Wearable Sensor Node for Cardiac Ischemia Detection
Piotr Augustyniak
AGH University of Science and Technology, Krakow, Poland
Keywords: Wearable Sensor Network, Intelligent Sensors, Ischemia.
Abstract: Detection of cardiac ischemia based on early repolarization ECG markers has been widely recognized since
three decades. It is also employed as a safety marker in exercise testing and cardiac rehabilitation. It
assumes a standardized load applied to a patient in laboratory conditions, which diminishes patients’
responsiveness and lowers the medical outcome statistics. A remedy consists in detecting ischemia markers
during daily living activities in context of instantaneous physical load. Patients more willingly participate in
diagnostics or rehabilitation, but the procedure requires specialized sensors and processors of ECG
dedicated to use in domestic conditions. To this point we designed a small and lightweight autonomous two-
lead ECG sensor node including heart rate and ST-segment processing algorithm and secure Bluetooth Low
Energy connectivity to a supervising Wearable Sensor Network. In laboratory exercise tests with 50
ischemic patients the sensor issued alerts well coinciding with the output of a standard 12-lead system (with
2 fp and 1 fn cases). Moreover, in a two-lead setup the electrodes can be randomly applied to the skin in
location best corresponding the ischemic region. Low power-oriented design and low transmission duty
cycle result in continuous operation of the sensor for over a month with one coin battery.
Recent developments of electronic and data
communication technologies enable to build smart
wearable devices for various medical purposes. This
allows to shift several diagnostic or therapeutic
procedures from hospitals to homes of patients
increasing their comfort and responsiveness. Such
intelligent sensors are organized in wireless sensor
networks (WSN) applied directly on the human body
(therefore called body sensor networks, BSN) or in
the living premises creating active ecosystems for
assisted living (e.g. Hao and Foster, 2008;
Figueiredo et al., 2010; Sahoo et al., 2017). This
idea has been applied far beyond the traditional
areas of medical interest and helps children, elderly,
danger-exposed professionals etc. to supervise their
Coronary arteries provided the oxygenated blood
to the heart muscle and cells of the conduction
system, thus deficient oxygenation may result in
arrhythmia, conduction blocks, ectopic beats or
acute infarct and partial necrosis of the heart muscle.
Two more facts should be noted here: (1) although
ground-truth ischemia markers require biochemical
blood analysis, its influence to the early
repolarization stage allow for reliable detection
based on ST segment in the electrocardiogram
(O’Gara et al., 2013) and (2) in most cases ischemia
is localized in heart muscle regions depending on the
topology of inefficient coronary vessels. Therefore
in a regular 12-lead exercise system, in one person
the ST-segment changes dominate in V1, V2 and in
another person in V5, V6 precordial leads depending
on localization of atherosclerosis (Menown et al.,
The need for reliable instrumentation for a daily
life condition exercise test or cardiac rehabilitation
motivated us to design an autonomous ischemia
sensor cooperating as a node of WSN with
accelerometers, posture and pressure sensors
estimating the physical load. The network is
supervised by a wearable server accompanying the
patient during the outdoor activity or by a stand-
alone hub embedded to the infrastructure of the
patient premise (e.g. dormitory). An example of
universal wearable biosignal recorder with a
Bluetooth-extensible WSN was presented in our
previous paper (Augustyniak, 2016), but the
ischemia detector is expected to work correctly with
any device (e.g. a smartphone) with complying
communication protocol.
Augustyniak, P.
Wearable Sensor Node for Cardiac Ischemia Detection.
DOI: 10.5220/0006544400970104
In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS, pages 97-104
ISBN: 978-989-758-279-0
Copyright © 2018 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Usually ischemia alerts are defined as ST-
segment amplitude crossing of arbitrarily given
thresholds, and therefore criticized for their
dependence on subject fatness. Due to the poorer
propagation of heart-related electrical phenomena to
the body surface, obese people in general show
lower ECG amplitudes and the ischemia signs are
underrepresented in amplitude measurements of ST-
segment. The other concern is the adjustment of the
ST-segment length with heart rate changes. In
exercise and daily living ECG recordings the heart
rate varies from 50 to 180 in healthy adults, so
various correction methods are implemented in the
interpretive software. Bazett formula (Bazett, 1920)
is one of most frequently referred, but some
cardiologist believe it is not equally suitable for all
subjects. Accordingly to the paradigm of
personalized medicine our ischemia sensor allows
for parametrization of several on-board signal
processing factors (e.g. threshold values or
correction method) by a supervising device through
the wireless communication channel.
The prototype wearable sensor node for ischemia
detection was designed and build for exploration of
new diagnostic purposes and usage scenarios it may
offer for cardiology. This paper presents the
prototype, while industrial research are continued on
the target circuitry, processing platform and form
factor. The prototype was designed as an
autonomous device identified by a supervising
device and working as a node of sensor network.
This device has not been presented in this paper, we
simply assume that it uses a compatible Bluetooth
Low Energy (BLE) protocol, is able to identify,
initialize and query the node and collects the
received data packets. The supervising device may
be wearable or stationary and may equally manage
other sensors within the network.
The design of prototype ischemia detector
needed will be presented in subsequent sections in
three main engineering aspects: the recording and
processing hardware, the processing algorithm and
the communication module.
2.1 Recording and Processing
We based on experience gathered in our previous
project on wearable wireless heart rate monitor for
continuous long-term variability studies
(Augustyniak, 2011). The prototype wearable
monitoring device (fig. 1) is designed as lightweight
(7.3 g) and reduced in size (below 1 sq inch). With
these physical properties the device It is based on
the ARM7 family processor (Atmel
AT91SAM7X256), running at 18 MHz due to very
low power consumption (0.5 mW). It is powered by
a replaceable 3.0 V Li-Manganese Dioxide coin
battery (CR2032, 220 mAh, diameter 20 mm, weight
2.5 g) allowing for 940 hours (i.e. 39 days) of
continuous work. A single-channel low-power
(0.2 mW) analog front-end was custom-built and
verified for compliance with a typical ECG
measurement specification (full range ±12 mV,
noise 1V RMS, k
= 85V/V). Further reduction of
the footprint and the power consumption down to
0.2 W is possible with the use of custom-designed
analog or mixed-signal chip manufactured in 0.18 -
0.35 m CMOS technology (Liu et al., 2010; Tseng
et al., 2012; Teng et al., 2014). These options are
considered for further development of the prototype
to a commercial product. Besides the battery life, the
limitation of long time electrophysiological
recordings is the stability of skin-electrode contact,
which currently allows for two weeks continuous
data capture (Weder et al., 2015). Alternative
approaches use capacitive coupling for ECG
recording (Ueno et al, 2007), but for maintaining
low frequency throughput required for ST-segment
diagnostics large electrode size is required.
The ECG is acquired with the processor built-in
sigma/delta analog to digital converter at a sampling
frequency of 1000 sps and 16 bits resolution. a short
buffer of memory stores approximately 4 seconds of
signal in case of the algorithm requires reprocessing
the raw signal (e.g. search back procedure when no
heart beat detected for more than 2 s.). In a service
mode, the prototype enables a direct digital input
data stream and yields the raw digital ECG signal at
internal connector pins. Otherwise, the recorded
ECG is abandoned once the ST-segment calculations
are completed.
We tried to minimize the size of the prototype to
test its properties as a device worn at everyday living
activities. Twin printed circuit boards with analog
and digital circuitry include the battery and the BLE
antenna. One electrode snap is directly embedded
into the board and, besides the electric contact, used
to hold the device attached to the skin, while the
other is mounted at the end of a 12 cm wire (fig. 1).
BIOSIGNALS 2018 - 11th International Conference on Bio-inspired Systems and Signal Processing
Figure 1: Circuitry of a prototype wearable sensor for
ischemia detection.
2.2 Processing Algorithm
The ECG processing algorithm works in real time
regime. A regular delay between the QRS
occurrence and the availability of heart beat
description is 1.6 s and can occasionally be extended
to 3.6 s in case of search back for a missed heartbeat.
The ECG waveform needs a specific processing
to yield the annotated heart beats positions and ST-
segment description (fig. 2). Single-lead
electrocardiogram is sufficient to provide reliable
temporal markers and therough information about
the origin for each heartbeat. Since ectopic beats
propagate through alternative conduction pathway,
only sinoatrial beats are considered for detection of
ST-segment-based ischemia markers. The
processing algorithm consists of QRS detection,
morphology detection and ST-segment processing
procedures presented in subsequent sections.
Figure 2: Block diagram of on-board processing software
for ischemia detection.
2.2.1 Heartbeat Detection
Although the ECG is acquired with 1ksps, a
subsampled (decimated) version of 250 sps is used
for heart beat detection in real time. This procedure
first calculates a detection function with the use of
signal filtering and mathematical transformations
favouring the features common for the QRS
complex accordingly to a modified Pan-Tompkins
algorithm (Pan and Tompkins, 1985). Although long
filter buffers result in significant delay in QRS
detection, we prefer this classical approach for its
very low computational complexity.
Next, an adaptive threshold is applied to
determine the rough position of each QRS section.
In case no QRS is detected in more than 2 s (i.e. 500
samples) a search-back procedure is employed to
scan again the detection function with a lower
threshold value. The search back helps finding small
supraventricular beats following a pair or series of
high ectopic beats.
The precise localization of the R wave peak is
further refined to 1 ms with the use of five points-
based parabola fitting (Augustyniak, 1999). Since
main QRS components fall far below 125 Hz, this
approach is more robust to the noise and determines
the QRS more precisely than a direct Pan-Tompkins
algorithm applied to a 1ksps ECG time series.
Additionally, the average difference between the
parabola samples and corresponding ECG samples is
taken as a measure of recorded noise. Excess of a
programmed noise threshold activates the noise flag
and deactivates other ST-segment-related alerts as
2.2.2 Morphology Detection
The on-board ECG morphology detection is
restricted to the set of features necessary for
supraventricular beats identification (de Chazal et
al., 2004). The classification procedure distinguishes
normal sinus beats (N) from arrhythmic beats (V,
others, artefacts) and, following the power economy,
is organized in two stages.
First, the rhythm stability is assessed by beat-to-
beat comparison of two features: the difference of
signal sections isolated in the ±100 ms (i.e. 200
samples) vicinity of consecutive R wave peak and
the difference of RR-interval. Next, if both values
fall below the respective thresholds, the beat origin
attribute is copied from the precedent beat. Large
differences in beat shapes or RR intervals indicate
the possible occurrence of other morphologies that
need to be excluded from ST-segment analysis. The
value of threshold is initially set to 10% but may be
modified in sensor initialization stage.
Only for beats with over threshold difference,
signal-based geometrical features selected as the
Wearable Sensor Node for Cardiac Ischemia Detection
most discriminative for atrial and ventricular beats
(the surface to perimeter ratio and the count of
samples of over-threshold speed) are calculated and
contribute to the final decision about the beat origin
attribute (Augustyniak, 1997). The morphology
detector is tuned to restrictively eliminate abnormal
heartbeats (prefers false negative cases). Erroneous
inclusion of a single ectopic beat leads to false ST-
segment measurements and probable false ischemia
alerts. Erroneous exclusion of a true sinus beat, if
occurs occasionally, is delaying but not otherwise
influencing the ischemia detection.
2.2.3 ST-segment Processing
The ST segment is identified as following the R
wave by 60 ms (J-point) and lasting for 80 ms at a
heart rate of 60 beats per minute (Jensen et al.,
2005). As the duration of repolarization phase varies
with the hearth rhythm change, several mathematical
formulas (known as Bazett, Fredericia, Framingham
and Hodges) have been proposed for correction of
QT interval length (Funck-Brentano and Jaillon,
1993). Since the ST-segment is a part of QT interval,
the correction is also applicable for determining ST-
segment border points at which amplitudes are
measured and taken as ischemia markers. All above
correction formulas were implemented in the on-
board software of ischemia sensor. Two
configuration bytes are used for correction
management: one defines the R-to-J distance (fig. 3)
and the other selects the length correction method.
Figure 3: Definitions of baseline (BL) and ST-segment
beginning (J) points for amplitude measurement used in
ischemia markers detection.
Two variables are calculated on ST-segment: the
elevation/depression and the slope. Flat,
downsloping, or depressed ST segment may indicate
coronary ischemia. ST elevation may indicate
transmural myocardial infarction, while ST
depression may be associated with subendocardial
myocardial infarction or other serious heart failures.
The ST elevation/depression is measured as
difference of average values of ECG signal samples
between ST-segment border points and average
values of ECG signal samples on baseline preceding
the QRS complex. The positive value of difference
is called ‘elevation’, while the negative is called
The ST-segment slope is measured as the slope
coefficient of the straight line best fitted to ECG
signal samples between ST-segment border points
(fig. 4). The slope can be either positive (upwards)
or negative (downwards).
Figure 4: Definitions of ischemia markers: a) elevation/
depression, b) slope.
Although the adopted definitions originate from
medical recommendations, we cannot implement a
single borderline value between normal and
abnormal results as it is common in 12-lead exercise
ECG systems (usually 100 µV for ST elevation). In
a single-lead ischemia sensor with not defined
electrode positions, the threshold values for ST-
segment elevation and slopes are then programmed
at the configuration stage. Depending on electrode
position the elevation may turn to depression and the
slope direction may change (Jiang et al., 2009).
2.3 Communication Module
The ischemia sensor is dedicated to work as a slave
device in a BSN organized with Bluetooth Low
Energy (4.0) technology using either a peer-to-peer
connection, either a star topology. The supervising
device initializes, controls and reads all sensors in
the network accordingly to the programmed
schedule or to the occurrence of ST events.
The communication begins with short data
packets allowing to identify and initialize the sensor
and establish a short time secure channel to transmit
the configuration and long term key. The
initialization procedure is performed repeatedly for
verification of the sensor status (e.g. battery level),
time synchronization or key update. Connection
setup and data transfer are completed within 3 ms,
allowing the supervising device to check and
BIOSIGNALS 2018 - 11th International Conference on Bio-inspired Systems and Signal Processing
synchronize the ischemia sensor in short time and at
a cost of very low energy.
In a typical exercise test ST-segment properties
change in response to the physical load thus the
output data are transmitted in synchronous packets at
the end of programmed reporting interval (1 256 s)
or asynchronously at each heartbeat interval. The
continuous mode or immediate reporting is not
economical, but exercise test are usually performed
in laboratory condition and last for 30 minutes.
In a typical rehabilitation or surveillance
scenario, a heartbeat interval (RR) should be kept
within a specified range. Excess of the RR range
borders or ST elevation/depression measure is
detected by on-board software and triggers
occasional asynchronous reporting. In this mode
connection duty cycle is very low improving data
security and power saving. This is particularly useful
for everyday living conditions.
The configuration packet has a length of 32 bytes
consists of:
two configuration bytes for heartbeat and
morphology detectors,
two configuration bytes for ST-segment
alignment and length correction,
four bytes of HR and ST thresholds setup,
two bytes for reporting mode setup,
twenty bytes of long term security key.
two spare bytes for further sensor
The report packet has a length of 8 bytes and
consists of:
two bytes of sensor status report,
four bytes of diagnostic data: RR-interval,
ST-segment elevation and slope,
one byte of actual ST segment length,
one byte of threshold excess flags.
The BLE uses modern communication
technology and security-oriented transmission
protocols. The security of low-energy transmission
within the BSN is based on AES-CCM encryption
algorithm with exclusive long time keys passed from
supervising device to each remote sensor via
individual temporary secure channels. A short term
key for the temporary channel is a string of 6
numeric digits generated uniquely for each pairing
(Padgette et al., 2017). Since the sensor
configuration session occurs randomly and lasts for
only few milliseconds, the man-in-the-middle
attacks have little chance to success.
3.1 Testing Conditions
The prototype was tested in three laboratory
conditions (referred to by their numbers):
1. with arbitrary waveform generator replaying the
pre-recorded real exercise ECG of known HR
and ST-segment results,
2. with cardiac patients undergoing a regular stress
test diagnosis for possible cardiac ischemia as
accompanying device,
3. with three human volunteers performing
everyday living activities including physical
training (jogging) or work (gardening).
In all experiments the sensor was supervised by a
smartphone with purpose-built application in peer-
to-peer mode. In experiment 2 the smartphone clock
was synchronized with the exercise test system.
Sensor-originated medical measurements were
stored in the smartphone memory (microSD card)
for further analysis.
Experiment 1 was performed with 30 half-hour
records made with commercial stress test system (by
Aspel) during a Bruce protocol treadmill test (Bruce,
1974). Recorded full disclosure 12-lead
electrocardiograms were first replayed digitally to
validate the correctness of sensor on-board ECG
interpretation procedures (fig. 2). Once the
validation was successful, we replayed the signals
again through the digital to analog converter and fed
to the bipolar inputs of the sensor in three various
combinations: V1-V3, V4-V6 and V2-V5.
Consequently, we had 90 half hour test signals with
Experiment 2 was performed during 20 regular
half-hour stress test diagnoses in patients suspected
for possible cardiac ischemia. Diagnostic records
were made with commercial stress test system
during a Bruce protocol treadmill test. The
electrodes of the tested prototype were placed in two
irregular locations orthogonal to the chest leads line:
symmetrically 5 cm over and under V2 and
symmetrically 5 cm over and under V5 lead. In this
experiment we tested the difference of ischemia
detection between the proposed wearable sensor and
the commercial exercise system.
Experiment 3 was performed in three young
volunteers wearing the sensor for 72h each during
everyday living activities including physical training
(jogging) or work (gardening). The recorded time
series of HR and ST measurements were compared
Wearable Sensor Node for Cardiac Ischemia Detection
to the activity record manually made by the subject.
The alert threshold HR was set to 110 bpm, and
amplitude to 50 µV in order to detect exercise
induced ischemia markers in healthy people.
3.2 Testing Results
Values of medical measurements HR and ST
calculated by the sensor in Experiment 1 were
compared to the annotations made by medical staff
on a source 12-lead exercise record. The setup used
raw ECG signals pre-recorded with 12-lead system,
thus it simulated electrode placement in exact
positions of V1-V3, V4-V6 and V2-V5 respectively.
Consequently in each configuration, the results from
the sensor were referred to the respective ECG
channels (tab. 1).
Table 1: Results of Experiment 1 comparing HR and ST
calculated by the sensor to the annotations made by
medical staff on a source 12-lead exercise record.
Amplitude measurement accuracy
1.5 µV (5 LSB)
Average HR difference
0.7 bpm
Standard deviation of HR
1.7 bpm
Average difference of ST
5.0 µV (16 LSB)
Standard deviation of difference of ST
8.2 µV (27 LSB)
Average difference of ST slope
0,03 µV/s
Standard deviation of ST slope
0,12 µV/s
In experiment 2 the sensor was used
simultaneously to commercial exercise test system
during 20 regular half-hour stress test diagnoses. In
eight out of the 20 patients cardiac ischemia was
diagnosed based on the outcome of the commercial
system. The sensor electrodes were positioned in
alternative location to the 12-lead system, thus direct
comparison of amplitudes were not possible.
Consequently, with ST alert threshold set to 100 µV,
we investigated false detection cases and temporal
difference of ischemia detection between the sensor
and the commercial system (tab. 2).
Table 2: False detection and temporal difference of
ischemia detection analysis.
Threshold excess time difference
8 s
Standard deviation of threshold excess
17 s
False positive ischemia detection
False negative ischemia detection
True positive ischemia detection
True negative ischemia detection
In experiment 3 the sensor was used in three
healthy volunteers with no previous ischemia record
to detect the physical exercise events of two types
(jogging and gardening) in their everyday life. As
the exercise manifests itself in slight deviations of
ST-segment measurements, we programmed lower
values of alert thresholds (110 bpm for HR and
50 µV for ST elevation). The sensor electrodes were
positioned at V2-V5 location. The sensor was paired
with a smartphone and the subjects were asked to
always recharge the battery at night (i.e. where no
activity is performed) in order to capture all
exercise-related ST events. During a continuous 72
hour session the subjects were asked to act
accordingly to their regular activity with taking
notes on beginning and ending time (tab. 3).
Table 3: Statistics of physical activity detected by the
sensor and reported by the volunteers.
Jogging events
Average jogging events duration
41 (± 17) min
Jogging start to HR threshold excess time
(average ± STD)
45 (± 18) s
Jogging start to ST elevation threshold
excess time (average ± STD)
170 (± 51) s
Gardening events (digging)
Average gardening events duration
3.3 (± 4.1) min
Gardening start to HR threshold excess
time (average ± STD)
27 (± 37) s
Gardening start to ST elevation threshold
excess time (average ± STD)
71 (± 54) s
BIOSIGNALS 2018 - 11th International Conference on Bio-inspired Systems and Signal Processing
A small and lightweight intelligent sensor for
ischemia detection has been proposed, designed and
First tests with pre-recorded signals helped to
evaluate the correctness of on-board software
implementation and accuracy of performed
measurements. A detailed review of errors leads to
the conclusion that amplitude measurements
accuracy is of order of 5 LSB (1.5 µV), and values
of ST segment amplitude-related errors greater than
that are due to occasional false positive or false
negative morphology detection. Nevertheless, in
comparison with regular 12-lead exercise test
systems the measurement accuracy of the ischemia
sensor is competitive. The accuracy of ST slope
coefficient cannot be quantitatively compared, since
no known exercise test system provide respective
technical data. Most systems simply state the slope
is going upward (positive coefficient) or downward
(negative coefficient), what is sufficient for medical
In Experiment 2 the sensor was tested
simultaneously to a commercial exercise test system
with several patients, in some of which cardiac
ischemia was diagnosed. Although the wearable
sensor used alternative lead positions, the test
proved acceptable detection accuracy and reasonable
simultaneity of detection. The true accuracy of ECG
exercise stress testing for ischemia detection is only
of about 75% and all ambiguous cases are subject to
further diagnostics based on coronary catheterization
or magnetic resonance imaging. Since no such
verification was made in our case, the false detection
cases may be interpreted in reference to a
commercial stress test system, and not in reference
to a ground-truth medical diagnosis of cardiac
Third experiment showed that the cardiac
ischemia sensor is a truly wearable device, even if
the prototype is too large and cumbersome for
unobtrusive operation in daily living conditions. The
accurate detection of physical load with the heart
rate is not surprising, but, what is more relevant in
our case, can also be reliably done with change of
ST parameters. What is most important in our case,
the ST segment changes may be analysed in context
of the heart rate, what enabled detection of ECG
ischemia markers during random physical activity
taken by the subject. Moreover, during this test the
sensor worked in asynchronous reporting mode,
where the excess of the RR or ST
elevation/depression range borders is detected by
on-board software and triggers occasional reporting.
Average power consumption with twelve short
communication sessions a day was estimated to
0.8 mW, what grants a month of autonomous
operation without battery replacement.
The presented study has several limitations: only
one prototype was build, simplified algorithms were
used for heart beats detection and morphology
classification, the number of patients was small, and
the number of those with ischemia was even smaller.
Therefore, we consider our work as a proof of
concept and are looking forward to build an
industrial prototype and test it in clinics with true
ischemia reference. This would reveal one of the
expected advantage of the sensor which consists in
unrestricted electrode positions.
The performance of proposed wearable system is
comparable to a standard 12-lead exercise test
system. Thanks to reduced weight and size and
prolonged autonomy its application area extends to
everyday activity detection and risk stratification in
home monitoring. With a wearable ischemia sensor,
the cardiologists may extend the scope of their
cardiac surveillance get better responsiveness from
their patients and optimize the monitoring
accordingly to patients’ medical history (e.g.
localization of the infarct).
This work is supported by the National Centre for
Research and Development (NCBiR) under Grant
No. STRATEGMED/269043/20/NCBR/2017
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BIOSIGNALS 2018 - 11th International Conference on Bio-inspired Systems and Signal Processing