocol for stepwise increases in BP and several simultaneous measurements
of ECG, PPG and a reference method (e.g. a mercury sphygmomanometer). Furthermore,
individual calibrations are often needed to increase accuracy. Therefore, it is a challenge to
use PAT for BP measurement under clinical conditions (Hennig and Patzak 2013).
Recently, there has been growing interest in cuff-less and non-invasive BP estimation
using machine learning algorithms with the PPG and ECG (Chen et al. 2019; Kachuee
etal. 2017; Mousavi etal. 2019; Ribas Ripoll and Vellido 2019; Rundo etal. 2018). Most
of the studies extracted specific features in the time domain or frequency domain and their
results reveal the high correlation of these features with BP (Elgendi etal. 2019; Kachuee
etal. 2017; Tanveer and Hasan 2019; Wang etal. 2018). The two main challenges with
these approaches are the need for considerable signal processing and extraction of features
associated with physiological signals.
These drawbacks, alongside the emerging methods of using raw signals as inputs into
deep learning for different purposes (Gotlibovych etal. 2018; Slapničar etal. 2019), have
motivated us to investigate this approach for non-invasive BP measurement. To ensure the
high quality of the data, this research conducted a series of measurements on 45 partici-
pants to obtain a database of 315 records, each containing PPG, ECG, BP values and cor-
responding participant’s physical characteristics (i.e., age, height, weight and gender). This
is suitable for the investigation of the use of deep learning with raw signals and physical
characteristics for BP measurement for the first time to the authors’ knowledge. Moreover,
another objective is to compare the accuracy of predictions between traditional machine
learning methods and the novel hybrid deep learning model.
The novelty of this study is threefold. Firstly, although there have been attempts pre-
dicting BP values using deep learning methods, they rely on the use of physiological sig-
nals. To date, no one has tried to use both physiological signals and physical characteristics
as inputs in a deep learning structure. This study presents the first attempt in this regard
by devising a novel hybrid deep learning model. Secondly, this study provides a compre-
hensive comparison not only between traditional machine learning methods and hybrid
deep learning models, but also between hybrid deep learning models with different struc-
tures. Thirdly, the methods used to collect data to predict BP are simple and replicable.
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Combined with the automatic nature of the hybrid deep learning model, it largely reduces
the complexity for the end user and has the potential of large-scale implementation.
The paper is organized as follows: Sect. 2 explains the experimental data processing
procedures and all the algorithms used in this work. Section3 demonstrate the experimen-
tal design for this study. Section4 presents the results and finally, Sect. 5 concludes and
discusses the research.
2 Methods
Before application of machine learning algorithms, the ECG and PPG are pre-processed
and features are extracted. Several popular machine learning algorithms are then applied to
estimate BP from signal features and physical characteristics. Unlike traditional methods,
the proposed deep learning method does not require feature extraction and key information
contained in the raw data are automatically extracted by the deep learning network by self-
learning. Data acquisition will be described in Sect.3.
2.1 Data pre‑processing
The acquired PPG signal is processed by a Chebyshev II bandpass filter with the lower
and upper cut off frequencies of 0.5 and 10Hz respectively in order to reduce noise within
the raw PPG signal (Liang etal. 2018b). For the ECG signal, baseline drift and high fre-
quency noise are removed using a Butterworth bandpass filter with lower and upper cut
off frequencies of 0.5 and 40Hz respectively (Shin etal. 2010). Afterwards, the PPG and
ECG signals are normalized and their peaks in each period are obtained. The most stable
segments are chosen from both signals by a calculation of the highest cross-correlation
coefficient between periods which is defined by neighbouring peaks (Kachuee etal. 2015).
2.2 Feature extraction
2.2.1 PPG
In the literature, morphological features from PPG and complexity features from ECG are
often used to predict BP (Elgendi 2012; Kachuee etal. 2017; Simjanoska etal. 2018; Yang
etal. 2020). There are more than twenty features that can be extracted from a PPG signal
and its first and second derivatives (Elgendi 2012). Twelve of them are selected and used
for further estimation in this research. A PPG signal with labelled features is displayed in
Fig.1a and a PPG and its second derivative signals are shown in Fig.1b. A summary of
used features is listed in Table1.
2.2.2 ECG
The extracted and used features in this research are listed in Table2. Most of these features
from ECG signals are obtained from complexity analysis, except heart rate which is calcu-
lated from the measurement of the peak-to-peak time interval of the ECG signals.
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2.2.3 Pulse arrival time (PAT)
PAT is extracted and applied as one of the features in this study. PAT is defined as the time
interval between the electrical activation of the heart and arrival of the pulse pressure at a
distal point measured as the time between the peaks of PPG and ECG (Chan etal. 2019). It
Fig. 1 a A PPG signal with labelled features, adapted from (Kachuee etal. 2017). b Measured PPG sig-
nal (upper) and its second derivative (lower), indicating systolic and diastolic peaks, a-wave and b-wave.
Adapted from (Elgendi 2012)
Table 1 Extracted PPG features used in the study
Feature no. Feature name Descriptions Figure
1 Systolic amplitude (Chua and
Heneghan 2006) (Chua etal. 2010)
Systolic peak Figure1a
2 Pulse width Width
3 Peak to peak interval Time difference two successive systolic
4 Inflection point (Millasseau etal. 2002) Used to replace diastolic point
5 Augmentation index (Elgendi 2012)
6 Large arterial stiffness index Inversely related to the time interval ΔT
7 S1 Areas under the PPG signal
8 S2
9 S3
10 S4
11 Crest time (Alty etal. 2007) CT
12 Ratio of b/a (Baek etal. 2007) From 2nd derivative Figure1b
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includes the pre-ejection period, which is the time it takes for blood to leave the heart after
the heart’s electrical impulse.
2.3 Traditional machine learning methods
Several commonly used machine learning methods are used in this study to evaluate the
effectiveness of different methods in predicting BP using features extracted from PPG and
ECG signals and physical characteristics.
LASSO (least absolute shrinkage and selection operator) is a linear model with L1 prior
as a regularizer (Friedman etal. 2010). As a large number of features are used to predict
BP, it is important to add a regularization term in linear models to help with the variable
selection. LASSO is able to perform both variable selection and regularization, leading to
increase of prediction accuracy. The amount of regularization is controlled by α, the coeffi-
cient of the L1 term, and it can be determined experimentally using cross-validation during
the training process. In this study, fivefold cross-validation is used to select α.
Support Vector Regression (SVR) is a popular machine learning model and has been
proven to be an effective tool in real-value function estimation (Drucker etal. 1996). SVR
uses a symmetrical loss function and errors with absolute values that are smaller than a
certain threshold are ignored. As a result, the model produced by SVR depends only on a
subset of the training data. A fivefold cross-validated grid-search is used to search for the
optimal values for several important parameters, including kernel type (linear, polynomial,
radial basis function), kernel coefficient (0.1, 0.01, 0.001, 0.0001), regularization param-
eter (1, 0.1, 0.01, 0.001, 0.0001) and epsilon-tube (0.1, 1, 5, 10, 20) which specifies the
tolerance level.
AdaBoost, which is short for Adaptive Boosting, is an ensemble method and can be used
to fit a sequence of weak learners (other types of learning algorithms) to improve perfor-
mance (Drucker 1997). The final output is a combination of a weighted sum of predictions
generated by these weak learners. A commonly used weak learner, a decision tree regres-
sor is adopted in this study. A fivefold cross-validated grid-search is further used to search
for the optimal values of the number of iterations (5, 50, 500), learning rate (1, 0.1, 0.01,
0.001, 0.0001) and loss function (linear, square, exponential).
Random forest (RF) is another ensemble method that constructs a number of deci-
sion trees built from samples drawn with replacement (Breiman 2001). With the added
Table 2 Extracted features from
ECG (Yang etal. 2020)Feature name Number of
Autoregressive (AR) model coefficients of order 8 8
Multifractal wavelet leader
Second cumulant of scaling exponents 1
Holder exponents 1
Shannon Entropy (SE) values for the maximal overlap
discrete wavelet packet transform at level 5
Hjorth parameters
Signal mobility 1
Signal complexity 1
Heart rate 1
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randomness, random forest can decrease the variance of the forest estimator. A fivefold
cross-validated grid-search is used to search for the optimal values of several important
parameters, namely the number of trees (100, 150, 200, 500, 1000), the criterion to meas-
ure the quality of a split (mean squared error, mean absolute error) and the minimum num-
ber of samples required to split an internal node (2, 3, 4, 5, 10).
K-Nearest Neighbours (KNN) is a non-parametric method that calculates the predicted
value by taking weighted average values of k nearest neighbours. K is an integer value that
needs to be specified, as well as weighting scheme and distance metric. In this study, a
fivefold cross-validated grid-search is used to search for the optimal values of k (1, 5, 10,
15, 20), weighting scheme (uniform, distance) and distance metric (Euclidean, Manhattan).
Multi-layer Perceptron (MLP) is a typical class of feedforward neural network and it has
the capability to learn non-linear models. It consists of at least three layers, including input,
hidden and output layers. A fivefold cross-validated grid-search is used to search for the
optimal values of several important parameters, namely number of hidden layer (1, 2, 3),
number of nodes in the hidden layers (5, 10, 20, 50), activation function in the hidden layer
(logistic sigmoid, hyperbolic tangent, ReLU), coefficient for the L2 regularization term (1,
0.1, 0.01, 0.001, 0.0001) and maximum number of iterations (100, 200, 500, 1000).
2.4 Proposed deep learning model
This study proposes a novel deep learning model to utilize the information contained in the
PPG and ECG along with physical characteristics to predict BP. In contrast to the methods
mentioned earlier, which require pre-processing and feature extraction from the PPG and
ECG, deep learning models can take directly the raw signal data as input and the feature
learning is essentially embedded in the modelling process. This novel hybrid deep learning
model consists of various types of neural network models, such as Convolutional neural
network (CNN), Long short-term memory (LSTM) and fully connected layer (Dense). The
Dense layer is essentially a hidden layer in the MLP.
CNN was initially developed for image classification problems, where it receives two-
dimensional image pixels as input and generates output after a series of operations that
involve pattern learning. Multiple CNN layers are often applied in problems like this so
that simple patterns can first be identified in the lower layers and be used to form more
complex patterns within higher layers (Krizhevsky etal. 2012). The same process can be
applied to one-dimensional time series data, such as the PPG and ECG in this study. One-
dimensional CNN (1D CNN) can automatically learn to extract useful features from these
signals and how to construct appropriate models to predict BP.
1D CNN applies the convolution operation on the input data with a number of fil-
ters (also called feature detector) (LeCun and Bengio 1995). The length of these filters
can be specified and it is often referred to as kernel size. These filters are then moved
along the signals and the shift size is referred to as strides, which is often chosen to be
1. Different types of padding can be applied to determine the size of the output. Zero-
padding is often found to perform well in practice (Krizhevsky etal. 2012), and it is
also adopted in this study. An activation function is often applied to the results gener-
ated from the convolution operation. ReLU is very popular and found to perform well in
practice (Jarrett etal. 2009). Convolutional layers are often followed by dropout layers
for regularization, and then pooling layers, such as max pooling and average pooling
(Krizhevsky etal. 2012; Srivastava etal. 2014). CNN models tend to learn very quickly
and the dropout layer can help slow down the learning process and result in a potentially
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better final model. The pooling layers can help reduce the dimension and consolidate
learned features to the most essential elements. Pool length of 2 is often used in practice
and it is also adopted in this study. Several convolutional layers can be stacked together
to extract more complicated features. Hyperparameters that need to be determined for
1D CNN layers include kernel size (3, 5, 7, 9), number of filters (64, 128, 256, 512) and
number of epochs (20, 50, 100). In this study, the range of kernel sizes, number of fil-
ters and epochs is investigated using a cross-validation process in which an optimum is
selected based on accuracy and convergence time.
LSTM network model is a special type of recurrent neural network (RNN) that is able
to learn long-term dependencies (Hochreiter and Schmidhuber 1997). It has been proven
to be effective for sequence prediction tasks such as speech recognition, natural language
processing and machine translation (Chen etal. 2017; Cui etal. 2016; Tian etal. 2017).
A typical memory block in LSTM contains a memory cell and three gates, namely,
input, output and forget gates. The activation functions associated with the gates are
often logistic sigmoid function. LSTM can support multiple parallel sequences of input
data, such as the PPG and ECG signals in this study. LSTM can be used to automati-
cally learn temporal dependencies in raw PPG and ECG signals and use them to predict
BP values (Su etal. 2018). The parameter needs to be chosen for LSTM is the length of
state vector (10, 50, 100).
CNN and LSTM are two types of deep learning structures that can be used separately
to automatically learn from raw PPG and ECG signals to predict BP. They can also
be stacked together in a way that the output from CNN is fed to the following LSTM
layer. This stacked structure can be used to extract useful features and then learn the
long-term temporal dependencies from the raw signals. This type of structure has been
used for tasks such as detection of diabetes (Goutham etal. 2018), human activity rec-
ognition (Ordóñez and Roggen 2016), continuous cardiac monitoring (Saadatnejad etal.
2020), atrial fibrillation detection (Gotlibovych etal. 2018) and classification of myo-
cardial infarction (Baloglu etal. 2019), and it is often found to perform well in practice.
In addition to the raw signals, this study investigates a novel deep learning structure
that can also utilize useful information contained in physical characteristics to predict
BP. This novel model consists of various types of models, including CNN, LSTM and
Dense. This new structure can directly take raw signals and physical characteristics as
input at the same time. It can learn to automatically pick up useful information con-
tained in different types of input data and find an optimal way to link to BP.
3 Experimental design
Two streams of experiments are conducted in this study. The first stream involves the
use of physical characteristics and features extracted from PPG and ECG signals, which
are then used as input in traditional machine learning methods, namely LASSO, SVR,
AdaBoost, RR, KNN and MLP in this study. The second stream is the construction of
novel hybrid models that consists of various deep learning methods such as CNN and
LSTM and utilises physical characteristics and the raw PPG and ECG directly as inputs.
Several different architectures of hybrid models are investigated which are comprised of
different numbers of layers of CNN and LSTM. Experimental data is gathered using the
set-up and protocol described in the next section.
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3.1 Data acquisition system
The proposed cuff-less BP estimation system is illustrated as Fig.2 where BP, ECG
and PPG are measured simultaneously. Acquisition of data with this system has been
reviewed and approved according to the ethical review process in place.
The measurement system shown in Fig.2 comprises three sections for measurement
of BP, ECG and PPG. BP values are measured as a reference standard by a commercial
device (Lloyds Pharmacy Fully Automatic Blood Pressure Monitor LBPK1) with meas-
urement accuracy of ± 3mmHg (Lloyds 2021). The PPG signal is measured by infrared
transmission through the finger via a finger clip sensor (HRM-2511E, Kyoto Electronic
Co., China) with data transferred to a data acquisition board (Easy Pulse Sensor Ver-
sion 1.1, Elecrow, China) (Raj 2013). The ECG is measured with 3 disposable solid gel
electrodes based on the lead I configuration placed on 2 wrists and an ankle connected
to a data acquisition board (Analog devices, AD8232) (Lu etal. 2014). Due to availabil-
ity and convenience, the power for both circuit boards is supplied by an Arduino UNO
board (Arduino Co., Italy).
The measured PPG and ECG are then transmitted to a data acquisition device (USB-
6211, National Instruments). The sample frequency for the data acquisition is 1k sam-
ples/second in order to achieve a high-quality signal. The collected signals are sent to
the processing unit which is a battery powered laptop for the benefit of minimum noise
and to isolate the subject from mains power lines. All data were monitored and recorded
through LabView (National Instruments).
Fig. 2 System for measurement of BP, ECG and PPG for cuff-less BP estimation
Table 3 Physical characteristics
of participants in the experiment Mean Max Min
Age (years) 23.24 61 21
Height (cm) 168.78 190.5 150
Weight (kg) 66.84 110 45
Gender 23 Males 22 Females
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3.2 Data collection protocol
Data collection is performed on 45 participants. A detailed description is listed in
All participants are healthy adults with no apparent arterial disease or physiological
abnormality. Informed consent is obtained from all participants and they are requested to
not take drinks that contained caffeine or a heavy meal 4h before the experiment to prevent
a large variability in BP. For each participant, the data collection includes two measure-
ment sections occurring on the same day using the same data acquisition protocol. Each
experiment takes less than 40min to collect all relevant signals with the following protocol:
1. The participant stays still for 10min, during which the consent forms are signed; the
individual physical characteristics including age, height, weight and gender are recorded;
the cuff of commercial BP measurement device is worn on the upper right arm; elec-
trodes are pasted on the limbs for ECG signal; and the clip is fixed on the index finger
of the left hand for PPG signal acquisition. Participants are requested to keep still during
the measurement because the PPG signal is sensitive to movement.
2. The PPG and ECG are recorded continuously for a period of 3min. At the same time,
BP is also measured. This procedure is repeated 3 times.
3. To induce a change in BP, the participant is asked to go downstairs from the 4th floor
to the 1st floor and then return as rapidly and safely as possible.
4. Once the participant returns, the same procedure in step (2) is repeated, but for 4 times.
Hence, there are 7 sets of data collected from each participant within around 40 min.
Accordingly, there is a total number of 315 records of data obtained. For each record
of the data, it includes PPG, ECG, BP and the corresponding participant’s physical
While raw PPG and ECG signals are fed directly into the hybrid deep learning model,
pre-processed and extracted features from PPG and ECG signals are used as inputs for tra-
ditional machine learning methods. As detailed in Sect.2.2, 12 features are extracted from
PPG and 45 features are extracted from ECG. In addition, PAT is also extracted, which
involves the use of both PPG and ECG. As a result, there are 58 features extracted from
PPG and ECG in total. Combined with four physical characteristics, the input dimension
for each observation in traditional machine learning models is 62. As models for DBP and
SBP are separately built, the output for each observation in traditional machine learning
models is 1, which is the corresponding DBP or SBP value.
3.3 Cross validation experiments
To generate a model with good generalization ability, this study conducts fivefold cross-
validation (CV) experiments where the training and testing samples are from different sets
of subjects. Since there are 45 participants in this study, data samples from 9 random par-
ticipants are used as testing samples and the rest are used as training samples in each CV
experiment. CV experiments are repeated 20 times and the evaluation results are averaged
over these 20 experiments. Separate models are built for systolic blood pressure (SBP)
and diastolic blood pressure (DBP). Such an experimental design provides robust results
as it involves multiple experiments to tackle the potential instability in a particular CV
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experiment. In addition, CV can also help avoid dependence of results on the choice of the
split in each experiment.
During the training process of each CV experiment, the hyperparameters of traditional
machine learning methods are determined using a fivefold CV on the training data set.
For hybrid deep learning models, as they take a lot more time to train, their hyperparam-
eters are determined in the first CV experiment where there are 5 different train-test splits.
The best hyperparameters are decided to be the ones that are chosen most times in these 5
3.4 Hybrid model architectures
A general representation of the architecture of the hybrid model is shown in Fig.3.
In contrast to traditional machine learning methods, this newly proposed hybrid
model can take raw PPG and ECG signals and physical characteristics as simultaneous
inputs by combining different deep learning structures. This hybrid model does not need
any feature extraction from the raw signals and can learn to extract the optimal fea-
tures itself. The input consists of two main parts, namely raw PPG and ECG signals and
physical characteristics. The dimension of the signal part for each sample observation
Block 1
Block n
Raw PPG and ECG signals
Physical characteriscs
Concaten ate
Fig. 3 The architecture of the hybrid model
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is (5000, 2), which means it is 5s of data (sampling rate 1000/s) and 2 channels (PPG
and ECG), while the dimension of physical characteristics is 4, including age, height,
weight and gender. Again, as the models for DBP and SBP are separately built, the out-
put dimension is 1.
CNN blocks and LSTM are used to extract features from raw signals while dense is
used to extract features from physical characteristics. The features learnt are then con-
catenated and fed to another Dense layer. Finally, this dense layer is followed by output
layer with no (linear) activation function as the target variable BP is continuous. Mean
absolute error (MAE) is used as the loss function.
CNN layers can be stacked together to extract more complicated features, different
numbers of CNN blocks are used to form several different architectures. The number
of CNN blocks is set to vary from 1 to 5, which leads to 5 different architectures. We
denote hybrid models with 1–5 CNN blocks as Hybrid Model 1–5 respectively.
Each CNN block is comprised of 1D CNN, dropout and maxpooling layers. Dropout
layer is also used following LSTM because it can impose regularization and prevent
overfitting. The dropout rate defines the probability of a randomly selected neuron being
dropped out. The dropout is only implemented during the training and not used in the
testing. The dropout rate is chosen from 0.1, 0.2 and 0.5 during the training, when other
hyperparameters are being chosen for the hybrid model, including the number of hidden
nodes for the Dense layers (10, 50, 100).
4 Results
4.1 Measured BP
Histograms of the BP data obtained from sphygmomanometer are presented in Fig.4.
The measured DBP and SBP ranged from 56–106mmHg to 84–170mmHg respectively.
The relatively large range of BP values is driven by interval measurement after physical
exercise in order to test the robustness of the prediction of BP values.
Fig. 4 Histogram of the BP values measured of: a SBP; b DBP
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4.2 Training andprediction results
After the first CV experiment, the hyperparameters are all chosen for hybrid models. Then
after 20 repetitions of CV experiments, the model with the best prediction performance is
Hybrid Model 3 with the following configuration details:
CNN parameters: kernel size 7, number of filters 128 and number of epochs 20.
Dropout rate: 0.5
Pool length of Maxpooling layer: 2
Length of state vector of LSTM: 50
Number of hidden nodes for the Dense layers: 50
The BP prediction results of traditional machine learning methods and newly proposed
hybrid models are shown in Table4. Criteria for performance evaluation are MAE and
standard deviation (STD) of estimation.
The MAE and STD are calculated over 20 repetitions of CV experiments. According
to Table 4, some comparisons can be made. For instance, what stands out in the table
is that Hybrid Model 3 performs best in terms of both DBP and SBP with the results of
3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. It is closely followed by Hybrid
Model 4 and Hybrid Model 5. It suggests that 3 CNN blocks are sufficient to extract useful
features from the raw signals.
It is clear that in all models, hybrid models achieved lower SBP and DBP errors than
traditional machine learning methods. It indicates that this newly proposed hybrid model
architecture can extract more information from the raw signals than manually extracted fea-
tures, which leads to a more accurate prediction of BP when combined with physical char-
acteristics. This also alludes to the possible misrepresentation of information by manually
extracted features due to challenges encountered with distorted waveforms. Deep learning
models seem to be more robust in this regard.
In addition to the comparisons within traditional machine learning methods, SVR is
found to perform best in terms of DBP with the result of 5.05 ± 7.26mmHg and followed
Table 4 Mean absolute error
(MAE) and standard deviation
(STD) achieved by traditional
machine learning methods and
novel hybrid models. Hybrid
Model n denotes n CNN blocks
in the model
Diastolic blood pressure
Systolic blood pres-
sure (mmHg)
LASSO 8.27 10.47 10.28 13.49
SVR 5.05 7.26 7.66 9.87
AdaBoost 7.82 8.08 8.92 10.95
RF 6.99 8.38 8.01 9.82
KNN 6.18 7.93 8.74 10.37
MLP 5.82 7.29 6.92 9.11
Hybrid model 1 4.98 5.85 6.73 8.02
Hybrid model 2 4.12 5.76 5.35 7.72
Hybrid model 3 3.23 4.75 4.43 6.09
Hybrid model 4 3.94 4.97 4.89 6.73
Hybrid model 5 3.83 5.01 5.22 7.28
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by MLP and KNN. In terms of SBP, MLP, with 6.92 ± 9.11mmHg, performs best among
traditional machine learning methods, followed by SVR and RF. LASSO is found to per-
form worst regarding both DBP and SBP, and it can be inferred that there exist strong non-
linear relationships between features used and BP which can lead to inferior performance
of LASSO. In addition, the inherent complexity in this problem necessitates using power-
ful regression algorithms, like hybrid models.
In order to further understand the origins of the improvements provided by the proposed
hybrid model, the performance of models without physical characteristics is investigated.
This investigates whether the superior performance of the hybrid models is mainly driven
by automatic feature extraction of raw signals or due to inclusion of the physical character-
istics of the subject.
The number of CNN blocks of these deep learning models are again set to vary from 1
to 5, and we denote these models by DP 1–5 respectively. The same procedure applied for
hybrid models is used for training.
The results of DP are shown in Table5. Regarding DBP prediction, the accuracy of DP
models improves quickly when the number of CNN blocks increases from 1 to 3. However,
as the number further increases from 3 to 5, the performance does not improve. A similar
pattern can be observed for the case of SBP prediction by DP model, except that the lowest
MAE and STD are obtained when the number of CNN blocks is equal to 4. When compar-
ing DP and hybrid model with the same number of CNN blocks, the results indicate that
the hybrid model always perform better than DP. This indicates that the inclusion of physi-
cal characteristics increases the prediction accuracy.
The results from the 3 best performing models, which are Hybrid Models with 3, 4 and
5 CNN blocks are further compared with British Hypertension Society (BHS) standard as
shown in Table6. This standard requires that the cumulative percentage of error is under
5mmHg, 10mmHg and 15mmHg (O’Brien etal. 2001). In this work, the predicted value
of DBP obtained from the Hybrid Model with 3 CNN blocks is consistent with Grade A
and the other two models meet Grade C. In addition, the hybrid model with 3 CNN blocks
is in congruence with Grade B and that with 4 CNN blocks meet Grade C in the estimation
of SBP values. However, the estimation of SBP from the Hybrid model with 5 CNN blocks
is not consistent with the BHS standard.
The Association for the Advancement of Medical Instrumentation (AAMI) standard
requires BP measurement devices to have MAE and STD values lower than 5mmHg and
8mmHg, respectively. According to Table6, all hybrid models achieve the requirements
when estimating DBP. However, only Hybrid Model 3 and 4 is consistent with the stand-
ard in SBP estimation. Also, the MAE and STD values of all traditional machine learning
models are outside the stipulated limits.
Table 5 Mean absolute error
(MAE) and standard deviation
(STD) of DP, Dense and new
hybrid models
Diastolic blood pressure
Systolic blood pres-
sure (mmHg)
DP 1 6.53 7.32 8.03 9.78
DP 2 5.29 6.13 6.98 8.15
DP 3 4.32 5.11 5.51 7.89
DP 4 4.33 5.18 5.37 7.60
DP 5 4.40 5.24 5.63 7.97
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Table 6 Comparison of the 3 best performing models with the BHS standard (O’Brien etal. 2001). Hybrid Model n denotes n CNN blocks in the model
Cumulative Error Percentage
≤ 5mmHg (%) ≤ 10mmHg (%) ≤ 15mmHg (%) ≤ 5mmHg (%) ≤ 10mmHg (%) ≤ 15mmHg (%)
3 best performance models
Hybrid model 3 69.53 87.29 97.97 51.65 85.95 90.02
Hybrid model 4 59.83 84.24 96.53 43.77 74.78 91.24
Hybrid model 5 63.42 85.36 98.21 39.35 68.57 86.29
BHS standard
Grade A 60 85 95
Grade B 50 75 90
Grade C 40 65 85
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Table 7 A summary of the comparison of BP estimation results with other work
*MIMIC II stands for Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) II
Wor k Subjects Database Features DBP
SBP (mmHg) Models Notes
Dey etal. (2018) 205 Collected by authors PPG 5.0 6.1 6.9 9.0 Combined model Demographic and
physiological parti-
Kachuee etal. (2017) 5599 MIMIC II* PPG and PTT 5.32 6.14 11.17 10.09 AdaBoost Calibration free
He etal. (2016)> 2000 MIMIC II* PPG and ECG 4.44 3.72 8.29 5.84 Random forest Calibration free
Gao etal. (2016) 65 Collected by authors PPG 4.6 5.1 Discrete wavelet transform Phone-obtained PPG
Kachuee etal. (2015) 850 MIMIC II* PPG and PTT 6.34 8.45 12.38 16.17 SVM Calibration free
This work 45 315 records and col-
lected by authors
Physical characteris-
tics, raw PPG and
3.23 4.75 4.43 6.09 Hybrid Model with 3 CNN blocks Calibration free
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Table7 compares the proposed approach in this paper and other works in the literature,
which use PPG, ECG and machine learning algorithms for BP estimation. In general, it is
difficult to compare related work in this field because of different and inadequately speci-
fied databases, different signal pre-processing procedures, and different evaluation methods
with different machine learning algorithms. In addition, some authors mixed all data and
split randomly for training and testing, but they did not explicitly report whether any data
of test subjects were included in the training data. Thus far, it is difficult to perform an
objective comparison between different research.
However, from a general perspective, most previous research applies traditional
machine learning algorithms, and the best results of DBP estimation ranges from about 4
to 6mmHg. The best SBP estimation results vary from 5 to 12mmHg. Although the BP
estimation results of traditional algorithms in this work have no obvious advantages com-
pared with the results from previous research, it is evident that hybrid models provide more
accurate prediction of BP.
5 Discussion andconclusions
In this paper, a novel hybrid deep learning model is proposed to predict BP using raw PPG,
ECG signals and some physical characteristics. Traditional machine learning methods used
in predicting BP involve extracting features from signals and it often presents challenges
when the quality of the signal is not good. This novel hybrid deep learning model consists
of several different types of deep learning layers which enable the automatic feature extrac-
tion and can learn to extract optimal features in the modelling process. The hybrid models
are tested on the data set collected and provide superior prediction results compared with
traditional machine learning models. Deep learning models have shown high performance
in many research areas and this study has shown its enormous potential in its application
in predicting BP. Because of its flexible structure, deep learning models can receive vari-
ous combinations of different types of inputs. This is a very useful feature as incorporating
more physiological data that can be relevant to BP is likely to increase the prediction accu-
racy. The best performance of hybrid model achieves 3.23 ± 4.75mmHg for DBP estima-
tion and 4.43 ± 6.09mmHg for SBP estimation. This result is consistent with Grade A and
Grade B in the estimation of DBP and SBP respectively. In line with this, this model also
achieves the requirements of the AAMI standard. It indicates that hybrid models with raw
PPG and ECG signals have high potential in cuff-less BP estimation.
Different number of CNN blocks are used in this study and three CNN blocks are found
to provide the best prediction results. Compared with its application in other areas such as
image processing, which often benefits from many more CNN layers, the useful features
contained in the physiological signals are not as complex. Therefore, the hybrid model
does not have to be very deep. Indeed, hybrid models with four and five CNN blocks are
outperformed by the hybrid model with three CNN blocks.
LSTM is included after CNN blocks as it is very useful in finding the important tem-
poral features in time series data and is suitable in processing signals. After LSTM the
features extracted from signals are combined with physical characteristics, which are age,
height, weight and gender in this study.
With the automatic learning of optimal features in the training stage, this hybrid model
minimizes the risk of omitting important features contained in the signals. Traditional
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feature extraction entails professional knowledge of specific signals and it is not often pos-
sible to extract all features that are potentially useful.
Despite the promising results found in this study, many important research questions
remain. The focus was on developing a general hybrid model whose hyperparameters are
determined using a pool of data from a number of individuals. A more accurate model can
be estimated by tuning these hyperparameters based on the data of each individual. After
this process, these models can be further calibrated to provide potentially more accurate
prediction for different people as their associated optimal structure and hyperparameters
may vary. The data used in this study is collected by the authors and there are 315 samples
in total. As deep learning models often require a big data set, more data is likely to further
improve the prediction accuracy. Therefore, in the next stage, we intend to further test the
novel hybrid model on bigger data sets, including those that are publicly available. In addi-
tion, this paper focuses on improving the prediction accuracy of BP, however, before deep
learning approaches are widely adopted it is important to consider the causality and rela-
tive importance of various features in predicting BP values (Holzinger etal. 2019). Due to
deep learning models’ multilayer and nonlinear structure, the relationship between input
and output is not transparent and predictions are often not traceable. This causes problems
in the interpretability of the deep learning models and make them of limited use in cases
where causalities are of great importance in the study. Although beyond the scope of this
work, this is a future direction that should be investigated.
Acknowledgements The authors acknowledge the financial support from the International Doctoral Inno-
vation Centre, Ningbo Education Bureau, Ningbo Science and Technology Bureau, and the University of
Nottingham. This work was also supported by the UK Engineering and Physical Sciences Research Council
[grant numbers EP/G037345/1 and EP/L016362/1].
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of interest.
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... ties, the principal drawback is the limited penetration of light into the tissue and their inability to capture haemodynamic parameters from arterial locations 27 . It has been proven that optical sensors can estimate heart rate from the skin surface and capillaries 25,26 ; however, the BP pulse wave does not reach capillaries effectively 28 , so BP cannot be captured from the capillaries ( Supplementary Fig. 1). ...
... The violin plot includes the box plot (defined as Q1 and Q3 quartiles, and median) with a kernel density estimation over the points. e, Comparison of the DBP estimation accuracy achieved with the GETs (Z-BP, green star) with Ag/AgCl gel-based BP 35 , photoplethysmography (PPG) 25 , ultrasound 52 , tonometry 53 , capacitive sensors 24 and various cuff-based methods (white circles) 54,55 . Only the works with relevant (m.e. and s.d.) data provided were included. ...
... 19 and 20), requiring only a primary calibration and subsequent continuous usage. Furthermore, our system demonstrates a capacity to run nocturnally with high fidelity without disturbing patients, which is not feasible with current obtrusive cuff-based monitors 3,50,51 or low-fidelity emerging cuffless BP monitoring solutions 25,26 owing to motion-noise artefacts that decrease the machine learning model efficiency 35 . Deployment of the system with miniaturized integrated circuits, wireless operation and data storage capabilities in the context of a smart-watch solution are among the future steps for translational research to develop a fully integrated wearable system. ...
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Continuous monitoring of arterial blood pressure (BP) in non-clinical (ambulatory) settings is essential for understanding numerous health conditions, including cardiovascular diseases. Besides their importance in medical diagnosis, ambulatory BP monitoring platforms can advance disease correlation with individual behaviour, daily habits and lifestyle, potentially enabling analysis of root causes, prognosis and disease prevention. Although conventional ambulatory BP devices exist, they are uncomfortable, bulky and intrusive. Here we introduce a wearable continuous BP monitoring platform that is based on electrical bioimpedance and leverages atomically thin, self-adhesive, lightweight and unobtrusive graphene electronic tattoos as human bioelectronic interfaces. The graphene electronic tattoos are used to monitor arterial BP for >300 min, a period tenfold longer than reported in previous studies. The BP is recorded continuously and non-invasively, with an accuracy of 0.2 ± 4.5 mm Hg for diastolic pressures and 0.2 ± 5.8 mm Hg for systolic pressures, a performance equivalent to Grade A classification. Self-adhesive bioimpedance graphene electronic tattoos enable accurate continuous blood pressure monitoring.
... Recently, researchers have shown increased interest in developing advanced models, such as Convolutional Neural Networks (CNN) [9][10][11][12][13]. Many of them tend to use automated feature extraction from raw PPG signals or images of PPG waveforms. ...
... The model introduced in [9] has a relatively high but acceptable standard deviation, particularly for SBP (5.59 ± 7.25) compared to other models. With a comparable number of subjects, the proposed CNN-LSTM model in [11] obtained 4.43 ± 6.09 for SBP and 3.23 ± 4.75 for DBP on 45 subjects. Whereas our model achieved a MAE ± SD of 2.03 ± 3.12 for SBP and 1.18 ± 1.70 for DBP on 40 subjects. ...
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Considerable research has been devoted to developing machine-learning models for continuous Blood Pressure (BP) estimation. A challenging problem that arises in this domain is the selection of optimal features with interpretable models for medical professionals. The aim of this study was to investigate evidence-based physiologically motivating features based on a solid physiological background of BP determinants. A powerful and compact set of features encompassing six physiologically oriented features was extracted in addition to another set of features consisting of six commonly used features for comparison purposes. In this study, we proposed a BP predictive model using Long Short-Term Memory (LSTM) networks with multi-stage transfer learning approach. The proposed model topology consists of three cascaded stages. First, a BP classification stage. Second, a Mean Arterial Pressure (MAP) regression stage to further approximate a quantity proportional to Vascular Resistance (VR) using the extracted Cardiac Output (CO) from the PPG signal. Third, the main BP estimation stage. The final stage (final BP prediction) is able to exploit embedded correlations between BP and the proposed features along with derived outputs carrying hemodynamic characteristics through the sub-sequence stages. We also constructed traditional single-stage Artificial Neural Network (ANN) and LSTM-based models to appraise the performance gain of our proposed model. The models were tested and evaluated on 40 subjects from the MIMIC II database. The LSTM-based multi-stage model attained a MAE ± SD of 2.03 ± 3.12 for SBP and 1.18 ± 1.70 mmHg for DBP. The proposed set of features resulted in drastic error reduction, of up to 86.21%, compared to models trained on the commonly used features. The superior performance of the proposed multi-stage model provides confirmatory evidence that the selected transferable features among the stages coupled with the high-performing multi-stage topology enhance blood pressure estimation accuracy using PPG signals. This indicates the compelling nature and sufficiency of the proposed efficient features set.
... The analysis of the second derivative of a PPG beat evaluates the ratios of the characteristic peaks. We included b/a as this is the most commonly used feature of the second derivative in BP estimation [42,53,54]. The second derivative is calculated from the reconstructed beat to reduce the impact of noise. ...
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Blood pressure (BP) is among the most important vital signals. Estimation of absolute BP solely using photoplethysmography (PPG) has gained immense attention over the last years. Available works differ in terms of used features as well as classifiers and bear large differences in their results. This work aims to provide a machine learning method for absolute BP estimation, its interpretation using computational methods and its critical appraisal in face of the current literature. We used data from three different sources including 273 subjects and 259,986 single beats. We extracted multiple features from PPG signals and its derivatives. BP was estimated by xgboost regression. For interpretation we used Shapley additive values (SHAP). Absolute systolic BP estimation using a strict separation of subjects yielded a mean absolute error of 9.456mmHg and correlation of 0.730. The results markedly improve if data separation is changed (MAE: 6.366mmHg, r: 0.874). Interpretation by means of SHAP revealed four features from PPG, its derivation and its decomposition to be most relevant. The presented approach depicts a general way to interpret multivariate prediction algorithms and reveals certain features to be valuable for absolute BP estimation. Our work underlines the considerable impact of data selection and of training/testing separation, which must be considered in detail when algorithms are to be compared. In order to make our work traceable, we have made all methods available to the public.
... In a word, monitoring theories of these methods limit their wide application in clinical and home use. Therefore, it is necessary to develop an easy-to-use and accurate method for continuous BP monitoring (Lázaro et al., 2019;Yang et al., 2021;Yen and Liao, 2022). ...
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In this article, a novel method for continuous blood pressure (BP) estimation based on multi-scale feature extraction by the neural network with multi-task learning (MST-net) has been proposed and evaluated. First, we preprocess the target (Electrocardiograph; Photoplethysmography) and label signals (arterial blood pressure), especially using peak-to-peak time limits of signals to eliminate the interference of the false peak. Then, we design a MST-net to extract multi-scale features related to BP, fully excavate and learn the relationship between multi-scale features and BP, and then estimate three BP values simultaneously. Finally, the performance of the developed neural network is verified by using a public multi-parameter intelligent monitoring waveform database. The results show that the mean absolute error ± standard deviation for systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) with the proposed method against reference are 4.04 ± 5.81, 2.29 ± 3.55, and 2.46 ± 3.58 mmHg, respectively; the correlation coefficients of SBP, DBP, and MAP are 0.96, 0.92, and 0.94, respectively, which meet the Association for the Advancement of Medical Instrumentation standard and reach A level of the British Hypertension Society standard. This study provides insights into the improvement of accuracy and efficiency of a continuous BP estimation method with a simple structure and without calibration. The proposed algorithm for BP estimation could potentially enable continuous BP monitoring by mobile health devices.
... According to the British Hypertension Society (BHS) standard, the results of their estimations achieve grade A for DBP and grade B for the mean arterial pressure (MAP). The authors of [14] developed a hybrid deep-learning-based model to predict BP from PPG and ECG signals. They implemented an automatic feature extraction layer in a deep-learning model to extract the optimal features from PPG signals for predicting BP. ...
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Blood pressure measurements are one of the most routinely performed medical tests globally. Blood pressure is an important metric since it provides information that can be used to diagnose several vascular diseases. Conventional blood pressure measurement systems use cuff-based devices to measure the blood pressure, which may be uncomfortable and sometimes burdensome to the subjects. Therefore, in this study, we propose a cuffless blood pressure estimation model based on Monte Carlo simulation (MCS). We propose a heterogeneous finger model for the MCS at wavelengths of 905 nm and 940 nm. After recording the photon intensities from the MCS over a certain range of blood pressure values, the actual photoplethysmography (PPG) signals were used to estimate blood pressure. We used both publicly available and self-made datasets to evaluate the performance of the proposed model. In case of the publicly available dataset for transmission-type MCS, the mean absolute errors are 3.32 ± 6.03 mmHg for systolic blood pressure (SBP), 2.02 ± 2.64 mmHg for diastolic blood pressure (DBP), and 1.76 ± 2.8 mmHg for mean arterial pressure (MAP). The self-made dataset is used for both transmission- and reflection-type MCSs; its mean absolute errors are 2.54 ± 4.24 mmHg for SBP, 1.49 ± 2.82 mmHg for DBP, and 1.51 ± 2.41 mmHg for MAP in the transmission-type case as well as 3.35 ± 5.06 mmHg for SBP, 2.07 ± 2.83 mmHg for DBP, and 2.12 ± 2.83 mmHg for MAP in the reflection-type case. The estimated results of the SBP and DBP satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standards and are within Grade A according to the British Hypertension Society (BHS) standards. These results show that the proposed model is efficient for estimating blood pressures using fingertip PPG signals.
p>Cuffless blood pressure (BP) monitoring has gained great attention in the past twenty years considering its significant benefits in cardiovascular healthcare. However, the main challenge of this technology is the inaccurate BP modeling under activities, i.e., existing work have either been inappropriately validated with sufficient intra-individual BP variations, or did not show promising estimation accuracy under activities. In this study, a novel deep learning model UTransBPNet , featured in short- and long-range feature representation, is proposed aiming to improve the estimation accuracy and tracking capability of intra-individual BP changes under activities. The model performance was comprehensively evaluated in three different datasets, i.e., one public dataset (Dataset_MIMIC) and two datasets under daily activities (Dataset_Drink and Dataset_Exercise). Under subject-independent validation, the model achieved state-of-the-art performance in the Dataset_MIMIC, with the mean absolute differences (MADs) for systolic BP (SBP) and diastolic BP (DBP) of 4.38 and 2.25 mmHg, respectively. In addition, the model achieved strong tracking capability of intra-individual BP variations under activities, with the individual Pearson’ correlation coefficients for SBP and DBP of 0.61±0.17 and 0.62±0.13 (Dataset_Drink), 0.82±0.11 and 0.72±0.18 (Dataset_Exercise), respectively. Moreover, this study for the first time tested the generalization capability across different activities, and showed that with small-sized scenario-specific data for finetuning, our model showed good cross-scenario generalization capability which however degraded significantly when there are differences in the BP distribution and variation patterns between the datasets. In conclusion, UTransBPNet is generally a very promising deep learning model for accurate cuffless BP estimation and tracking BP changes. Future work should further investigate the influence of BP distribution and variation patterns on the generalization capability for building effective training dataset. </p
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Blood pressure (BP) estimation is one of the most popular and long-standing topics in health-care monitoring area. The utilization of machine learning (ML) and deep learning (DL) for BP prediction has made remarkable progress recently along with the development of ML and especially DL technologies, and the release of large-scale available datasets. In this survey, we present a comprehensive, systematic review about the recent advance of ML and DL for BP prediction. To start with, we systematically sort out the current progress from four perspectives. Then, we summarized commonly-used datasets, evaluation metrics as well as evaluation procedures (especially the usually ignored splitting strategy operation), which is followed by a critical analysis about the reported results. Next, we discussed several practical issues as well as newly-emerging techniques appeared in the research community of BP prediction. Also, we introduced the potential application of several advanced ML technologies in BP estimation. Last, we discussed the question of what a good BP estimator should look like?, and then a general proposal for an objective evaluation of model performance is given from the perspective of an ML researcher. Through this survey, we wish to provide a comprehensive, systematic, up-to-date (to Feb, 2022) review of related research on BP prediction using ML & DL methods, which may be helpful to researchers in this area. We also appeal an objective view of the progress reported in the relevant literatures in a more systematic manner. The experimental data & code and other useful resources are available at
A far cry from the bulky, uncomfortable cuff, the ultralight sensor takes measurements of the vital sign without the wearer feeling a thing.
Estimating physiological parameters - such as blood pressure (BP) - from raw sensor data captured by noninvasive, wearable devices rely on either burdensome manual feature extraction designed by domain experts to identify key waveform characteristics and phases, or deep learning (DL) models that require extensive data collection. We propose the Data-Driven Guided Attention (DDGA) framework to optimize DL models to learn features supported by the underlying physiology and physics of the captured waveforms, with minimal expert annotation. With only a single template waveform cardiac cycle and its labelled fiducial points, we leverage dynamic time warping (DTW) to annotate all other training samples. DL models are trained to first identify them before estimating BP to inform them which regions of the input represent key phases of the cardiac cycle, yet we still grant the flexibility for DL to determine the optimal feature set from them. In this study, we evaluate DDGA's improvements to a BP estimation task for three prominent DL-based architectures with two datasets: 1) the MIMIC-III waveform dataset with ample training data and 2) a bio-impedance (Bio-Z) dataset with less than abundant training data. Experiments show that DDGA improves personalized BP estimation models by an average 8.14% in root mean square error (RMSE) when there is an imbalanced distribution of target values in a training set and improves model generalizability by an average 4.92% in RMSE when testing estimation of BP value ranges not previously seen in training.
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A novel method for the continual, cuff-less estimation of the systolic blood pressure (SBP) and diastolic blood pressure (DBP) values based on signal complexity analysis of the photoplethysmogram (PPG) and the electrocardiogram (ECG) is reported. The proposed framework estimates the blood pressure (BP) values obtained from signals generated from 14 volunteers subjected to a series of exercise routines. Herein, the physiological signals were first pre-processed, followed by the extraction of complexity features from both the PPG and ECG. Subsequently the complexity features were used in regression models (artificial neural network (ANN), support vector machine (SVM) and LASSO) to predict the BP. The performance of the approach was evaluated by calculating the mean absolute error and the standard deviation of the predicted results and compared with the recommendations made by the British Hypertension Society (BHS) and Association for the Advancement of Medical Instrumentation. Complexity features from the ECG and PPG were investigated independently, along with the combined dataset. It was observed that the complexity features obtained from the combination of ECG and PPG signals resulted to an improved estimation accuracy for the BP. The most accurate DBP result of 5.15 ± 6.46 mmHg was obtained from ANN model, and SVM generated the most accurate prediction for the SBP which was estimated as 7.33 ± 9.53 mmHg. Results for DBP fall within recommended performance of the BHS but SBP is outside the range. Although initial results are promising, further improvements are required before the potential of this approach is fully realised.
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Considering the existing issues of traditional blood pressure (BP) measurement methods and non-invasive continuous BP measurement techniques, this study aims to establish the systolic BP and diastolic BP estimation models based on machine learning using pulse transit time and characteristics of pulse waveform. In the process of model construction, the mean impact value method was introduced to investigate the impact of each feature on the models and the genetic algorithm was introduced to implement parameter optimization. The experimental results showed that the proposed models could effectively describe the nonlinear relationship between the features and BP and had higher accuracy than the traditional methods with the error of 3.27 ± 5.52 mmHg for systolic BP and 1.16 ± 1.97 mmHg for diastolic BP. Moreover, the estimation errors met the requirements of the Advancement of Medical Instrumentation and British Hypertension Society criteria. In conclusion, this study was helpful in promoting the practical application of methods for non-invasive continuous BP estimation models.
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Hypertension is one of the most prevalent diseases and is often called the “silent killer” because there are usually no early symptoms. Hypertension is also associated with multiple morbidities, including chronic kidney disease and cardiovascular disease. Early detection and intervention are therefore important. The current routine method for diagnosing hypertension is done using a sphygmomanometer, which can only provide intermittent blood pressure readings and can be confounded by various factors, such as white coat hypertension, time of day, exercise, or stress. Consequently, there is an increasing need for a non-invasive, cuff-less, and continuous blood pressure monitoring device. Multi-site photoplethysmography (PPG) is a promising new technology that can measure a range of features of the pulse, including the pulse transit time of the arterial pulse wave, which can be used to continuously estimate arterial blood pressure. This is achieved by detecting the pulse wave at one body site location and measuring the time it takes for it to reach a second, distal location. The purpose of this review is to analyze the current research in multi-site PPG for blood pressure assessment and provide recommendations to guide future research. In a systematic search of the literature from January 2010 to January 2019, we found 13 papers that proposed novel methods using various two-channel PPG systems and signal processing techniques to acquire blood pressure using multi-site PPG that offered promising results. However, we also found a general lack of validation in terms of sample size and diversity of populations.
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Blood pressure (BP) is a direct indicator of hypertension, a dangerous and potentially deadly condition. Regular monitoring of BP is thus important, but many people have aversion towards cuff-based devices, and their limitation is that they can only be used at rest. Using just a photoplethysmogram (PPG) to estimate BP is a potential solution investigated in our study. We analyzed the MIMIC III database for high-quality PPG and arterial BP waveforms, resulting in over 700 h of signals after preprocessing, belonging to 510 subjects. We then used the PPG alongside its first and second derivative as inputs into a novel spectro-temporal deep neural network with residual connections. We have shown in a leave-one-subject-out experiment that the network is able to model the dependency between PPG and BP, achieving mean absolute errors of 9.43 for systolic and 6.88 for diastolic BP. Additionally we have shown that personalization of models is important and substantially improves the results, while deriving a good general predictive model is difficult. We have made crucial parts of our study, especially the list of used subjects and our neural network code, publicly available, in an effort to provide a solid baseline and simplify potential comparison between future studies on an explicit MIMIC III subset.
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The measurement of blood pressure (BP) is critical to the treatment and management of many medical conditions. High blood pressure is associated with many chronic disease conditions, and is a major source of mortality and morbidity around the world. For outpatient care as well as general health monitoring, there is great interest in being able to accurately and frequently measure BP outside of a clinical setting, using mobile or wearable devices. One possible solution is photoplethysmography (PPG), which is most commonly used in pulse oximetry in clinical settings for measuring oxygen saturation. PPG technology is becoming more readily available, inexpensive, convenient, and easily integrated into portable devices. Recent advances include the development of smartphones and wearable devices that collect pulse oximeter signals. In this article, we review (i) the state-of-the-art and the literature related to PPG signals collected by pulse oximeters, (ii) various theoretical approaches that have been adopted in PPG BP measurement studies, and (iii) the potential of PPG measurement devices as a wearable application. Past studies on changes in PPG signals and BP are highlighted, and the correlation between PPG signals and BP are discussed. We also review the combined use of features extracted from PPG and other physiological signals in estimating BP. Although the technology is not yet mature, it is anticipated that in the near future, accurate, continuous BP measurements may be available from mobile and wearable devices given their vast potential.
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Objective: A novel electrocardiogram (ECG) classification algorithm is proposed for continuous cardiac monitoring on wearable devices with limited processing capacity. Methods: The proposed solution employs a novel architecture consisting of wavelet transform and multiple long short-term memory (LSTM) recurrent neural networks (see Fig. 1). Results: Experimental evaluations show superior ECG classification performance compared to previous works. Measurements on different hardware platforms show the proposed algorithm meets timing requirements for continuous and real-time execution on wearable devices. Conclusion: In contrast to many compute-intensive deep-learning based approaches, the proposed algorithm is lightweight, and therefore, brings continuous monitoring with accurate LSTM-based ECG classification to wearable devices. Significance: The proposed algorithm is both accurate and lightweight. The source code is available online at
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Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. However, their weakness was in dealing with uncertainties of the real world. Through the introduction of probabilistic learning, applications became increasingly successful, but increasingly opaque. Explainable AI deals with the implementation of transparency and traceability of statistical black‐box machine learning methods, particularly deep learning (DL). We argue that there is a need to go beyond explainable AI. To reach a level of explainable medicine we need causability. In the same way that usability encompasses measurements for the quality of use, causability encompasses measurements for the quality of explanations. In this article, we provide some necessary definitions to discriminate between explainability and causability as well as a use‐case of DL interpretation and of human explanation in histopathology. The main contribution of this article is the notion of causability, which is differentiated from explainability in that causability is a property of a person, while explainability is a property of a system This article is categorized under: • Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Abstract Explainable AI.
Although photoplethysmogram (PPG) and electrocardiogram (ECG) signals can be used to estimate blood pressure (BP) by extracting various features, the changes in morphological contours of both PPG and ECG signals due to various diseases of circulatory system and interaction of other physiological systems make the extraction of such features very difficult. In this work, we propose a waveform-based hierarchical Artificial Neural Network – Long Short Term Memory (ANN-LSTM) model for BP estimation. The model consists of two hierarchy levels, where the lower hierarchy level uses ANNs to extract necessary morphological features from ECG and PPG waveforms and the upper hierarchy level uses LSTM layers to account for the time domain variation of the features extracted by the lower hierarchy level. The proposed model is evaluated on 39 subjects using the Association for the Advancement of Medical Instrumentations (AAMI) standard and the British Hypertension Society (BHS) standard. The method satisfies both the standards in the estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP). For the proposed network, the mean absolute error (MAE) and the root mean square error (RMSE) for SBP estimation are 1.10 and 1.56 mmHg, respectively, and for DBP estimation are 0.58 and 0.85 mmHg, respectively. The performance of the proposed hierarchical ANN-LSTM model is found to be better than the other feature engineering-based networks. It is shown that the proposed model is able to automatically extract the necessary features and their time domain variations to estimate BP reliably in a noninvasive continuous manner. The method is expected to greatly facilitate the presently available mobile health-care gadgets in cuffless continuous BP estimation.
Myocardial infraction (MI), commonly known as heart attack, causes irreversible damage to heart muscles and even leads to death. Rapid and accurate diagnosis of MI is critical to avoid deaths. Blood tests and electrocardiogram (ECG) signals are used to diagnose acute MI. However, for an increase in blood enzyme values, a certain time must pass after the attack. This time lag may delay MI diagnosis. Hence, ECG diagnosis is still very important. Manual ECG interpretation requires expertise and is prone to inter-observer variability. Therefore, computer aided diagnosis may be useful in automatic detection of MI on ECG. In this study, a deep learning model with an end-to-end structure on the standard 12-lead ECG signal for the diagnosis of MI is proposed. For this purpose, the most commonly used technique, convolutional neural network (CNN) is used. Our trained CNN model with the proposed architecture yielded impressive accuracy and sensitivity performance over 99.00% for MI diagnosis on all ECG lead signals. Thus, the proposed model has the potential to provide high performance on MI detection which can be used in wearable technologies and intensive care units.
Continuous cuffless blood pressure (BP) monitoring has attracted much interest in finding the ideal treatment of diseases and the prevention of premature death. This paper presents a novel dynamical method, based on pulse transit time (PTT) and photoplethysmogram intensity ratio (PIR), for the continuous cuffless BP estimation. By taking the advantages of both the modeling and the prediction approaches, the proposed framework effectively estimates diastolic BP (DBP), mean BP (BP), and systolic BP (SBP). Adding past states of the cardiopulmonary system as well as present states of the cardiac system to our model caused two main improvements. First, high accuracy of the method in the beat to beat BP estimation. Second, notwithstanding noticeable BP changes, the performance of the model is preserved over time. The experimental setup includes comparative studies on a large, standard dataset. Moreover, the proposed method outperformed the most recent and cited algorithms with improved accuracy.