ECGAWARE: AN ECG MARKUP LANGUAGE FOR

AMBULATORY TELEMONITORING AND DECISION MAKING

SUPPORT

Bernardo Gonçalves, José G. Pereira Filho

Computer Science Department, Federal University of Espírito Santo (UFES), Vitória, Brazil

Rodrigo V. Andreão

Electrical Engineering Department, Federal University of Espírito Santo (UFES), Vitória, Brazil

Keywords: Telecardiology, Telemonitoring, ECG data, Interoperability, Context-Awareness, Emergency and decision

support.

Abstract: The ambulatory electrocardiogram (AECG) can be acquired and transmitted through mobile and wireless

technologies and devices to foster heart’s telemonitoring anytime, anywhere. This sort of service is

purposeful when combined with ECG analysis systems and infrastructural support for providing context-

aware services. Such setting makes efficient emergency services possible as well as improves the support to

physician’s decision making. This paper presents an ECG XML-based markup language that extends ECG

reference standards in order to cover patient’s heart telemonitoring during his/her daily activities. The ECG

data format we propose is then applied in a real scenario.

1 INTRODUCTION

The rapid expansion of ICT has been allowing the

creation of new services on Healthcare. The

Telecardiology, in particular, has developed itself

mainly through the transmission of the

electrocardiogram (ECG). On one side, the ECG is

fast, cheap and non-invasive when compared with

other cardiology examination procedures. On the

other side, the analysis of the ECG waveform can

identify a wide range of heart illnesses, which are

distinguished by specific modifications on ECG

elementary waveforms. These are the reasons why

ECG is the most frequently applied test for

measuring heart activity in Cardiology. According to

estimates, more than 100 million ECG’s are

recorded yearly in Western Europe (Fischer and

Zywietz, 2003).

The storage and transmission of all these data

have then been object of some initiatives concerning

ECG format standardization. The oldest standards

are AHA/MIT-BIH (Goldberger et al., 2000) and

SCP-ECG (SCP, 2002), regarding records’ storage

and transmission respectively. In face of the Internet

popularization, however, novel standards have been

conceived in order to integrate interoperable and

user-driven solutions, standing out FDADF (Brown

et al., 2002) and ecgML (Wang et al., 2003), both

based on the XML markup language.

Nonetheless, such standards do not take into

account heart’s telemonitoring, which calls for the

representation and transmission of the ambulatory

electrocardiogram (AECG). The portable device that

records the AECG was invented by Norman Holter

in 1957. Since then, the ICT advances in addition to

improvements in the accuracy of ECG software-

based analysis systems have opened new potential

uses to the AECG. Indeed, it is largely employed by

the medical community, mostly for diagnosis and/or

therapeutic treatment of the myocardial ischemia,

which constitutes a pre-infarct. Since most ischemia

episodes are related to increases in heart rate

possibly associated to day-to-day variability of

physical or emotional activities, AECG is indicated

for patient’s heart monitoring throughout his or her

daily activities. With this in mind, the most suitable

duration of a recording session to detect and

quantify ischemia episodes is probably 48 hours.

Some experiments point out that most patients are

quite comfortable wearing the recorder for 48 hours

(Crawford et al., 1999).

Besides, with the advent of a new Computing

paradigm, the Pervasive Computing, context-aware

Gonc¸alves B., G. Pereira Filho J. and V. Andre

ao R. (2008).

ECGAWARE: AN ECG MARKUP LANGUAGE FOR AMBULATORY TELEMONITORING AND DECISION MAKING SUPPORT.

In Proceedings of the First International Conference on Health Informatics, pages 37-43

 SciTePress

systems provide new features, standing out for

collaboration among professionals, systems, and

action triggering from the detection of changes in

the context of the user. This can be verified with the

growth of initiatives dedicated to patient’s

monitoring, whether at home or in emergency

situations wherever they take place. An example is

the Awareness project (Awareness, 2007). Such

efforts have taken advantage of the latest advances

in mobile and wireless technologies and devices, in

general, and may rely on signal processing

algorithms, in particular, in order to provide both

alarms generation and decision making support.

In face of all these aspects, we advocate that an

ECG data format should cover the particular issues

concerning telemonitoring through AECG, such as

the ones related to emergency assistance (e.g.

patient’s location), or the activities performed by the

patient during the ECG recording session (e.g. rest,

physical exercise, routine activities, etc.). In this

article we propose a novel ECG data format that

extends former reference standards in order to cope

with real-time telemonitoring and decision making.

The paper is organized as follows. Section 2

discusses the background of ECG data standards as

well as aspects of telemonitoring; Section 3 presents

the ECG data format we propose in this paper;

Section 4 introduces a usage scenario where the

format proposed is applied; and, finally, Section 5

concludes the paper and depicts future work.

2 BACKGROUND

Throughout the last thirty years, we can notice a

regular evolution of standards regarding ECG

record’s representation and transmission. One may

state that each standard resembles its purpose and

the ICT environment at the time of its arising.

Since 1975, the Massachusetts Institute of

Technology (MIT) together with laboratories of the

Beth Israel Deaconess Medical Center have carried

out research concerning medical examinations

analysis and related points. As a result, in 1980 the

MIT-BIH Arrhythmia Database was deployed after

testing and standardization for arrhythmia detection

and evaluation. Also at this time, the American

Heart Association (AHA) has deployed the AHA

Database for Evaluation of Ventricular Arrhythmia

Detectors (

Goldberger et al., 2000). Together, those

databases have been largely used and played an

important role on research in the field of Cardiology

(Moody and Mark, 2001).

The AHA/MIT-BIH standard has focused on

bringing in an ECG records’ library for providing

input for developers of ECG analysis systems. It, in

fact, is responsible for substantial advances on ECG

data processing. This standard, however, does not

aid interoperation over the Internet due to its tight

coupling with programming language. Moreover, it

is not human readable, which is a desirable

requirement with respect to the analysis of

electrocardiography’s domain experts.

Later, there was a great effort to conceive the

SCP-ECG - Standard Communications Protocol for

Computer-Assisted Electrocardiography (SCP,

2002). SCP-ECG is a specification concerning ECG

data format as well as transmission procedure from

the acquisition device to the host where the message

is stored and retrieved. From 1989 to 1990, it was

carried out a survey on ECG compression methods

that has as a result led to an original approach for

signal compression (Fischer and Zywietz, 2003).

Nonetheless, despite the SCP-ECG allows

suitable data compression, the elements of the

format are defined at the bit level. This obstructs

changes on the format, either for updating or

customization, as well pushes final applications (i.e.,

ECG viewers) to be familiar with SCP codes. As per

(Clunie, 2004), SCP implementation is an

awkwardly task especially on the compression

mode. Considering that computational resources are

currently more accessible than at the creation of the

SCP, bandwidth over the networks, memory

capacity and disc space are not main concerns as

they were before. Meanwhile, other concerns have

taken place on Telemedicine scenarios, such as the

need for platform- and application-independent

solutions involving human readable data models.

In this trend, and also as a result of the grown

popularity of the Internet, XML-based formats as

FDADF and ecgML have been increasingly used on

Telecardiology research. The Extensible Markup

Language (XML) has played an important role on

data exchange over the web, especially by providing

the separation of data content and presentation. After

XML has became a W3C recommendation in 1998,

several domain specific languages were created from

a XML Schema. In this way, several committees of

Health organizations such as CEN/TC251, Health

Level Seven (HL7), American Society for Testing

and Materials (ASTM), etc, have worked on the

development of recommendations for using XML on

Telemedicine research.

The Food and Drug Administration (FDA) has

carried out a survey on ECG standards and has

chosen the XML technology for data representation

based on the HL7 ECG annotation message v3

(HL7, 2003). As a result, in 2002 it has produced the

FDA XML Data Format (FDADF). The FDADF is

HEALTHINF 2008 - International Conference on Health Informatics

an effort to reach the standardization of ECG data

representation for all stakeholders share the same

view (Brown et al., 2002). Looking for addressing

requirements previously defined, the scope of the

FDADF specification covers ECG data as much as

significant submission information. FDADF has

achieved a significant progress on ECG data

representation by using XML. Nevertheless, as per

(Wang et al., 2003), it does not exploit as far as

possible XML features. That is because, on account

of ECG viewer applications’ concerns, it has

incorporated elements related to data presentation in

its metamodel, rather than to cope only with data

content.

More recently, in 2003, the ecgML was

developed in face of the increased demand for a

standardized application- and platform-independent

ECG format. This one has been conceived from the

former standards (especially the FDADF), reusing

then concepts and nomenclature. The ecgML allows

ECG data analysis and transmission between

heterogeneous platforms (Wang et al., 2003).

Indeed, rather than FDADF, the ecgML has

comprised only data content. It holds benefits such

as flexibility, readability and descriptiveness.

Nonetheless, as remarked by the authors themselves,

there are issues left to evaluation, such as concepts

still not covered in ecgML (Wang et al., 2003).

As a matter of fact, the more is the emergence of

new technologies increasing the usage potential of

computer systems, the more there are usage

scenarios foreseen. As a result, further information

can be explored promoting then more useful

services. In this way, a data format for wrapping

biomedical signals, in fact, constitute an interface

between data acquisition and data usage systems

(see Figure 1). Therefore, such a data format should

not be a restrictive mean for useful data acquired

from sophisticated devices. As opposed, it should

abstract the complexity related to biomedical signals

acquisition to the health professionals’ environment.

This concern is particularly worth in context-aware

telemonitoring of patients’ heart relying on both

wireless and mobile technologies and devices and

the transmission of AECG. The existing ECG

standards, however, lack this concern and neither

were conceived from advanced modeling techniques

such as domain ontologies.

Figure 1: Separation between data acquisition and usage.

As an effort to cover this gap in literature, we

have carried out an extensive research on the

Electrocardiography domain. At this time, we have

developed an ECG domain ontology which is

presented elsewhere (Gonçalves et al., 2007), and

the ecgAware, a XML-based ECG data format

which is the focus of this article. The ecgAware

extends former standards especially by covering

AECG aspects related to context-aware

telemonitoring. On the next section we elaborate on

the ecgAware markup language, remarking the main

issues we have previously mentioned.

3 ECGAWARE

The ecgAware has a tree hierarchical structure which

is described in the following in a prefix way, i.e.,

expanding each significant XML complex element

on the left. The main elements are depicted on

diagrams in the figures 2 to 5. XML elements and

attributes are both referenced in bold and italic, (the

elements have the first letter capitalized); optional

elements or attributes are dotted in the diagrams.

The ecgAware model constitutes an ECGStudy

(see Figure 2) of a single patient, which integrates

attributes that provide some prior data. These data

are studyID, a unique ID; studyTimeStamp, i.e.,

date and start time of the latest ECG record present

in the message; dateTimeZone, which supplies the

acquisition local time zone (based on SCP);

studyLocation, holding the latest location obtained;

the alarm attribute indicates that at least one record

inside the study contains an abnormal event, which

may be either detected by an ECG analysis system

or triggered by the patient. In case it is flagged true,

the ecgAware message supports an efficient

emergency service by the studyTimeStamp and

studyLocation attributes. Finally, computerID

identifies the machine where signal processing takes

place (based on SCP) and investigatorID is a unique

ID of the health professional which blames for the

ECG study (based on FDADF). ECGStudy has three

child elements: (i) PatientData, for patient’s

demographics data and electronic record; (ii)

Record, the ECG record produced in each recording

session; and (iii) Comments, for free text.

The Demographics element then comprises data

for identifying and contacting the patient (inspired

on ecgML); its child elements are Name, Sex, DOB

(date of birthday), Address, Phone, Fax and Email.

Meanwhile, EPR represents a basic patient’s

electronic record; it is composed by patient’s Height

and Weight; the boolean elements Hypertension,

Diabetes, Smoker and Alcohol

; Other for inserting

ECGAWARE: AN ECG MARKUP LANGUAGE FOR AMBULATORY TELEMONITORING AND DECISION

MAKING SUPPORT

other clinical data; and Comments, a free text field.

Demographics and EPR may be obtained by means

of a simple anamnesis. Those data are optional

because there may be situations (e.g. an emergency)

where there is no time for collecting them.

Figure 2: The ECGStudy root element.

ECG data are laid in the Record element (see

Figure 3). A recordID attribute identifies the record;

The RecordingDevice element describes the

acquisition device used to obtain the record (based

on FDADF) and filtering technique(s) performed by

it (based on SCP); RecordingSession bears the

recording session context, and is especially useful

for emergency services and decision support;

RecordChannel (min. one, max. twelve) constitutes

the ECG signal acquired through a channel;

GlobalAnnotations and GlobalMeasurement in turn

(inspired on FDADF and SCP respectively) are

annotations and measurements related to all leads;

and lastly, Report is a record finding carried out

either by a physician that interacts with a system or

by an analysis system to be further verified by a

confirming physician.

Figure 3: The Record element.

RecordingDevice has a deviceID to identify the

device by a serial number. It has also a Type, a

Manufacturer and a Model (e.g. Holter, Space Labs,

90205). The BaselineFilter, LowpassFilter and zero

or many OtherFilter elements constitute noise

filtering to overlook signal frequency components

over superior bounds and other filtering possibly

performed on the signal, respectively.

As we previously mentioned, patient’s heart

continuous telemonitoring can support diagnosis

and/or therapeutic treatment of the myocardial

ischemia. This is possible by means of a long-term

AECG RecordingSession (see Figure 4). With this

in mind, we included the Activity element, which

lays up a description of each activity performed by

the patient during the recording session (e.g. rest,

physical effort, etc). This information can be either

obtained by user interaction with the ECG

acquisition system (in replacement of the paper in

which patients used to populate his/her activity/time

over the recording session); or much better, acquired

by a sensor device such as a video camera jointly

with an eye-tracking system (Zhai, 2003), or by

other sensing techniques (Boudy et al 2006).

Still from a context-awareness standpoint, the

patient’s context during a recording session may be

used, for example, to guide an ambulance to the

patient’s location whenever an emergency takes

place. This sort of feature is possible thereby small

mobile devices which permit, nowadays, patient’s

vital signs and location telemonitoring even in

outdoor scenarios. That is why we included the

AcquisitionLocation element, which holds the latest

patient’s location acquired in a RecordingSession

from a device such as GPS.

Figure 4: The RecordingSession element.

Besides, AcquisitionTimeStamp hands over date

and start time of each session; Alarm flags on a true

or false Value for abnormal event(s) either identified

by an ECG real-time analysis system or triggered by

the patient, and keeps on zero or many TimeStamp

elements date and time of the detected event(s). In

case Alarm is true, on one side, an ecgAware

HEALTHINF 2008 - International Conference on Health Informatics

message comprising a partial record must be

transmitted as far as the abnormal event was

detected; on the other side, the whole ECG must be

recorded including all alarm events occurred during

the recording session related to it. Lastly, SiteID is

an abstract description of the place whereby the

session took place (e.g. domicile).

ClinicalProtocol, rather than in other ECG data

formats, is placed in the RecordingSession element.

Indeed, it is related to the meantime of a session

instead of a range of sessions. It then is composed by

DiastolicBP and SystolicBP, taken in the session at

some timestamp under a value unit; Medication,

specifying drugs which the patient has been using;

and finally, Sweaty and Pale (based on ecgML)

indicating true or false for abnormal sweat and

abnormal looking skin on the face, respectively.

The ECG signal is obtained from correlated

observation series taken at the same time by

electrodes placed on some positions on the human

body. These placements, when combined, provide

different viewpoints of the heart electrical activity,

i.e., the ECG leads. In Electrocardiography twelve

leads were standardized.

ECG data is thus laid up on

one or more (max. twelve) RecordChannel elements

(see Figure 5) standing for the leads. The Channel

element identifies the lead (e.g. Lead II); Waveform

contains the XY signal; ChannelAnnotations and

zero or many Measurement elements, in turn, are

annotations and measurements related to a lead (all

inspired on FDADF and ecgML).

The ECG samples are obtained from the

observations performed by the device over the time,

and thus constitute XY values. They are situated in

the Waveform element by XValues and YValues.

However, since observations are evenly spaced in

time, we do not need to store time values (XValues)

in the XML document. They rather can be easily

obtained by the Xoffset, Duration (of the record)

and SampleRate elements (all of them holding a

unit attribute). The sample values (YValues),

otherwise, must be covered in the XML Document,

even though there are different options to get it

done. Those values have also a unit and may be laid

up either (i) in an external file, which the link path is

indicated by FileLink; or (ii) by an integer series

IntValue (which can be easily converted to float by

using a scale); or even (iii) by a binary encoding

(BinaryData). Both IntValue and BinaryData are

composed by the From, To, Data and Scale

elements. They are respectively the beginning and

ending of the waveform in the X axis, the sample

values, and a scale factor to obtain the real number

of each value. The BinaryData has also a data

encoding attribute (e.g. Base64).

Figure 5: The RecordChannel element.

ChannelAnnotations mark significant events

identified on the waveform. They are carried out by

an author, which may be either a system or a

physician. Annotations may be either about points

(PointNotation) or time intervals (WaveNotaion).

The former involves a PointLabel describing the

point, an XValue, an YValue, and a Comment. The

latter in turn marks beginning, peak and ending time

values of one or more waves by the Onset, Peak and

Offset elements, respectively. Moreover, it holds

also an Interpretation (e.g. abnormal) of the

waveform in this time interval. It is worth to say the

WaveNotation element is basically addressed by the

elementary forms, or waves, which compose the

heart beat. They were defined by Einthoven in 1895

as PQRST; we can abstract the elementary forms by

Pwave, QRScomplex and Twave. Zero or many

OtherWave elements may also be considered. On

different leads, a specific elementary form can be

viewed in a better or worse way, exception by the

QRS complex, which can be well viewed through

whichever lead. This is the reason why we made

only the QRScomplex element required on

WaveNotation.

Besides annotations, zero or many

Measurement(s) can be made either of the duration

or of the amplitude of elementary forms. We can

distinguish that by means of the label and unit

attributes, e.g. P-duration and ms (based on ecgML

and SCP).

Global annotations and measurements, as

opposed to the channel ones, are related to all leads.

This sort of annotations then can be performed either

by a physician marking a vertical line correlating the

same XValue on all leads through an ECG viewer

application, or simply by a system from an average

of the correlated channel annotations. The

GlobalAnnotations element (inspired on FDADF)

thus discriminates itself from ChannelAnnotations

only by, on the former, all elementary forms are

required. The GlobalMeasurement element

(inspired on SCP), otherwise, has exactly the same

structure of the channel Measurement. The former,

however, is obtained from an average of the

correlated channel measurements.

ECGAWARE: AN ECG MARKUP LANGUAGE FOR AMBULATORY TELEMONITORING AND DECISION

MAKING SUPPORT

Finally, we have admitted a Report element to

provide a finding about the ECG record. This report

is carried out by an author which may be either a

system or a physician that has saw the record

through an application and then has edited it. The

finding comprises HeartRate, ElectricalAxis of the

heart, and Diagnosis.

4 USAGE SCENARIO

As part of a research program in healthcare and

bioengineering technologies at UFES, in Brazil, we

have been developed in the TeleCardio project a

context-aware system for remote monitoring patients

with cardiological syndromes (Andreão et al.,

2006a). In TeleCardio, the patient can have his/her

heart activity monitored anytime either in domicile,

ambulance, or outdoor scenarios. The TeleCardio

system carries out the transmission of the AECG in

combination with contextual data (e.g. location) in

order to allow physicians follow their patients’

condition in real-time and report diseases remotely.

TeleCardio, in fact, is a rich field for applying

ecgAware to wrap and deliver ECG signals, related

data and contextual data. The Figure 6 depicts the

TeleCardio architecture.

Figure 6: TeleCardio Architecture.

The Sensing layer comprises the ECG Wrapper,

an integrated system (hardware and software) for

acquiring the ECG signal from a Holter device. This

system handles wireless communication with the

device, signal processing, data wrapping and

delivery. One of the components of such system is

an ECG analysis software (Andreão et al., 2006b),

which makes use of an enhanced approach for ECG

classification and segmentation. This software thus

produces the ECG enhanced data for populating the

ecgAware model. The ECG Wrapper, in fact, hides

all complexity related to biomedical signals

acquisition for the middleware and application

layers by delivering ecgAware data to them.

The middleware layer, named Infraware (Filho et

al., 2006), provides context-aware services for

supporting client applications. Examples of such

services are: (i) to supply subscription management

for the health client applications as well as to

manage the interactions between these applications

and the ECG Wrapper; (ii) to guarantee privacy and

access control to patients, physicians, etc; (iii) to

guide an ambulance from the patient’s domicile to a

hospital by choosing the best traffic routes; and (iv)

interpretation of pieces of information in order to

trigger emergency services, e.g. when alarm is

flagged true.

The Application layer in turn addresses services

configuration, users’ profile and so on. The Figure 7

shows a web application that we have developed in

the TeleCardio project whereby physicians can view

the patients’ ECG signals and take advantage of the

ecgAware features. By means of such an application,

the physician can follow his/her patient’s heart

activity anywhere, anytime. In case an emergency

takes place, the physician is notified both by the

application (in case he/she is online on the system)

and by a SMS message his/her cellular phone.

Figure 7: Snapshot of the TeleCardio’s ECG viewer.

Because we are speaking of telemonitoring in

real-time, the ECG Wrapper has then to pack the

AECG record into small pieces of ecgAware data for

delivery by each 30 seconds. This time interval is

related to hardware optimal operation as well as the

emergency procedure. In fact, one could argue that

the XML format do not meet computational

efficiency as much as a binary format. Nonetheless,

we can overcome this either by using a binary file to

store and transmit the ECG XY data (see the

FileLink element) or by developing a compression

procedure in order to reduce the XML file. In

(Erfianto, 2004), for example, the compression

scheme has reached a reduction of up to 53% for

ECG data and 87,5% for patient data. The technique

HEALTHINF 2008 - International Conference on Health Informatics

used parses the XML document to an ASN.1 format

and, in the sequel, to a binary-encoded format.

5 FINAL CONSIDERATIONS

Currently we have advanced wireless and mobile

technologies and devices as well as systems that

carry out biomedical signal’s analysis through signal

processing algorithms. In this work we have argued

on the worth of taking advantage of such resources

in order to improve emergency services and decision

making support in the Healthcare domain. In this

way we can make the acquired sensor data much

more useful.

In the scope of Telecardiology, in particular, we

have elaborated in this paper how the existing ECG

data format standards lack this concern. We have

then proposed a novel ECG model striving not only

for application- and platform-independence and

focusing data content, but also admitting elements to

address telemonitoring concerns. As a result, we can

provide better emergency services and decision

making support, meeting then the requirements

related to pervasive scenarios in Healthcare.

The first usage results remarks ecgAware is

suitable for its purpose. After all, throughout the

TeleCardio evaluation we will test such data format

under an intensive usage by one or more medical

communities. Hence, we will have statistical metrics

to better evaluate it.

Former usage scenarios in Telemedicine, e.g.

remote reporting, are still covered by the ecgAware

data format. In fact, it embraces features of the

former ECG reference standards. Future usage

scenarios may cover other vital signs telemonitoring.

This is feasible by using the same research

methodology, i.e. exploring each biomedical signal

domain as we did in this work with the ECG.

Moreover, the XML technology provides flexibility

such that we can incorporate several XML schemas

to a root one. We thus can keep the elements

regarding telemonitoring, in general, and to design a

record structure for each biomedical signal we

choose to bear.

ACKNOWLEDGEMENTS

This work has received financial support from

FAPES (grant no. 31024866/2005) and CNPq (grant

no. 50.6284/04-2). The first author would like to

express his gratitude to Giancarlo Guizzardi for his

invaluable contribution to the progress of this work.

REFERENCES

Andreão, R. V., Filho, J. G. P., et al., 2006a. TeleCardio –

Telecardiologia a serviço de pacientes hospitalizados

em domicílio. In X Brazilian Conference in Health

Informatics (CBIS’06), Florianópolis, Brazil.

Andreão, R. V., et al., 2006b. ECG signal analysis through

hidden Markov models. In IEEE Transactions on

Biomedical Engineering, v. 53, n. 8.

Awareness project, 2007. http://www.freeband.nl/.

Boudy, J., Andreão, R. V., et al., 2006. Telemedicine for

elderly patient at home: the TelePat project. In

International Conference on Smart Homes and Health

Telematics. Belfast, Northern Ireland.

Brown, B., Kohls, M., Stockbridge, N., 2002. FDA XML

data format design specification. Retrieved August 9,

2006 from http://www.cdisc.org/discussions/EGC/

FDA_XML_Data_Format_Design_Specification_DR

AFT_B.pdf.

Clunie, A., 2004. Extension of an open source DICOM

toolkit to support SCP-ECG waveforms. In 2nd

OpenECG Workshop. Berlin, Germany.

Crawford, M., et al, 1999. ACC/AHA Guidelines for

ambulatory electrocardiography. In Journal of the

American College of Cardiology.

Erfianto, B., 2004. Design of a Vital Sign Protocol Format

Using XML and ASN.1. M. Sc. Thesis, University of

Twente, The Netherlands.

Filho, J.G. P., et al., 2006. Infraware: um middleware de

suporte a aplicações móveis sensíveis ao contexto. In

Proceedings of the 24

Brazilian Symposium of

Computer Networks, Curitiba, Brazil.

Fischer, R., Zywietz, C., 2003. SCP How to Implement -

Part I. Retrieved August 8, 2006, from

http://www.openecg.net/.

Goldberger A., et. al., 2000. PhysioBank, PhysioToolkit,

and PhysioNet: components of a new research

resource for complex physiologic signals. In

Circulation 101(23):e215-e220.

Gonçalves, B., et al., 2007. An electrocardiogram (ECG)

domain ontology. In Proc. of the 2

Workshop on

Ontologies and Metamodels for Software and Data

Engineering (WOMSDE’07), João Pessoa, Brazil.

HL7 ECG Annotation Message v3, 2003. Retrieved

August 9, 2006, from http://wwwhl7.org/V3AnnECG/

index.htm.

Moody, G., and Mark, R., 2001. The impact of the MIT-

BIH arrhythmia database. In IEEE Engineering in

Medicine and Biology Magazine.

Standard communication protocol – Computer-assisted

electrocardiography N02-15, 2002. CEN/TC251

Secretariat. Retrieved August 8, 2006, from

http://www.centc251.org/.

Wang, H., Azuaje, F., Jung, B., Black, N., 2003. A

markup language for electrocardiogram data

acquisition and analysis (ecgML). In BMC Medical

Informatics and Decision Making 3:4.

Zhai, S., 2003. What's in the eyes for attentive input. In

Communications of the ACM, 46(3).

ECGAWARE: AN ECG MARKUP LANGUAGE FOR AMBULATORY TELEMONITORING AND DECISION

MAKING SUPPORT