Device Technique For Cardiac Risk Health And Social Care Essay

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Electrocardiographic Waveforms Fitness Check
Device Technique for Cardiac Risk Screening
Omar J Escalona 1, IEEE Member, Marianela Mendoza 2
1 School of Engineering, University of Ulster, Newtownabbey, United Kingdom
2 Universidad Sim�n Bol�var, Valle de Sartenejas, Caracas, Venezuela
1 [email protected]
2 [email protected]
Abstract - A novel cardiac health device technique development
for reliable, non-invasive and cost-effective heart screening in
preventive cardiovascular healthcare is presented. Three major
causes of mortality are addressed: identification of apparently
healthy individuals involved in sports activities (particularly in
the young, age < 35 years) who may be at-risk of sudden-cardiac-
death (SCD), cardiovascular abnormalities in children and
adolescents with type1-diabetes, and in detecting patients with
Brugada syndrome. The device system has been aimed to provide
a single figure diagnostic output, thus, not requiring highly-
skilled medical personnel. The principles of the required ECG-
waveform analysis algorithm have been reported in previous
clinical studies. A prototype system platform design that will
enable low-cost, portability and key user-friendly characteristics
was implemented and in-vitro tested. Real-time firmware
integrity and cardiac fitness detection algorithm performed
reliably with an in-vitro positive SCD ECG-waveform modelling
technique.
Keywords: SAECG, Cardiac screening, sports risk detection, sudden
cardiac death, SCD, cardiovascular dibetology, Brugada syndrome,
VLP, ventricular late potentials.
I. INTRODUCTION
Sudden cardiac death (SCD) is a major health problem in
Europe. While there are difficulties in estimating the exact
incidence, in the Maastricht study that showed that during the
1990s in the Netherlands, 45% of SCD sufferers had no
known history of cardiac disease and 40% had low or medium
risk profiles following a heart attack. This study showed that
the incidence of SCD was almost exactly one per 1000
patient-years. It also showed that men are more than twice as
likely as women to suffer sudden cardiac death, that it usually
happens when the patient is at home�often when they are
asleep�and although resuscitation is attempted in about 50%
of cases, it succeeds in only about 6% of them. Victims are
often young (< 35 years of age) otherwise healthy individuals
[1]. Unrecognised inherited electrophysiological abnormalities
are a common underlying cause for many of these deaths [2].
Any public initiative of large-scale personalised health
scheme attending these individuals with inherited conditions,
would require efficient and reliable screening techniques and
devices for rapid and accurate detection, risk stratification and
categorisation, in order to lead specific therapeutic strategies
to prevent SCD in at-risk individuals, including implantation
of cardioverter defibrillators (ICD) [3]. To date, the diagnosis
of these conditions remains a vexing challenge for clinicians
because either these conditions are not commonly identifiable
with standard clinical evaluation, or new techniques are linked
to a high level of expertise, which constitutes a hindrance to
their ample adoption by clinicians. These combined factors
contribute to a limited reach of potential health benefits to the
affected population. It would be a significant advance if a
method was devised capable of rapidly identifying a greater
proportion of at risk individuals. An innovative approach
could be by embedding within the related device technology
the expertise required for the novel method. In this way,
clinician training necessary for handling and interpreting new
health devices using advanced concepts, can be minimised or
not required at all. Thus benefits offered by improved but
sophisticated screening techniques are facilitated more quickly
and efficiently into medical practice, leading to significant
reduction on the frequency of SCD.
Heart fitness screening techniques based on family history
and personal symptom questionnaires alone are insufficient to
identify people with diseases associated with SCD [2]. Large-
scale electrocardiographic screening of young athletes has
been shown to reduce the incidence of sudden cardiac death in
Italy [4]. An electrocardiogram (ECG) is the standard method
used in screening programmes; however it is capable of
identifying a limited number of cardiac abnormalities, and
lacks specificity and sensitivity [5]. Thus, an innovative
cardiac screening technology that is accurate, portable and
cost effective would constitute a significant advance in this
area. Therefore, we aim to provide a medical device
technology that is user friendly, portable, and cost-effective
for cardiac screening using non-invasive ECG complex
waveform analysis to achieve the required reliability of the
cardiac risk screening procedure. Also, it would be ideal if the
targeted device technique can minimise clinician training
requirements by embedding within the device the required
knowledge and expertise for the interpretation of ECG
waveform complexity analysis, by providing a single
diagnostic output figure, using already proven concepts [6]
and recent in-house developments on such a device [7].
Electrocardiographic Waveforms Fitness Check
Device Technique for Cardiac Risk Screening
Omar J Escalona 1, IEEE Member, Marianela Mendoza 2
1 School of Engineering, University of Ulster, Newtownabbey, United Kingdom
2 Universidad Sim�n Bol�var, Valle de Sartenejas, Caracas, Venezuela
1 [email protected]
2 [email protected]
Abstract - A novel cardiac health device technique development
for reliable, non-invasive and cost-effective heart screening in
preventive cardiovascular healthcare is presented. Three major
causes of mortality are addressed: identification of apparently
healthy individuals involved in sports activities (particularly in
the young, age < 35 years) who may be at-risk of sudden-cardiac-
death (SCD), cardiovascular abnormalities in children and
adolescents with type1-diabetes, and in detecting patients with
Brugada syndrome. The device system has been aimed to provide
a single figure diagnostic output, thus, not requiring highly-
skilled medical personnel. The principles of the required ECG-
waveform analysis algorithm have been reported in previous
clinical studies. A prototype system platform design that will
enable low-cost, portability and key user-friendly characteristics
was implemented and in-vitro tested. Real-time firmware
integrity and cardiac fitness detection algorithm performed
reliably with an in-vitro positive SCD ECG-waveform modelling
technique.
Keywords: SAECG, Cardiac screening, sports risk detection, sudden
cardiac death, SCD, cardiovascular dibetology, Brugada syndrome,
VLP, ventricular late potentials.
I. INTRODUCTION
Sudden cardiac death (SCD) is a major health problem in
Europe. While there are difficulties in estimating the exact
incidence, in the Maastricht study that showed that during the
1990s in the Netherlands, 45% of SCD sufferers had no
known history of cardiac disease and 40% had low or medium
risk profiles following a heart attack. This study showed that
the incidence of SCD was almost exactly one per 1000
patient-years. It also showed that men are more than twice as
likely as women to suffer sudden cardiac death, that it usually
happens when the patient is at home�often when they are
asleep�and although resuscitation is attempted in about 50%
of cases, it succeeds in only about 6% of them. Victims are
often young (< 35 years of age) otherwise healthy individuals
[1]. Unrecognised inherited electrophysiological abnormalities
are a common underlying cause for many of these deaths [2].
Any public initiative of large-scale personalised health
scheme attending these individuals with inherited conditions,
would require efficient and reliable screening techniques and
devices for rapid and accurate detection, risk stratification and
categorisation, in order to lead specific therapeutic strategies
to prevent SCD in at-risk individuals, including implantation
of cardioverter defibrillators (ICD) [3]. To date, the diagnosis
of these conditions remains a vexing challenge for clinicians
because either these conditions are not commonly identifiable
with standard clinical evaluation, or new techniques are linked
to a high level of expertise, which constitutes a hindrance to
their ample adoption by clinicians. These combined factors
contribute to a limited reach of potential health benefits to the
affected population. It would be a significant advance if a
method was devised capable of rapidly identifying a greater
proportion of at risk individuals. An innovative approach
could be by embedding within the related device technology
the expertise required for the novel method. In this way,
clinician training necessary for handling and interpreting new
health devices using advanced concepts, can be minimised or
not required at all. Thus benefits offered by improved but
sophisticated screening techniques are facilitated more quickly
and efficiently into medical practice, leading to significant
reduction on the frequency of SCD.
Heart fitness screening techniques based on family history
and personal symptom questionnaires alone are insufficient to
identify people with diseases associated with SCD [2]. Large-
scale electrocardiographic screening of young athletes has
been shown to reduce the incidence of sudden cardiac death in
Italy [4]. An electrocardiogram (ECG) is the standard method
used in screening programmes; however it is capable of
identifying a limited number of cardiac abnormalities, and
lacks specificity and sensitivity [5]. Thus, an innovative
cardiac screening technology that is accurate, portable and
cost effective would constitute a significant advance in this
area. Therefore, we aim to provide a medical device
technology that is user friendly, portable, and cost-effective
for cardiac screening using non-invasive ECG complex
waveform analysis to achieve the required reliability of the
cardiac risk screening procedure. Also, it would be ideal if the
targeted device technique can minimise clinician training
requirements by embedding within the device the required
knowledge and expertise for the interpretation of ECG
waveform complexity analysis, by providing a single
diagnostic output figure, using already proven concepts [6]
and recent in-house developments on such a device [7].
For Peer Review OnlyEUROCON 2013
A. State of the Art and the Clinical Pull
Globally, SCD is currently a major cause of mortality in all
developed countries [8]. According to the most recent report
of the European Society of Cardiology's Task Force on SCD,
the implementation of novel and effective risk stratification
and of therapies known to reduce the risk of SCD has been
slow and inconsistent. Much more work is needed and
expected in larger populations with less or no apparent heart
disease. Accurate identification and personalised treatment of
these subjects leads to a very substantial reduction in SCD in
the screened population [4].
In addition to a comprehensive medical history and clinical
examination the conventional cardiac screening procedure
includes a 12-Lead ECG recording. The latter records the
sequential electrical activation of the cardiac chambers.
Disposable self adhesive ECG electrodes are placed on the
subject�s limbs and chest wall, and then connected to an ECG
recording device. The heart�s electrical activity is thus
obtained for evaluation by a cardiologist. In selected cases a
cardiac ultrasound (echocardiogram) is also performed. This
evaluates cardiac chamber size, valvular abnormalities and
cardiac contractile function [4]. Nevertheless, the low
incidence of anomalies makes screening not very cost
effective, although one study has suggested that ECG
screening is more cost effective than echocardiographic
screening [8].
B. Rationale and Hypothesis
For our device development approach, it was hypothesised
that the main drawback of conventional heart screening
techniques, for accurate detecting individuals at-risk of SCD,
is the expertise dependency nature of them. Although the
complete medical equipment required in conventional
techniques may be affordable (around � 8k), the number of
people being screened would be strongly limited by the
number of cardiologists or specialised clinical physiologists
(highly trained clinical staff) available/working in the
screening programme, and not by the number of available
heart screening machines and low-skilled personnel. In
contrast, the proposed ECG waveform fitness check
(ECGWFC) device, is targeted to be operator independent.
The device will just provide a single measurement figure (a
dimensionless number) result output per person being checked.
Therefore, the number of people being heart screened can be
increased only by increasing the number of ECGWFC units
and the number of unspecialised medical personnel, such as
nurses or paramedics, who could be easily trained for
operating the ECGWFC device and placing the required ECG
electrodes (only 6 electrodes). Therefore, increasing the
feasibility of the programme and reducing its operating costs.
The estimated cost of an ECGWFC device, is estimated to be
approximately � 5k, with negligible maintenance costs. The
proposed ECGWFC device is in response to recent guidelines
which have highlighted the need to develop novel tools in
order to identify patients at highest risk of ventricular
arrhythmias and SCD [8]. According to these guidelines,
numerous modalities exist at present for assessing this risk but
only two are currently approved by the U.S. Food and Drug
Administration (FDA): Signal Averaged ECG (SAECG) and
T wave alternans (TWA). The proposed ECGWFC device
concept is based on the SAECG modality. The original main
patent linked to the invention of the basic algorithms for the
interpretation of the 3-dimensional (3D) SAECG heart
waveforms was assigned to the British Technology Group Ltd
by University of Ulster and Prof O Escalona [6]. Currently
this patent is in the public domain. Further commodity
contributions would be mainly based on the novel embedded
system techniques and firmware design that will enable the
proposed ECGWFC device to deliver its advantages for the
medical practice.
II. METHODS
The key medical technology involved in the heart fitness
check device comprises a technique of analysis of ventricular
late potentials (VLP) measurements in a high-resolution
electrocardiographic recording using a unique ECG signal
averaging (SAECG) process, named SFP (single fiducial
point) [7]. SAECG improves the signal-to-noise ratio of a
surface ECG, permitting the identification of VLP, which are
low-amplitude (microvolt level) signals at the end of the
ventricular activity. VLPs result from regions of abnormal
myocardium demonstrating slow conduction, a pathological
condition that may favour reentrant ventricular arrhythmias,
and their waveform analysis and quantification can provide a
marker for the presence of an electrophysiological substrate
for reentrant ventricular tachyarrhythmias [9]. In a related
context, other causes of sudden death in subjects with
apparently normal hearts, are known to be associated with
arrhythmogenic right ventricular cardiomyopathy (ARVC)
and myocarditis, according to studies which have included
SAECG methods [10 - 13].
SAECG techniques have been studied by our
cardiovascular engineering research group at University of
Ulster, since 1990 [14]. Our successful research has lead to
novel and reliable cardiac risk detection techniques [6].
Furthermore, use of these techniques can be readily extended
for detecting diabetic cardiovascular complications in children
and adolescents with type-1 diabetes [15, 16]. Also, major
depression in diabetic patients has been associated with an
increased number of known cardiac risk factors, and systems
of care that integrate diagnosis and treatment of major
depression into medical management of diabetes may be
needed for particular patients, in order to lower cardiac risks
and complications [17]. Thus, in this arm of applications, the
ECGWFC device can provide a useful tool to facilitate
personalised medicine. More recently, SAECG methods have
provided non-invasive means for detecting at-risk patients
with Brugada syndrome (BS) [18]. BS is associated with a
high risk for sudden cardiac death in young and otherwise
healthy adults, and less frequently in infants and children [19].
Patients with a spontaneously appearing Brugada ECG have a
high risk for sudden arrhythmic death secondary to ventricular
tachycardia/fibrillation. BS accounts for approximately 20%
A. State of the Art and the Clinical Pull
Globally, SCD is currently a major cause of mortality in all
developed countries [8]. According to the most recent report
of the European Society of Cardiology's Task Force on SCD,
the implementation of novel and effective risk stratification
and of therapies known to reduce the risk of SCD has been
slow and inconsistent. Much more work is needed and
expected in larger populations with less or no apparent heart
disease. Accurate identification and personalised treatment of
these subjects leads to a very substantial reduction in SCD in
the screened population [4].
In addition to a comprehensive medical history and clinical
examination the conventional cardiac screening procedure
includes a 12-Lead ECG recording. The latter records the
sequential electrical activation of the cardiac chambers.
Disposable self adhesive ECG electrodes are placed on the
subject�s limbs and chest wall, and then connected to an ECG
recording device. The heart�s electrical activity is thus
obtained for evaluation by a cardiologist. In selected cases a
cardiac ultrasound (echocardiogram) is also performed. This
evaluates cardiac chamber size, valvular abnormalities and
cardiac contractile function [4]. Nevertheless, the low
incidence of anomalies makes screening not very cost
effective, although one study has suggested that ECG
screening is more cost effective than echocardiographic
screening [8].
B. Rationale and Hypothesis
For our device development approach, it was hypothesised
that the main drawback of conventional heart screening
techniques, for accurate detecting individuals at-risk of SCD,
is the expertise dependency nature of them. Although the
complete medical equipment required in conventional
techniques may be affordable (around � 8k), the number of
people being screened would be strongly limited by the
number of cardiologists or specialised clinical physiologists
(highly trained clinical staff) available/working in the
screening programme, and not by the number of available
heart screening machines and low-skilled personnel. In
contrast, the proposed ECG waveform fitness check
(ECGWFC) device, is targeted to be operator independent.
The device will just provide a single measurement figure (a
dimensionless number) result output per person being checked.
Therefore, the number of people being heart screened can be
increased only by increasing the number of ECGWFC units
and the number of unspecialised medical personnel, such as
nurses or paramedics, who could be easily trained for
operating the ECGWFC device and placing the required ECG
electrodes (only 6 electrodes). Therefore, increasing the
feasibility of the programme and reducing its operating costs.
The estimated cost of an ECGWFC device, is estimated to be
approximately � 5k, with negligible maintenance costs. The
proposed ECGWFC device is in response to recent guidelines
which have highlighted the need to develop novel tools in
order to identify patients at highest risk of ventricular
arrhythmias and SCD [8]. According to these guidelines,
numerous modalities exist at present for assessing this risk but
only two are currently approved by the U.S. Food and Drug
Administration (FDA): Signal Averaged ECG (SAECG) and
T wave alternans (TWA). The proposed ECGWFC device
concept is based on the SAECG modality. The original main
patent linked to the invention of the basic algorithms for the
interpretation of the 3-dimensional (3D) SAECG heart
waveforms was assigned to the British Technology Group Ltd
by University of Ulster and Prof O Escalona [6]. Currently
this patent is in the public domain. Further commodity
contributions would be mainly based on the novel embedded
system techniques and firmware design that will enable the
proposed ECGWFC device to deliver its advantages for the
medical practice.
II. METHODS
The key medical technology involved in the heart fitness
check device comprises a technique of analysis of ventricular
late potentials (VLP) measurements in a high-resolution
electrocardiographic recording using a unique ECG signal
averaging (SAECG) process, named SFP (single fiducial
point) [7]. SAECG improves the signal-to-noise ratio of a
surface ECG, permitting the identification of VLP, which are
low-amplitude (microvolt level) signals at the end of the
ventricular activity. VLPs result from regions of abnormal
myocardium demonstrating slow conduction, a pathological
condition that may favour reentrant ventricular arrhythmias,
and their waveform analysis and quantification can provide a
marker for the presence of an electrophysiological substrate
for reentrant ventricular tachyarrhythmias [9]. In a related
context, other causes of sudden death in subjects with
apparently normal hearts, are known to be associated with
arrhythmogenic right ventricular cardiomyopathy (ARVC)
and myocarditis, according to studies which have included
SAECG methods [10 - 13].
SAECG techniques have been studied by our
cardiovascular engineering research group at University of
Ulster, since 1990 [14]. Our successful research has lead to
novel and reliable cardiac risk detection techniques [6].
Furthermore, use of these techniques can be readily extended
for detecting diabetic cardiovascular complications in children
and adolescents with type-1 diabetes [15, 16]. Also, major
depression in diabetic patients has been associated with an
increased number of known cardiac risk factors, and systems
of care that integrate diagnosis and treatment of major
depression into medical management of diabetes may be
needed for particular patients, in order to lower cardiac risks
and complications [17]. Thus, in this arm of applications, the
ECGWFC device can provide a useful tool to facilitate
personalised medicine. More recently, SAECG methods have
provided non-invasive means for detecting at-risk patients
with Brugada syndrome (BS) [18]. BS is associated with a
high risk for sudden cardiac death in young and otherwise
healthy adults, and less frequently in infants and children [19].
Patients with a spontaneously appearing Brugada ECG have a
high risk for sudden arrhythmic death secondary to ventricular
tachycardia/fibrillation. BS accounts for approximately 20%
For Peer Review OnlyEUROCON 2013
of cases of SCD in patients with structurally normal hearts
[20]. A technology which reveals late potentials could help
identify persons with BS and who would thus be candidates
for electrophysiological study (EPS), which can identify those
at risk of SCD. The ICD is the only therapy known to help
prevent SCD in patients with BS [21].
A particular especial algorithm for VLP analysis quantifies
a parameter related to the complexity of VLP waveforms as
the indicator for risk assessment (VLPd). This is done by
computing the fractal dimension of the 3-dimensional (3D)
VLP curve drawn in a high definition voltage scale (in
microvolts); the voltage being measured on three orthogonal
(X,Y and Z) ECG signals [6]. Several clinical studies have
confirmed that a fractal dimension above 1.3, can be selected
as the threshold value indicating risk of SCD in the subject
being checked for heart fitness [6, 22]. The reported standard
deviation (s) of VLPd in the at-risk groups being about 0.08,
and in the non at-risk groups being about 0.06, with difference
between means ranging about twice those values (�1 - �2 � 2s).
The fractal dimension quantification parameter VLPd may be
provided as a numerical display or just as a simple SCD risk
warning lamp (green/red) on the device, depending on
whether the value of VLPd is below/above a threshold value
(e.g., a value of 1.3). Figure 1 illustrates VLP attractor 3D
trajectories with VLPd values above and below 1.3. The basic
algorithm was clinically tested using off-line processing with
an �unpractical� laboratory personal computer system.
Therefore, investment is now necessary for further
development using state of the art technology to provide a
suitable and novel embedded system that implements the real-
time fractal analysis algorithm, in order for the ECGWFC
device technology to be practical, user friendly and accessible
at low-cost for its inclusion into the clinical practice of any
cardiac department, sports coach and any national heart
screening programme easy way to comply with the conference
paper formatting requirements is to use this document as a
template and simply type your text into it.
(a)
(b)
Figure 1. 3D plots of VLP attractors at microvoltage scale: (a) a healthy
subject: VLPd = 1.163; (b) a patient that clinically is at-risk: VLPd = 1.404.
A. HRECG System Implementation
A real-time HRECG system was implemented for VLP
isolation and VLPd analysis. It is integrated by two main
components: the hardware and the software. The hardware
itself is formed by power supply batteries, indicators, 3-
channel ECG front-end amplification, analogue filters,
analogue to digital converters (ADC), opto-isolation chip-set,
RSR-232 USB converter and a laptop computer. A block
diagram is shown in Fig. 2.
Figure 2. Real-time high-resolution ECG system integration.
The ECG signals are obtained by recording the orthogonal
bipolar XYZ lead system [9].
The ECG amplification section contains a couple of gain
stages with overall gain fixed to 2000. The analogue filter
stage is composed by a first-order, 3Hz high-pass filter and by
an antialiasing fifth-order low-pass filter, set to a high cut off
frequency of fh= 360Hz. The sampling frequency (fs) was set
to 2kHz. Thus, the acquisition front-end has a bandwidth from
3 up to 360Hz, and the dynamic range was set to �10V. 16-
bit resolution ADCs were used in the system and thus the
minimum input voltage change per bit was 305�V. Digital
isolation was fully implemented to protect the following
laptop/PC stage.
Firmware and signal processing were implemented using a
micro-controller. It computed a highly accurate real-time
alignment reference in the QRS complex, used for signal
averaging (SAECG). For this real-time task, coding was
implemented to carry out the Single-Fidutial-Point (SFP)
aligment technique algorithm [14].
Data output for the three ECG channels (XYZ) plus the
QRS alignment reference pulse was sent to the laptop/PC via
the USB-port. Operator console interface and high level
computational processes were implemented at the laptop/PC
stage, using LabVIEW. Real-time ECG display of the three
channels and the QRS reference pulse was provided. The
developed LabVIEW application also processes these four
signals to compute the fractal dimension of the VLP attractor.
This last process involves several steps that are described
below.
B. Filtered SAECG and VLP Isolation
For computing the SAECG frame, the SFP alignment
algorithm [14] was coded in the micro-controller. The
reference channel and the number of beats to be averaged are
decided by the operator. The SAECG was computed for the
three channels (X, Y, Z). As the frequency spectrum of VLP is
Power Supply (Rechargeable Batteries)
3-Channel
ECG
Front-End
Amplifier
Embedded
R-Wave, SFP
Processing &
16-Bit ADC
Opto-
Isolation &
UART-USB
Interface
Laptop PC,
Computing
& Display:
SAECG &
3D Attractor
of VLPs
of cases of SCD in patients with structurally normal hearts
[20]. A technology which reveals late potentials could help
identify persons with BS and who would thus be candidates
for electrophysiological study (EPS), which can identify those
at risk of SCD. The ICD is the only therapy known to help
prevent SCD in patients with BS [21].
A particular especial algorithm for VLP analysis quantifies
a parameter related to the complexity of VLP waveforms as
the indicator for risk assessment (VLPd). This is done by
computing the fractal dimension of the 3-dimensional (3D)
VLP curve drawn in a high definition voltage scale (in
microvolts); the voltage being measured on three orthogonal
(X,Y and Z) ECG signals [6]. Several clinical studies have
confirmed that a fractal dimension above 1.3, can be selected
as the threshold value indicating risk of SCD in the subject
being checked for heart fitness [6, 22]. The reported standard
deviation (s) of VLPd in the at-risk groups being about 0.08,
and in the non at-risk groups being about 0.06, with difference
between means ranging about twice those values (�1 - �2 � 2s).
The fractal dimension quantification parameter VLPd may be
provided as a numerical display or just as a simple SCD risk
warning lamp (green/red) on the device, depending on
whether the value of VLPd is below/above a threshold value
(e.g., a value of 1.3). Figure 1 illustrates VLP attractor 3D
trajectories with VLPd values above and below 1.3. The basic
algorithm was clinically tested using off-line processing with
an �unpractical� laboratory personal computer system.
Therefore, investment is now necessary for further
development using state of the art technology to provide a
suitable and novel embedded system that implements the real-
time fractal analysis algorithm, in order for the ECGWFC
device technology to be practical, user friendly and accessible
at low-cost for its inclusion into the clinical practice of any
cardiac department, sports coach and any national heart
screening programme easy way to comply with the conference
paper formatting requirements is to use this document as a
template and simply type your text into it.
(a)
(b)
Figure 1. 3D plots of VLP attractors at microvoltage scale: (a) a healthy
subject: VLPd = 1.163; (b) a patient that clinically is at-risk: VLPd = 1.404.
A. HRECG System Implementation
A real-time HRECG system was implemented for VLP
isolation and VLPd analysis. It is integrated by two main
components: the hardware and the software. The hardware
itself is formed by power supply batteries, indicators, 3-
channel ECG front-end amplification, analogue filters,
analogue to digital converters (ADC), opto-isolation chip-set,
RSR-232 USB converter and a laptop computer. A block
diagram is shown in Fig. 2.
Figure 2. Real-time high-resolution ECG system integration.
The ECG signals are obtained by recording the orthogonal
bipolar XYZ lead system [9].
The ECG amplification section contains a couple of gain
stages with overall gain fixed to 2000. The analogue filter
stage is composed by a first-order, 3Hz high-pass filter and by
an antialiasing fifth-order low-pass filter, set to a high cut off
frequency of fh= 360Hz. The sampling frequency (fs) was set
to 2kHz. Thus, the acquisition front-end has a bandwidth from
3 up to 360Hz, and the dynamic range was set to �10V. 16-
bit resolution ADCs were used in the system and thus the
minimum input voltage change per bit was 305�V. Digital
isolation was fully implemented to protect the following
laptop/PC stage.
Firmware and signal processing were implemented using a
micro-controller. It computed a highly accurate real-time
alignment reference in the QRS complex, used for signal
averaging (SAECG). For this real-time task, coding was
implemented to carry out the Single-Fidutial-Point (SFP)
aligment technique algorithm [14].
Data output for the three ECG channels (XYZ) plus the
QRS alignment reference pulse was sent to the laptop/PC via
the USB-port. Operator console interface and high level
computational processes were implemented at the laptop/PC
stage, using LabVIEW. Real-time ECG display of the three
channels and the QRS reference pulse was provided. The
developed LabVIEW application also processes these four
signals to compute the fractal dimension of the VLP attractor.
This last process involves several steps that are described
below.
B. Filtered SAECG and VLP Isolation
For computing the SAECG frame, the SFP alignment
algorithm [14] was coded in the micro-controller. The
reference channel and the number of beats to be averaged are
decided by the operator. The SAECG was computed for the
three channels (X, Y, Z). As the frequency spectrum of VLP is
Power Supply (Rechargeable Batteries)
3-Channel
ECG
Front-End
Amplifier
Embedded
R-Wave, SFP
Processing &
16-Bit ADC
Opto-
Isolation &
UART-USB
Interface
Laptop PC,
Computing
& Display:
SAECG &
3D Attractor
of VLPs
For Peer Review OnlyEUROCON 2013
mainly between 40 and 300Hz, a 40Hz, bi-directional, 4th
order Butterworth high pass filter was applied to generate the
filtered SAECG frames [9].
For VLP isolation method consists, the XVLP, YVLP and
ZVLP vectors were selected. In order to select the VLP vectors,
a vector magnitude of the filtered SAECGs was computed. It
follows the next equation:
(1)
In equation (1) Xf, Yf and Zf are the 40 Hz high-pass
filtered SAECG signals. On the computed vector magnitude
frame (M), the end of VLP (te) is located when the amplitude
of M is equal to the lowest noise level plus three times its
standard deviation in the ST region; the start time (ts) is
located when the amplitude of M is equal to 40�V. The
amplitude is obtained by measuring the average of a 10ms
window and moving it by steps of 5ms towards the QRS
complex.
The segment of each vector (X,Y,Z) between those two
time limits (T = te-ts), will be the XVLP, YVLP and ZVLP vectors,
in other words, the VLP isolated in each channel.
C. Filtered SAECG and VLP Isolation
Once the VLP are isolated, each XVLP, YVLP and ZVLP
vector is numerically scaled into �V units. There are only two
parameters that need to be computed to calculate the VLPd
parameter which determines whether or not the patient is at
risk of SCD. The method may include an estimation of the
fractal dimension of the attractor (VLPd), as the quotient of:
(2)
where is the total length of the attractor (3-D curve) and
is the spheric extent diameter of the attractor. Parameters L
and DD are measured in the microvolts scale to properly
compute the parameter . The total length of the trajectory
can be calculated as follows:
(3)
In where N is the number of time steps in the interval T
used to record the ventricular late potential, that is, the number
of samples taken by the digitisation process in the interval T.
The computation of DD diameter involves more calculations
than the one carried out by the total length. However it is not
complicated. It states that for each couple of values (X,Y,Z),
the distance has to be calculated and placed in a matrix; then
the maximum diameter DD, will be the maximum value of the
matrix D.
(4)
The matrix representation of all Dij elements has a
symmetric form due to the square functions in the equation
above, besides when i = j the result is zero (0). Then, only
half of the elements are needed to determine the DD
parameter.
(5)
Afterwards, taking the maximum value of the matrix
elements the DD will be obtained.
(6)
Previous studies have found that a fractal dimension in
excess of 1.3 may be selected as the value that indicates a
positive condition for risk of SCD [6, 22].
D. System Testing Methods
In order to bench test the system, a QRS signal model (a
60ms width periodic pulse with 750ms period) was utilised as
an input in channel X, and as the front-end has a band pass
from 3 to 360Hz the QRS signal model becomes as Fig. 3
shows.
Figure 3. Filtered QRS signal model.
D.1 SAECG Noise Immunity
Spectral degradation upon the SAECG due to noise
interference effect on the SFP (single-fidutial-point) alignment
technique can be evaluated by measuring the alignment jitter.
That is, how accurate the algorithm can be while noise
conditions increase. For this, QRS signal model was set as an
input in channel Z, while the same QRS signal model with
added 50Hz or simulated EMG noise was set as an input in
channel X. The microcontroller detects the QRS complex
using channel X as reference; after, this detection was stored
into a vector called qrs. Then, a detection of QRS complex of
channel Z (without added noise) was ran using Matlab. The
evaluation algorithm computed and saved the time difference
of the qrs and the detection made by Matlab, then the standard
deviation is calculated on this vector, the result will be the
value of the jitter SD. For our 2kHz sampling operation, if the
jitter SD value is closer to 0.5ms when a remarkable noise
mainly between 40 and 300Hz, a 40Hz, bi-directional, 4th
order Butterworth high pass filter was applied to generate the
filtered SAECG frames [9].
For VLP isolation method consists, the XVLP, YVLP and
ZVLP vectors were selected. In order to select the VLP vectors,
a vector magnitude of the filtered SAECGs was computed. It
follows the next equation:
(1)
In equation (1) Xf, Yf and Zf are the 40 Hz high-pass
filtered SAECG signals. On the computed vector magnitude
frame (M), the end of VLP (te) is located when the amplitude
of M is equal to the lowest noise level plus three times its
standard deviation in the ST region; the start time (ts) is
located when the amplitude of M is equal to 40�V. The
amplitude is obtained by measuring the average of a 10ms
window and moving it by steps of 5ms towards the QRS
complex.
The segment of each vector (X,Y,Z) between those two
time limits (T = te-ts), will be the XVLP, YVLP and ZVLP vectors,
in other words, the VLP isolated in each channel.
C. Filtered SAECG and VLP Isolation
Once the VLP are isolated, each XVLP, YVLP and ZVLP
vector is numerically scaled into �V units. There are only two
parameters that need to be computed to calculate the VLPd
parameter which determines whether or not the patient is at
risk of SCD. The method may include an estimation of the
fractal dimension of the attractor (VLPd), as the quotient of:
(2)
where is the total length of the attractor (3-D curve) and
is the spheric extent diameter of the attractor. Parameters L
and DD are measured in the microvolts scale to properly
compute the parameter . The total length of the trajectory
can be calculated as follows:
(3)
In where N is the number of time steps in the interval T
used to record the ventricular late potential, that is, the number
of samples taken by the digitisation process in the interval T.
The computation of DD diameter involves more calculations
than the one carried out by the total length. However it is not
complicated. It states that for each couple of values (X,Y,Z),
the distance has to be calculated and placed in a matrix; then
the maximum diameter DD, will be the maximum value of the
matrix D.
(4)
The matrix representation of all Dij elements has a
symmetric form due to the square functions in the equation
above, besides when i = j the result is zero (0). Then, only
half of the elements are needed to determine the DD
parameter.
(5)
Afterwards, taking the maximum value of the matrix
elements the DD will be obtained.
(6)
Previous studies have found that a fractal dimension in
excess of 1.3 may be selected as the value that indicates a
positive condition for risk of SCD [6, 22].
D. System Testing Methods
In order to bench test the system, a QRS signal model (a
60ms width periodic pulse with 750ms period) was utilised as
an input in channel X, and as the front-end has a band pass
from 3 to 360Hz the QRS signal model becomes as Fig. 3
shows.
Figure 3. Filtered QRS signal model.
D.1 SAECG Noise Immunity
Spectral degradation upon the SAECG due to noise
interference effect on the SFP (single-fidutial-point) alignment
technique can be evaluated by measuring the alignment jitter.
That is, how accurate the algorithm can be while noise
conditions increase. For this, QRS signal model was set as an
input in channel Z, while the same QRS signal model with
added 50Hz or simulated EMG noise was set as an input in
channel X. The microcontroller detects the QRS complex
using channel X as reference; after, this detection was stored
into a vector called qrs. Then, a detection of QRS complex of
channel Z (without added noise) was ran using Matlab. The
evaluation algorithm computed and saved the time difference
of the qrs and the detection made by Matlab, then the standard
deviation is calculated on this vector, the result will be the
value of the jitter SD. For our 2kHz sampling operation, if the
jitter SD value is closer to 0.5ms when a remarkable noise
For Peer Review OnlyEUROCON 2013
level is present in channel X, then the SFP algorithm
implementation is reliable.
D.2 SAECG Denoising Performance
To evaluate denoising performance of the signal averaging
(SA) process, two types of noises (50Hz and the simulated
EMG) were considered under a controlled bench testing
method. For this, two analogue noise generators were
implemented. The QRS model was corrupted with noise and,
also was set as input signal in channel X. Seven different
levels of both noises were considered. Then, each level of
noise recording, was passed through the SA process and the
level of noise in the signal was measured when the number of
averaged beats was 1, 10, 20, 50, 100, 200 and 400. For
Gaussian noise, it is known that the noise level is inversely
proportional to the square root of the number of averaged
beats, and this is the case for our simulated EMG noise.
D.3 In-vitro VLPd Measurement Performance
The cardiac activity of five healthy volunteers were
recorded and processed through the VLP algorithm to observe
expected results for healthy subject cases. For testing LP
positive conditions, controlled abnormal ECG synchronised
signal models of LP for each orthogonal channel, were
analogically generated with a filters bank network (see Fig. 4).
The objective was to synthesise in-vitro SCD positive ECG
waveforms presenting rather complex 3D, LP signal
components at the body surface, so they appear coherently
within the recorded cardiac signal activity. The 3D LP signal
model consists of three different bipolar wave pulses: resonant
waveforms at natural frequencies from 40Hz to 120Hz. A
range of VLPd values, at abnormal levels (SCD positive)
between 1.32 and 1.38, was obtained by varying the amplitude
of the ECG synchronised input pulse generation between 3V
and 18V (see Fig. 5).
Figure 4. Modelled VLPd signal generation process block diagram.
III. RESULTS
A. SAECG Spectral Degradation vs Noise
The jitter standard deviation (SD) of the QRS alignment
technique was computed under certain noise levels for 50Hz
and simulated EMG noise types. The results are summarised
in Fig. 6. The closer to 0.5 ms the value of SD jitter, the more
accurate would be the SFP algorithm. According to the results
obtained, an extreme case of 50Hz noise level of 340�V(rms),
was found to be the worst type of noise to handle and yielded
Figure 5. Modelled VLPd values obtained as the ECG synchronised input
pulse amplitude is adjusted between 3V and 18V.
a jitter level of 2.6 ms. The implemented real-time SFP
algorithm includes a 30Hz cut-off frequency low-pass filter,
but still the 50Hz interference has its influence on the QRS
alignment precision. A simulated EMG noise (Gaussian with
300Hz BW) level of 71�V (rms) yielded a 1.3 ms jitter (SD).
With EMG type of noise, spectral degradation can be deduced
from the measured jitter by the relation BW = (0.13/(SD jitter))
[14]; several calculated values are presented in Table I.
Figure 6. Beat alignment jitter resulting from added EMG and 50Hz noise
levels to a clean signal.
TABLE I
MEASURED JITTER SD OF QRS ALIGNMENT AT DIFFERENT NOISE LEVELS
AND ESTIMATED BANDWIDTH LIMITATION DUE TO JITTER.
EMG Noise
(�V)
Measured
SD Jitter (s)
Bandwidth Limit (Hz)
BW � [0.13/(SD jitter)]
70.87 1.30E-03 100.0
59.14 7.32E-04 177.7
25.8 6.96E-04 186.8
26.44 6.51E-04 199.8
12.1 6.91E-04 188.0
4.08 6.38E-04 203.8
B. Denoising Performance
The denoising performance results of the implemented SA
technique, for the SAECG frame generation, are presented in
ECG
synchronised
input pulse
generation
Filters
bank
Natural
frequencies:
40 Hz
to
120 Hz
Diff.
output
Amplif.
Diff.
output
Amplif.
Diff.
output
Amplif.
+ X _LP
- X_LP
+ Y_ LP
- Y_ LP
+ Z_ LP
- Z_ LP
level is present in channel X, then the SFP algorithm
implementation is reliable.
D.2 SAECG Denoising Performance
To evaluate denoising performance of the signal averaging
(SA) process, two types of noises (50Hz and the simulated
EMG) were considered under a controlled bench testing
method. For this, two analogue noise generators were
implemented. The QRS model was corrupted with noise and,
also was set as input signal in channel X. Seven different
levels of both noises were considered. Then, each level of
noise recording, was passed through the SA process and the
level of noise in the signal was measured when the number of
averaged beats was 1, 10, 20, 50, 100, 200 and 400. For
Gaussian noise, it is known that the noise level is inversely
proportional to the square root of the number of averaged
beats, and this is the case for our simulated EMG noise.
D.3 In-vitro VLPd Measurement Performance
The cardiac activity of five healthy volunteers were
recorded and processed through the VLP algorithm to observe
expected results for healthy subject cases. For testing LP
positive conditions, controlled abnormal ECG synchronised
signal models of LP for each orthogonal channel, were
analogically generated with a filters bank network (see Fig. 4).
The objective was to synthesise in-vitro SCD positive ECG
waveforms presenting rather complex 3D, LP signal
components at the body surface, so they appear coherently
within the recorded cardiac signal activity. The 3D LP signal
model consists of three different bipolar wave pulses: resonant
waveforms at natural frequencies from 40Hz to 120Hz. A
range of VLPd values, at abnormal levels (SCD positive)
between 1.32 and 1.38, was obtained by varying the amplitude
of the ECG synchronised input pulse generation between 3V
and 18V (see Fig. 5).
Figure 4. Modelled VLPd signal generation process block diagram.
III. RESULTS
A. SAECG Spectral Degradation vs Noise
The jitter standard deviation (SD) of the QRS alignment
technique was computed under certain noise levels for 50Hz
and simulated EMG noise types. The results are summarised
in Fig. 6. The closer to 0.5 ms the value of SD jitter, the more
accurate would be the SFP algorithm. According to the results
obtained, an extreme case of 50Hz noise level of 340�V(rms),
was found to be the worst type of noise to handle and yielded
Figure 5. Modelled VLPd values obtained as the ECG synchronised input
pulse amplitude is adjusted between 3V and 18V.
a jitter level of 2.6 ms. The implemented real-time SFP
algorithm includes a 30Hz cut-off frequency low-pass filter,
but still the 50Hz interference has its influence on the QRS
alignment precision. A simulated EMG noise (Gaussian with
300Hz BW) level of 71�V (rms) yielded a 1.3 ms jitter (SD).
With EMG type of noise, spectral degradation can be deduced
from the measured jitter by the relation BW = (0.13/(SD jitter))
[14]; several calculated values are presented in Table I.
Figure 6. Beat alignment jitter resulting from added EMG and 50Hz noise
levels to a clean signal.
TABLE I
MEASURED JITTER SD OF QRS ALIGNMENT AT DIFFERENT NOISE LEVELS
AND ESTIMATED BANDWIDTH LIMITATION DUE TO JITTER.
EMG Noise
(�V)
Measured
SD Jitter (s)
Bandwidth Limit (Hz)
BW � [0.13/(SD jitter)]
70.87 1.30E-03 100.0
59.14 7.32E-04 177.7
25.8 6.96E-04 186.8
26.44 6.51E-04 199.8
12.1 6.91E-04 188.0
4.08 6.38E-04 203.8
B. Denoising Performance
The denoising performance results of the implemented SA
technique, for the SAECG frame generation, are presented in
ECG
synchronised
input pulse
generation
Filters
bank
Natural
frequencies:
40 Hz
to
120 Hz
Diff.
output
Amplif.
Diff.
output
Amplif.
Diff.
output
Amplif.
+ X _LP
- X_LP
+ Y_ LP
- Y_ LP
+ Z_ LP
- Z_ LP
For Peer Review OnlyEUROCON 2013
Fig. 7. Noise reduction trends as a function of increased
number of averaged beats is effectively delivered for both
types of noise (50Hz and simulated EMG). For example, with
400 beats, 26.02 dB attenuation on simulated EMG noise can
be delivered.
Theoretically, for Gaussian noise, the attenuation factor is
, where N is the number of averaged beats. Fig. 8
graphically depicts the evidence of this fact in the
implemented SAECG system. There, (Final Noise) vs (Initial
Noise) of seven cases of EMG noise levels are plotted. To
understand this relation more clearly, the equation
,
can be rewritten as y = m�x. Hence,
; in which
y =
and
, and therefore, the slope of the linear
function (m) is intended to be N, which is 400 in Fig. 8.
Figure 7. ECG denoising profile with increased number of averaged beats
( ), for several EMG and 50Hz noise levels.
Figure 8. Signal averaging denoising performance plot of (Square initial
noise) vs (Square final noise).
C. VLPd Parameter Measurement Algorithm: In-vitro Test
The ECG signal of five healthy volunteers were recorded
and analysed with the completed system prototype. The
average value of the VLPd parameter in these healthy subjects
(SCD negative) was 1.204 with a standard deviation of
�0.0526, which is under 1.3; as expected in healthy subjects.
Figure 9(a) illustrates the SAECG vector magnitude frame
obtained in one of these healthy subjects (Volunteer #1), and
Fig. 10(a) shows the corresponding measurement of the
isolated 3D VLP attractor and the measured VLPd value
(1.179) for this particular case.
Furthermore, after coherently adding the VLP signal model
while the body surface ECG signal was recorded again in one
of the healthy volunteers (# 1), the SAECG vector magnitude
frame shown in Fig. 9(b) was obtained. As the synthetic VLP
model was of remarkable complexity, with an estimated VLPd
value between 1.32 and 1.38, it was detected in combination
with the baseline VLP activity of this particular healthy
subject, hence resulting in a measured VLPd value of 1.476, as
illustrated in Fig. 10(b). Thus, the value of VLPd in this
healthy subject increased from 1.179 to 1.476 after the in-vitro
implementation of a positive SCD ECG-waveform modelling
technique.
(a)
(b)
Figure 9. Filtered SAECG vector magnitude frames obtained from a healthy
subject case (Volunteer #1): (a) baseline SAECG recording, (b) SAECG
recording with coherently added synthetic VLP signal model.
(a) Volunteer #1: VLPd = 1.179 (b) With model VLP: VLPd =1.476
Figure 10. Three dimensional plots of VLP attractors: (a) baseline recording,
(b) with the added synthetic VLP signal model.
Fig. 7. Noise reduction trends as a function of increased
number of averaged beats is effectively delivered for both
types of noise (50Hz and simulated EMG). For example, with
400 beats, 26.02 dB attenuation on simulated EMG noise can
be delivered.
Theoretically, for Gaussian noise, the attenuation factor is
, where N is the number of averaged beats. Fig. 8
graphically depicts the evidence of this fact in the
implemented SAECG system. There, (Final Noise) vs (Initial
Noise) of seven cases of EMG noise levels are plotted. To
understand this relation more clearly, the equation
,
can be rewritten as y = m�x. Hence,
; in which
y =
and
, and therefore, the slope of the linear
function (m) is intended to be N, which is 400 in Fig. 8.
Figure 7. ECG denoising profile with increased number of averaged beats
( ), for several EMG and 50Hz noise levels.
Figure 8. Signal averaging denoising performance plot of (Square initial
noise) vs (Square final noise).
C. VLPd Parameter Measurement Algorithm: In-vitro Test
The ECG signal of five healthy volunteers were recorded
and analysed with the completed system prototype. The
average value of the VLPd parameter in these healthy subjects
(SCD negative) was 1.204 with a standard deviation of
�0.0526, which is under 1.3; as expected in healthy subjects.
Figure 9(a) illustrates the SAECG vector magnitude frame
obtained in one of these healthy subjects (Volunteer #1), and
Fig. 10(a) shows the corresponding measurement of the
isolated 3D VLP attractor and the measured VLPd value
(1.179) for this particular case.
Furthermore, after coherently adding the VLP signal model
while the body surface ECG signal was recorded again in one
of the healthy volunteers (# 1), the SAECG vector magnitude
frame shown in Fig. 9(b) was obtained. As the synthetic VLP
model was of remarkable complexity, with an estimated VLPd
value between 1.32 and 1.38, it was detected in combination
with the baseline VLP activity of this particular healthy
subject, hence resulting in a measured VLPd value of 1.476, as
illustrated in Fig. 10(b). Thus, the value of VLPd in this
healthy subject increased from 1.179 to 1.476 after the in-vitro
implementation of a positive SCD ECG-waveform modelling
technique.
(a)
(b)
Figure 9. Filtered SAECG vector magnitude frames obtained from a healthy
subject case (Volunteer #1): (a) baseline SAECG recording, (b) SAECG
recording with coherently added synthetic VLP signal model.
(a) Volunteer #1: VLPd = 1.179 (b) With model VLP: VLPd =1.476
Figure 10. Three dimensional plots of VLP attractors: (a) baseline recording,
(b) with the added synthetic VLP signal model.
For Peer Review OnlyEUROCON 2013
IV. DISCUSSION
A major cause of unexpected and sudden natural death is
due to underlying cardiac causes in people with apparently
healthy hearts. A considerable number of these people live
without being aware of their potential heart problem and being
at-risk of SCD. Any public initiative of large-scale
personalised health scheme attending these individuals, would
require efficient and reliable screening techniques and devices
for rapid and accurate detection, risk stratification and
categorisation, in order to lead specific therapeutic strategies
to prevent SCD in at-risk classified individuals. Even though
several standard and unconventional techniques are available,
the main drawback of conventional heart screening techniques,
for accurately detecting individuals at-risk of SCD, is the
expertise dependency nature of them, and this is a major
hindrance to their effective and ample adoption by clinicians.
The number of people being screened would be strongly
limited by the number of cardiologists or specialised clinical
physiologists (highly trained clinical staff) working for the
screening programme.
A cardiac screening technology that is accurate, portable
and cost effective is important in providing a significant
contribution in solving the problem. In this research project,
we have implemented and in-vitro tested an innovative
solution in which the required advanced expertise component
is embedded within the system device. With this approach, the
required clinician training is minimised or not required at all,
with the final aim of facilitating its benefits into the clinical
practice and consequently, significantly bringing down the
incidence of SCD in the population. Thus, targeted solution
here was to provide a novel medical technology that is user
friendly, portable, and cost-effective for cardiac screening
using non-invasive complex waveform analysis of the
electrocardiogram. Such a novel device will require minimal
or no clinician training because of its embedded or integrated
processing expertise, and also its simple patient grading
output figure that is easy to interpret, as it could be a simple
outcome lamp indicator (VLPd value above or below the 1.3
threshold value).
Therefore, the developed ECG waveform fitness check
(ECGWFC) device, is intended to be operator independent,
providing a single outcome indicative figure or alternatively a
simple �green/red� light result output per person being
screened. In this way, the flow of heart checked people in a
screening day can be decided simply by the number of
available ECGWFC units and the number of minimally
trained nurses or paramedic personnel. The ECGWFC device
uses conventional SAECG recording methods from three
different perpendicular views (orthogonal XYZ leads).
However, its novel technique isolates particular small
waveforms in the ECG corresponding to abnormal and
delayed ventricular activity of the heart (VLP) in each
orthogonal lead. These waveforms are analysed in a 3D
voltage space, at microvolts scale level, and its trajectory
complexity measured by its fractal property. The latter
measurement reveals a robust indication of the heart�s fitness
or being at-risk to SCD, it can also be associated with
cardiovascular complications related to type-1 diabetes and
some other life threatening cardiac disorders such as Brugada
syndrome. Nevertheless, a comprehensive clinical trial of the
proposed ECGWFC prototype device and methods will be the
next stage envisaged for this research and development work.
V. CONCLUSIONS
To support healthcare policies in preventing patient from
suffering SCD, a real-time HRECG system prototype was
implemented and tested at the Centre for Advanced
Cardiovascular Research (CACR), in Ulster. By using fractal
dimension analysis of the VLPs, a handheld portable and
reliable cardiac point-of-care diagnostic device can be
provided using these methods. The system device can enable
doctors to screen patients at risk in cardiac clinics and out-of-
hospital communities. It is important to mention that this
method is a non-invasive one, and can be used anywhere. It
would require the patient to be at rest for a few minutes while
recording. Also, this device may prove useful in future
research studies about VLP related cardiac pathologies, such
as in patients with Brugada syndrome, and in children and
adolescents with type1-diabetes.
The expected benefits of the proposed ECGWFC device
are that those people unaware of being at-risk of SCD can have
a greater chance of being detected on time and receive the
benefit of cardiac treatment and advice on taking a preventive
change of life. The related benefit in a preventive cardiac
healthcare programme with the proposed ECGWFC device, is
that the number of people being heart screened can be
increased only by increasing the number of devices and the
number of low-skill medical personnel, who could be easily
trained for operating the ECGWFC device and for placing the
disposable ECG electrodes. All these potential features will
translate into lower running costs per screened person.
ACKNOWLEDGEMENT
The authors wish to acknowledge the philanthropic funding
provided in part by the Ulster Garden Villages Ltd and The
McGrath Trust, for the CACR research staff who participated
in this project.

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