ECG CLASSIFICATION AND ANALYSIS IN A ZIGBEE

WIRELESS SENSOR NETWORK

Enda Barrett, Des Chambers and Cosmin Rotariu

National University of Ireland, Galway, University Road, Galway, Ireland

Keywords: Wireless Sensor Networks, Ambulatory ECG monitoring, Arrhythmia analysis, Biomedical Signal

Processing.

Abstract: Wireless technology has become ubiquitous in our daily lives. From 802.11 to Bluetooth we have become

familiar with new technologies and expectations are rife as to its potential. The medical world is potentially

lucrative for the use of such technology. The ability to improve patient comfort, monitor patients remotely

and increase device mobility should all contribute handsomely to patient life quality. It also offers the

unique opportunity to monitor ambulatory patients in a real-time environment. Outlined is an approach to

integrate an Electrocardiogram (ECG) classifier into an overall wireless patient monitoring system enabling

real-time classification and analysis of ECG data. Our research has shown that it is possible to use the open

source classifier (Hamilton, 2002) in a wireless sensor network for beat detection and arrhythmia

classification. We have tested the classifier with up to 80 simulated sensors proving that its lightweight

implementation enables it to cope perfectly with only minor modifications needed. It was found that the

addition of multiples of sensors produced on average 0.01% performance degradation.

1 INTRODUCTION

Ambulatory patient monitoring is crucial to post

operative care in many modern day hospitals. In

many cases the onset of cardiac arrhythmias can

signal a more serious underlying condition.

Traditionally holter monitors were used to classify

infrequently occurring arrhythmias and cardiac

abnormalities in an ambulatory environment. Holter

recordings normally comprise of 24-48 hours of

ECG data, with post-classification occurring after

the recording has completed. Potentially life

threatening conditions and arrhythmias would only

be detected in retrospect. With this in mind it was

decided that, classifying ECG data in real-time,

raising alarms as problems arose, would be

extremely beneficial for both patient and

practitioner.

Classifying arrhythmia’s in a real-time

ambulatory environment poses challenges with

respect to redundancy, reliability and resource

constraints. A beat detector and classifier was

needed that could operate effectively under these

limitations. The Open Source classifier written by

Patrick Hamilton and provided by EP Limited was

chosen as a result of this. With QRS detection

sensitivities close to 99.8% on MIT/BIH and AHA

databases coupled with classification positive

predictivities of 96.48% and 97.83% on MIT and

AHA databases respectively (Hamilton, 2002), it’s

accuracy is excellent. Its QRS detector was

originally developed by Pan and Tompkins and

designed to operate in real-time. All the processing

is done with integer arithmetic so that the algorithm

can operate in real-time without requiring excessive

computing power (Pan, 1985).

This paper aims to demonstrate an approach to

ECG classification in a wireless sensor network. We

show how the Open Source beat detector and

arrhythmia classifier in a Zigbee enabled wireless

sensor network copes easily with the demands of

real-time classification.

ECG data classification is part of a wider project

called VISOR (Enhanced VItal Sign Observation

and Recording). VISOR is an overall wireless

patient monitoring system that monitors respiratory,

pulse oximetry (SP02), and ECG data in real-time,

raising alarms as problems arise. The VISOR project

has been in existence for three years.

322

Barrett E., Chambers D. and Rotariu C. (2009).

ECG CLASSIFICATION AND ANALYSIS IN A ZIGBEE WIRELESS SENSOR NETWORK.

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, pages 322-326

DOI: 10.5220/0001548703220326

 SciTePress

2 RELATED WORK

Extensive research has been carried out in the

application of wireless sensor networks to the

medical domain. Projects such as Harvard’s

“CodeBlue” project (Malan) have proven the

validity of transmitting ECG data over Zigbee, but

they are not classifying the data in real-time. Others

such as the “WISE” (Jovanov, 2001) bio-medical

sensors tend to focus more on data acquisition rather

than data analysis. Fensli propose using sensor

networks coupled with limited arrhythmia

classification in the home (Fensli et al, 2005). ECG

is transmitted from the home via a GPRS/GSM band

to the hospital for analysis by consultants. In

contrast our system aims to detect a wide range of

cardiac arrhythmias, focusing on potentially serious

abnormalities with the view to early diagnosis. Our

system will also display other patient vitals.

While commercially available products such as

the G.E. Apex Pro Telemetry System (GE

Healthcare) and the LifeSync Wireless ECG system

(Wireless ECG) are not using sensor networks to

transmit ECG data, they are proving the commercial

potential of wirelessly enabling ECG.

3 THE VISOR SYSTEM

VISOR is a Zigbee enabled patient monitoring

system, consisting of multiple sensors attached to

multiple patients. These sensors currently consist of

ECG, SP02 and MDI (Medical Device Interface)

sensors. The sensors obtain bio-data from the patient

and transmit it to the server for analysis via a Zigbee

module on the device. The server analyses the bio-

data for eventual display on the client. A database is

used to store all relevant patient demographics and

bio-data for historical playback.

Data produced by the ECG sensors is sampled at

200Hz, and then passed through three second order

band pass filters and the savitzky-golay (Savitzky,

1964) smoothing filter. It’s then transmitted to the

server for classification and analysis. The SP02

sensors transmit the heart rate and oxygen saturation

levels, while the MDI sensor which is essentially a

serial to wireless bridge, interfaces with existing

devices in the ward to obtain patient bio-data. These

devices can range from vital signs monitors to

ventilators and infusion pumps. Our MDI

architecture allows for the addition of new or legacy

medical devices to the system increasing scalability.

3.1 Zigbee

Zigbee’s suitability has already been established by

others as a reliable platform for the transmission of

ECG data (Lin et al, 2006). It has also been shown

(Frehill, 2008) how Zigbee meets our requirements

with respect to bandwidth, scalability and

redundancy when handling real-time ECG data.

Zigbee is based on the IEEE 802.15.4 standard

(IEEE 802.15.4). Its advantages include, mesh

networking, low power consumption, security and

mobility (Zigbee Alliance). The Zigbee mesh

networking framework provides a flexible approach

to routing data in a hospital environment where any

node can be used to route the ECG data. Zigbee’s

low power consumption enables ECG sensors to

have smaller, lighter batteries that run for longer.

This also offers relatively low device maintenance

for nurses and hospital care workers, where sensors

need only be re-charged once every 7 days.

3.2 Custom GUI Client

The central server relays relevant data to all

subscribed clients. Our client software was

developed using the .NET framework, enabling the

software to operate on any windows based device.

Subscribed clients can range from handheld

PDA’s/laptops to PC’s. The client contains all

relevant patient demographics, displaying details of

attached devices and patient vitals. A patient’s

ventilator data, ECG “Figure 1” and plethysmogram

“Figure 2” graphs are rendered for the user to

scrutinize.



If an arrhythmia or alarm condition is detected,

the server will notify associated clients, by raising an

alarm on that client. Annotation codes defined by

physionet (Physiologic Signals) to label events in an

ECG recording are used to identify the type of

arrhythmia that occurred. If the user wishes to view

a particular alarm, the ECG graph can be played

back to view the detected arrhythmia. This allows

critical care workers to monitor patients remotely

and less frequently.

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Figure 1: Client ECG Trace.

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323

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Figure 2: Client SP02 Plethysmogram.

3.3 Wireless Gateway

A PANC (Personal Area Network Coordinator) acts

as a gateway transmitting data from the sensors to

the analysis server as shown in “Figure 3”. The

gateway is responsible for forming the network and

inserting/removing wireless sensor nodes from the

system.



Figure 3: High Level Architecture.

3.4 Central Analysis Server

Our system classifies ECG data on a central server.

Unlike other ECG wireless sensors that classify the

ECG data on the device itself (Fariborzi, 2007), we

feel that classifying the ECG data on the server gives

us scope for scalability, allowing us to eventually

analyze ECG data with multiple ECG classifiers.

Combining multiple classifiers on a tiny ECG

module where memory, battery life and processing

power are at a premium would not be prudent. The

focus of VISOR is to produce sensors that are

lightweight and cost effective (Rotariu, 2006).

Once a node is powered on it begins sending

status messages to the server via the gateway. When

the server receives a status message from a sensor it

adds that sensor to its list of active sensors, only

active sensors can be associated with a patient.

Analyzing bio-data on a single central server can

raise concerns regarding efficiency and scalability.

The VISOR project has placed enormous emphasis

on developing computationally efficient analytical

modules. It has been possible as a result of this, to

maintain our single analysis server architecture, all

the while increasing scalability and cost

effectiveness.

4 OPEN SOURCE CLASSIFIER

To facilitate the processing of live ECG data we

needed a QRS detector and beat classifier that could

operate in real-time. We felt that since the open

source classifier’s QRS detector had originally been

intended to analyse real-time ECG data, it would

cope with the constraints of beat detection in this

unique environment. Its lightweight classifier,

implemented in C, added to its efficiency.

The QRS beat detector was ported to C by

Hamilton and Tompkins (Hamilton, 1986). The beat

classification rules were developed by Hamilton.

The advantages of this QRS detection algorithm are

that it is computationally efficient and easily

modified for different samples rates (Hamilton,

2002). Our ECG sensors produce 1 lead ECG

data sampled at 200Hz. The performance of the

classifier has been shown to be slightly better with

data sampled at 200Hz, compared to data sampled at

higher rates. Its published results were based on a

single channel of ECG data, similar to the data our

sensors produce. Many modern day commercial

classifiers work with 12 lead, 3 channel ECG data to

detect arrhythmias and abnormalities. For these

reasons we felt the open source classifier was the

closest match in meeting our requirements for

efficiency and accuracy.

4.1 Dynamic Link Layer (dll)

Integration

In keeping with the existing server architecture

which supports several devices, the classifier was

implemented as a dll on the analysis server (Frehill,

2007). Within this framework subsequent classifiers

can be added as required by loading their respective

dll’s.

In order to allow for classification in a real-time

environment a number of modifications have had to

be made. The classifier comes with functions to

analyse MIT/BIH and AHA records. It uses the

WFDB library functions to read the ECG data from

the records and write to annotation files. We stripped

the classifier of all WFDB types, rewriting a number

of functions and deleting redundant ones. The entire

C program was converted into OO C++, for the

purpose of instantiating instances of classifier

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objects. This allowed the classification of multiple

sensors in real-time.

With this in place we were then able to modify

and integrate George Moody’s ihr.c. Instantaneous

heart rate (IHR) calculates the heart rate from the

reciprocals of the inter-beat intervals. This extension

enabled the detection of episodes of Sinus

Tachycardia/Bradycardia during normal Sinus

rhythm. Once an episode has been detected an event

is raised on the server that is handled on the client.

4.2 Packet Loss

A single packet loss on the network could result in

the detection of an arrhythmia or unclassifiable beat

due to packet size. The Zigbee software stack

handles packet loss which we have proved in our

laboratory environment. In addition packet sequence

numbers are tracked and if a discontinuity is

detected, the classifier is notified. Any arrhythmias

and beats detected during this window are ignored. It

would be inappropriate to call the reset method on

the classifier, as normal rhythms and beat templates

have already been determined and would be lost.

Packet loss may occur as a result of a sensor moving

out of range or if the battery is fully drained.

5 RESULTS

In the absence at the time of publication of a

sufficient quantity of physical ECG sensors, it was

decided to simulate the presence of multiple sensors

on the system to ascertain results. We placed both

the simulation software and the server software on

separate P.C’s with each P.C containing a Pentium 4

HT processor and 1GB of RAM. The physical ECG

sensors transmit 100 bytes of data at a rate of 1

packet every 500ms, which our simulation software

emulates. For the ECG data simulation we took a

single record at a time, with each sensor transmitting

data from the same record simultaneously. In trying

to mimic a hospital environment, we simulated a

mixed scenario where some patients were associated

with one sensor and some with many sensors. The

data was handled by the PANC and sent to the

server for processing.

Four MIT/BIH arrhythmia records (100, 103,

106 and 119) were chosen to simulate the ECG data.

These were selected for their disparate

abnormalities. Records 106 and 119 contain a

substantial amount of premature ventricular

contractions (PVC) whereas 100 and 103 contain

predominately normal beats. As you can see from

“Table 1” the classifier had lower detection times on

records that contained fewer PVC’s (100, 103).

The performance differential on records 106 and 119

is largely due to the beat classification techniques

involved in arrhythmia detection. It shows that

detecting arrhythmias involves longer detection

times, due to post classification and beat template

matching.

When classifying normal beats the classifier will

compare the beat to the dominant rhythm, if it’s the

same it’s classified as normal.



Table 1: Detection Times (ms).

Record

Number of Sensors

20 40 60 80

100 1436.98 1437.01 1437.07 1437.14

103 1530.00 1530.01 1530.04 1530.06

106 1668.07 1668.07 1668.11 1668.25

119 1642.09 1642.20 1642.32 1642.35

To derive the beat detection average, the

processing times for five hundred beats were used.

From the graph “Figure 4” you will see that there is

virtually no performance degradation even when

tested with upwards of 100 sensors. In fact in the lab

we found that performance only began to lag with 20

times that amount. Theoretically we hope to be able

to classify beats from hundreds of sensors on a

single server. With the advent of multi-core

processors and cheaper faster RAM our server

implementation will scale to meet demands.

Figure 4: Detection Degradation.

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6 FUTURE WORK

We are currently integrating the HeartFx 2000

arrhythmia (Heart FX 2000) classifier developed by

Medata. Unlike the QRS detector in the open source

classifier, the HeartFx 2000 classifier was not

designed for real-time analysis. It is intended instead

to be used in a post classification environment,

analyzing holter recordings.

We hope to compare the performance of both of

these classifiers, with respect to efficiency, accuracy

and reliability while running multiple ECG sensors

transmitting data simultaneously. We feel that

multiple ECG classifiers would give us greater beat

detection and classification redundancy in a range of

disparate environments.

Using the commercial classifier as a reference

we would hope to extend the open source classifier

to detect new arrhythmias such as VT (Ventricular

Tachycardia), VF (Ventricular Fibrillation) and

Asystole. There are plans to carry out usability

testing in a hospital.

7 CONCLUSIONS

It has been shown that it is possible to integrate and

implement an open source ECG beat detector and

arrhythmia classifier in an ambulatory patient

monitoring system. Our results prove that our

architecture is capable of supporting large numbers

of ECG sensors simultaneously. Using Zigbee as our

wireless infrastructure has enabled us to produce low

cost, low power ECG sensors. We hope that, in long

term patient monitoring, where accurate

classification of arrhythmias is crucial, our system

will prove useful.

REFERENCES

Open Source ECG Analysis, P Hamilton E.P. Ltd

Somerville, USA. IEEE Computers in Cardiology

2002.

Pan J, Tompkins WJ. A real-time QRS detection

algorithm. IEEE Trans.Biomed. Eng 1985;32 :230-6.

Paul Frehill, Des Chambers, Cosmin Rotariu. A Wireless

Network Sensor and Server Architecture for Legacy

Medical Devices. In proceedings of 15th International

Conference on Telecommunications. 16-19 June. 2008

David Malan, Thaddeus Fulford-Jones, Matt Welsh, Steve

Moulton. CodeBlue: An Ad Hoc Sensor Network

Infrastructure for Emergency Medical Care.

E. Jovanov, D. Raskovic, J. Price, J. Chapman, A. Moore,

A. Krishnamurthy. Patient Monitoring Using Personal

Area Networks of Wireless Intelligent Sensors.

Biomed Sci Instrum, vol. 37, pp. 373-8, 2001.

Rune Fensli, Einar Gunnarson, and Torstein Gundersen. A

wearable ECG-recording system for continuous

arrhythmia monitoring in a wireless tele-home-care

situation. In Proceedings of the IEEE International

Symposium on Computer-Based Medical Systems

(CBMS), 2005.

GE Healthcare Wireless ECG Device http://www.geh

ealthcare.com/euen/patient_monitoring/products/imm-

monitoring/telemetry/unity-apex-pro-telemetry-

system_index.html

Wireless ECG specification, http://www.lifesynccorp.com/

A. Savitzky and Marcel J.E. Golay (1964). Smoothing and

Differentiation of Data by Simplified Least Squares

Procedures. Analytical Chemistry, 36: 1627–1639.

Jun-Liang Lin et al. The Development of Wireless Sensor

Network for ECG Monitoring. The proceedings of the

IEEE EMBS annual international conference

2006.

IEEE 802.15.4 standard. Available at http://www.ieee802.

org/15/pub/TG4.html

Zigbee Alliance, Zigbee Specification, http://www.

zigbee.org

Physiologic Signals, http://www.physionet.org

H. Fariborzi, M. Moghavvemi, S. Mehrkanoon. Design of

a Low-power Microcontroller-based Wireless ECG

Monitoring System. The 5th Student Conference on

Research and Development –SCOReD 2007 11-12

December 2007, Malaysia

C. Rotariu, Des Chambers, Petronel Bigioi. “Lightweight

PnP ECG sensor for Monitoring of Biomedical

Signals”. In proceeding of 5th RoEduNet IEEE

Internation Conference, June 2006, Pages 17-22.

Hamilton PS, Tompkins WJ. Quantitative investigation of

QRS detection rules using the MIT/BIH arrhythmia

database. IEEE Trans. Biomed Eng. 1986;33:1157-65.

Paul Frehill, Des Chambers, Cosmin Rotariu. Using

Zigbee to Integrate Medical Devices. Proceedings of

the 29th Annual International Conference of the IEEE

EMBS Cité Internationale, Lyon, France 2007

Heart Fx 2000 Specification, http://www.medata.us/

BIOSIGNALS 2009 - International Conference on Bio-inspired Systems and Signal Processing

326