Device Technique For Cardiac Risk Health And Social Care Essay

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 oj.escalona@ulster.ac.uk

2 marianela.min@gmail.com

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 oj.escalona@ulster.ac.uk

2 marianela.min@gmail.com

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.