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Measurement 41 (2008) 463–470

www.elsevier.com/locate/measurement

Real-time ECG telemonitoring system design with mobilephone platform

Cheng Wen a, Ming-Feng Yeh a, Kuang-Chiung Chang a,*, Ren-Guey Lee b

a Department of Electrical Engineering, Lunghwa University of Science and Technology, Tauyan 33306, Taiwanb Department of Electronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan

Received 20 August 2006; received in revised form 19 December 2006; accepted 20 December 2006Available online 17 January 2007

Abstract

In this paper, we propose an ECG (electrocardiogram) telemonitoring system based on a mobile phone platform. Ittransmits abnormal heartbeats, which are identified in the patient-worn unit (Holter), in real time by using MMS (multi-media messaging service) on GPRS (general packet radio service) and transfers all ECG data acquired and stored in theHolter by the Internet. By this way, the Holter can be used outdoors, and the communication cost can be reduced withoutlosing important information of patients. The GPS (global positioning system) information provided by the Holter can beused to locate the patient for emergency help. To identify abnormal beats, we develop a real-time ECG classification algo-rithm that can be executed by the dual-core processor in the Holter. Experimental results show that the proposed systemachieves an ECG classification accuracy of 98.98%.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Telemonitoring; Electrocardiogram (ECG); R wave detection; Real-time classification; Multimedia messaging service (MMS)

1. Introduction

Telemedicine, which refers to the utilization oftelecommunication technology for medical diagno-sis, treatment, patient care, and remote monitoring,is currently a significant area of research and devel-opment. It is seen as an important way of reducingmedical costs by allowing for healthcare to beadministered outside a hospital setting. Telemedi-cine can also be used within a clinical institution.One rapid growing area of telemedicine is the

0263-2241/$ - see front matter � 2007 Elsevier Ltd. All rights reserved

doi:10.1016/j.measurement.2006.12.006

* Corresponding author. Tel.: +886 2 82093211x5501; fax:+886 2 82099728.

E-mail address: kcchang@mail.lhu.edu.tw (K.-C. Chang).

long-term monitoring of patients with cardiovascu-lar diseases at home. This is made possible with theemergence of portable ECG (electrocardiogram)telemonitoring systems, which are commonly usedto record the arrhythmia when it happens or torecord ECGs for allowing the experts to see varioustrends. ECG telemonitoring systems can be dividedinto two modes of operations: real-time mode, inwhich patient data are available at the server endimmediately after acquisition, and store-and-for-ward mode, which involves accessing the data at alater time. In both modes, the ECG data are trans-mitted via computer networks [1–3], public tele-phone networks [4], or cable TV networks [5] tothe server. In these system models, an expert is

.

464 C. Wen et al. / Measurement 41 (2008) 463–470

expected at places where he/she can use a PC toaccess the server for analyzing the ECG data, andthe patient is bounded at a fixed place like homeor healthcare center where a PC is equipped fortransmitting the ECG. The use of wired networkconnected PCs limits the degree of freedom of bothdoctors and patients to move around.

To improve the mobility of the doctor, the GSM(global system for mobile communication) mobiletelephony network was used for connecting theserver. The mobile phone has been recognized as apossible tool for telemedicine since it becamecommercially available. By using remote portabledevices such as notebooks and personal digitalassistants that wirelessly connected to a computernetwork via GSM cellular phone, previous studiessuccessfully demonstrated the merging of cellularcommunication and the Internet in telemedicine[6,7]. The role of the mobile phone in these designswas as a wireless modem for downloading datafrom a server to a portable computer. A currenttrend in telecommunication is the convergence ofwireless communication and computer networktechnologies for providing mobile Internet access.Recently, Hung and Zhang [8] implemented aWAP (wireless application protocol) based telemon-itoring system. It utilized WAP devices as mobileaccess terminals and allowed doctors to browsethe monitored data on WAP devices in store-and-forward mode.

The improvement on the mobility of the patientis much less, comparing to the doctor. In many pre-vious ECG telemonitoring systems, the patient-worn unit, known as the portable digital Holter,consisted of an ECG data acquisition circuit, anA/D converter, and a storage unit with a capacitybeing sufficiently large for 24 h of recording. Toprovide a very limited mobility of the patient, theHolter was equipped with an indoor, wireless trans-mitter for feeding the monitored data to a networkconnected PC [4,8]. A GSM modem was equippedwith a PC for real-time transmission of ECG datafrom a moving ambulance vehicle in [9]. The focusof this design was on transmitting ECG data forconsultation while transporting patients in emer-gency cases. This is probably because use of aGSM network is costly, and the data transmissionrate is low. Recently, Rasid and Woodward [10]suggested a mobile telemonitoring system operatingin store-and-forward mode by using a Bluetooth-enabled processor unit, which transmitted the mon-itored data to a Bluetooth mobile phone and subse-

quently via the GSM/GPRS (general packet radioservices) network to the server. On the other hand,Engin et al. [11] used a mobile phone to transmitthe measured ECG signal in real-time mode. Inthese designs [10,11], the mobility of the patient isimproved. However, the analysis of ECG is not per-formed in the place where the ECG is acquired, i.e.,the ECG is analyzed at the server end. In fact, thereis a loss of efficiency in the use of the GSM/GPRSnetwork because normal ECGs are also transferred,which implies a high cost.

In this paper, we propose an ECG telemonitoringsystem operating in both modes. In store-and-for-ward mode, the Holter records the ECG signal ofthe patient continuously up to 48 h. The monitoreddata is transmitted to the server through the Inter-net when a wired network is available. The Holteralso contains a software program performing real-time ECG classification. When an abnormal heart-beat that the doctor concerns is detected, the Holtertransmits it with the GPS (global positioning sys-tem) information to the server via MMS (multime-dia messaging service) in real-time. The doctor atthe server side could communicate with the patientalso by using MMS if necessary. In the server, aGIS (geographic information system) is resided forlocating the patient in an emergency case by usingthe GPS information packaged in the MMS mes-sage. By this way, the Holter can be used outdoors,and the cost for using the GPRS network is loweredbecause only abnormal ECGs are transmitted.

The remainder of this paper is organized as fol-lows. The system is described in Section 2. The pro-posed system consists of a Holter and a server. Thehardware and software designs of the Holter aredescribed in Section 3. The system has been imple-mented and tested. Experimental results arereported in Section 4. Finally, Section 5 containssome discussions and conclusions.

2. System description

Fig. 1 illustrates the proposed telemonitoring sys-tem. This system consists of a portable Holter, and aserver. The Holter is responsible for ECG dataacquisition, storage, and real-time analysis. In theHolter, a software program is used to classify theinput ECGs into several beat types. In case thatthe program classifies a current ECG beat into abeat type that the doctor concerns, the Holter trans-mits the ECG data with the GPS information to theserver by using MMS in real time.

Fig. 1. System model.

C. Wen et al. / Measurement 41 (2008) 463–470 465

MMS is a new standard in mobile messaging. Itis a communications technology developed by3GPP (third generation partnership project) thatallows users to exchange multimedia communica-tions between capable mobile phones and otherdevices. It is also possible to send MMS messagesfrom a mobile phone to an email address. MMScan include not just text, but also sound, imagesand video. An MMS message is a single entity,not a collection of attachments, and has no sizelimit. MMS requires a third generation network toenable such large messages to be delivered, althoughsmaller messages can be sent even with second gen-eration networks using GPRS. MMS confirms eachexchange of message by giving a confirmation mes-sage, as shown in Fig. 2. This property prevents the

Fig. 2. Information flow of MMS starts from MM1_sub-mit.REQ, the Holter sends a request for submission of anMMS message to the MMS center, and ends up at MM1_deliv-ery_report.RES, the Holter responds the delivery report from thecenter. Information exchange in each step is confirmed by usingrequest and response messaging.

loss of information and makes MMS a particularlyappropriate communication tool in the telemonitor-ing system design. Note that, in this design, thecommunication between the MMS center and theserver is through TCP/IP protocol to the communi-cation gateway equipped with the server.

When the server receives the monitored ECGdata, it stores the data and automatically streamsthe data to authorized doctors for diagnosis. Afterexamining the ECG data, the doctor can send afeedback MMS message to the user. The messagemay contain medical advice and/or a list of controlcommands to the Holter for resending the abnormalECG data, transferring a specified period of ECGdata for further examination, or tuning parametersof the ECG analysis algorithm. The transmissionof ECG data in this mode can also be operatedmanually. Whenever the user feels uncomfortable,he/she can transfer his/her current ECG data tothe server for advice or a check up. Finally, byincorporating the GPS information packaged inthe MMS message, the GIS resided in the servercan be used to locate the patient in an emergencysituation.

In store-and-forward mode, the Holter transfersthe entire long-term (up to 48 h) monitored datapackage through the Internet to the server for com-prehensive analysis. When receiving the package,the server stores the data and streams the data toauthorized doctors when requested. Because thedesign and implementation of the store-and-for-ward mode is quite easy, the focus will be mainlyon the real-time mode in the following.

3. Portable holter

3.1. Hardware

Fig. 3 shows the hardware block diagram of theHolter, which is based on a mobile phone platform.

Fig. 3. Hardware of the Holter.

Fig. 4. ECG signal consists of three basic waves: P wave, QRScomplex, and T wave.

466 C. Wen et al. / Measurement 41 (2008) 463–470

In the ECG signal acquisition module, a one-lead(Lead II) ECG signal is detected by pre-jelled elec-trodes, fed into a passive AC coupling with a cutofffrequency of 0.01 Hz that highpass filters the volt-age difference to reduce baseline wander and ampli-fier saturation, amplified by an instrumentationamplifier with a gain of 1000, and filtered by a sec-ond-order Bessel low-pass filter with a cutoff fre-quency of 160 Hz for removing high frequencynoise or movement artifacts. The ECG signal is dig-itized by an A/D converter at 360 Hz with a resolu-tion of 11 bits. After being digitized, the ECG datais fed into a dual-core processor1 for analysis andstorage. This processor integrates a DSP with anARM (advanced RISC machine) and is widely usedin mobile phones. The analysis is done by the soft-ware program resided in the processor. When aninput ECG is classified into a beat type that the doc-tor concerns, the processor packages the ECG withthe information provided by the GPS module intoan MMS message and sends the message to the ser-ver through the GSM/GPRS module. The LCDtouch panel is used for the selection of the operationfunctions shown on the screen and to display themessage from the doctor. The external flash mem-ory of 1 G bytes is used for storing the ECG data,record time, location of the R wave, and estimatedECG beat type. This capacity is sufficient for 48 hof recording. The USB port is for the connectionwith the PC, and the Ethernet port is for the connec-tion with the Internet.

3.2. Software

The classification of ECG beats is a difficultproblem even with the aid of a computer, sinceECGs differ significantly even for the same typeand for the same patient. Due to the limited compu-tation power of the processor, the goal is to developa simple and effective software program that can beexecuted in real time. Fig. 4 shows a typical ECGbeat, where the R wave is the first positive (upward)deflection of the QRS complex. The software pro-gram resided in the processor consists of an R wavedetection routine and an ECG classification routine.In the following, we will first introduce the R wavedetection routine and then the classification routine.

The accurate detection of the R wave is the baseof the automatic ECG recognition. There has been

1 OMAP5910.

much work done in this area by PC-based methods[12–14]. So and Chan [14], developed a real-time Rwave detection algorithm, in which the QRS onsetpoint was selected by exceeding a self-adaptive slopethreshold in two successive values of slope and theR wave was found by searching the first highestpoint following the onset. Their algorithm is simpleand effective and can be implemented in a micropro-cessor. An alternative adaptation rule for findingthe onset point was developed by our group [15]and is used here. The real-time R wave detectionroutine used is summarized as follows.

Routine 3.1

Step 1: In the first 1500 ms, find a maximum of thedifferentiated ECG data as M(0). Set the initialthreshold to the slope as T(0) = aM(0).Step 2: When two successive values of slope thatexceed the threshold, the first point is recognizedas the QRS onset point.Step 3: The R wave is detected by searching in thedifferentiated ECG data the first zero followingthe onset point.Step 4: The self-adaptive threshold is obtained byT(n) = bM(n) + cT(n � 1).

The three constants, 0 < a,b,c < 1, in Routine 3.1depend on the filters used in the ECG signal acqui-sition circuit. In general, a small threshold has ahigher sensitivity to the detection of R wave thana large threshold does, but a lower immunity tonoise. The current maximal slope M(n) is obtainedby searching after 200 ms of the M(n � 1) in theinterval where the value of slope is greater thanthe threshold T(n � 1). Note that no R wave detec-tion is allowed 200 ms after the current one isdetected in Step 3. In this time interval, a new valueT(n) in Step 4 is calculated and the beat classifica-tion algorithm described in the below is executed.

C. Wen et al. / Measurement 41 (2008) 463–470 467

We use the QRS part of the ECG signal toachieve beat classification. After detecting the Rwave, Routine 3.1 extracts the QRS complex outfrom the beat by using a 73 samples (about200 ms) window centered on the R wave and feedsit to the classification routine. The window lengthis selected to ensure that also very wide durationPVCs (pre-mature ventricular contractions) can beincluded. To avoid complicated computations, weuse the waveform of the QRS complex withoutany feature extraction in the classification algo-rithm. The proposed beat classification algorithmis based on our previous work [16], which followedfrom the ART2 (adaptive resonance theory) neuralnetwork initially introduced by Grossberg in 1976[17,18]. Without preliminary training, ART net-works not only allow category templates to adaptto current circumstances, but also allow on-line cre-ation of categories during clustering sessions. Thistwofold flexibility is very useful in ECG clusteringproblems, because the ECGs may differ from ses-sion to session [19]. However, the clustering resultof the ART has to be annotated by a cardiologistin order to achieve the purpose of classification.This fact implies that ART networks cannot per-form ECG classification in real time.

In order to classify heartbeats in real time, it isrequired that a doctor assigns some typical beatsof the patient as initial templates, yj, of the proposedmethod. The initialization will not cause any prob-lem in clinical practice because there must existsome ECG recordings of the patient before the doc-tor decides to long-term monitor the patient. Then,input beats are compared with those templates asfollows.

Routine 3.2

Step 1: For an input beat, say x(k) = [x(1), . . . ,x(73)], compare it with all templates, yj, by usingthe similarity measure [20]:

gðx; yjÞ ¼Xn

k¼1

½wðkÞ � rðxðkÞ; yjðkÞÞ� ð1Þ

where w(k) P 0 is the weighting function withPnk¼1wðkÞ ¼ 1. In the above equation,

rðxðkÞ; yjðkÞÞ ¼d

DjðkÞ þ dð2Þ

where Dj(k) = jx(k) � yj(k)jand d > 0 is a constant.The winner, denoted by j*, is the one yielding thelargest measurement maxj{g(x,yj)}.Step 2: The neuron j* passes the vigilance test if andonly if gðx; yj� ÞP q; where 0 < q 6 1 is the vigilancethreshold. In this case, the input is classified into thecluster that the winner belongs, and the weightvector of the winner j* is adapted by

yðnewÞj� ¼

NumðclusterðoldÞj� Þ � y

ðoldÞj� þ x

NumðclusterðoldÞj� Þ þ 1

ð3Þ

where Numðclusterj� Þ denotes the number of mem-bers in clusterj� .Step 3: If the winner fails the vigilance test, a newneuron unit j is created with the input as its tem-plate, i.e., yj = x.

Adaptation rule (3) finds the center of gravity ofa cluster and does not suffer from selecting anappropriate learning rate. The most important fea-ture of the similarity measure (1) for the selectionof the vigilance threshold is that we can alwaysguarantee that 0 < g(xi,yj) 6 1 no matter how largethe input data is. As a consequence, the vigilancethreshold q is limited to the range (0,1]. In general,a larger vigilance threshold value may result inhigher classification accuracy but more clusters(neurons) than a smaller value does. The more theclusters, the much heavy the computational load.It is required that Routine 3.2 classifies an inputbeat within 200 ms, the time interval that Routine3.1 stops to detect the next R wave, while the pro-cessor reads an ECG data every 2.78 ms (360 Hzsampling rate), calculates T(n) (Step 4, Routine3.1), stores the classification result, reports anabnormal beat to the server if necessary, andreceives an MMS message from the server thatmay contain a list of control commands. As a con-sequence, a limit on the number of clusters is neededto avoid the overload of the processor in the real-time classification procedure.

When a new neuron is on-line created in Step 3,the template of the new neuron is sent to the doctor.The doctor labels the beat type of the template andsends it back for achieving real-time classification.A false alarm occurs when a normal beat is misiden-tified as an abnormal beat or an abnormal beat ismisclassified into a cluster dominated by a differentabnormal type of beats. In either case, to improvethe classification result, the doctor can on-lineadjust the vigilance threshold value, create a new

468 C. Wen et al. / Measurement 41 (2008) 463–470

neuron with an assigned template, or delete an exist-ing neuron if it causes false alarms frequently bysending an MMS message to the Holter.

Fig. 6. R wave detection result.

4. Experimental results

The design of the Holter has been implemented inour laboratory by using off-the-shelf components. Aphotograph of the prototype, measuring 90 mm ·65 mm · 35 mm, is shown in Fig. 5. The softwarewas developed in JAVA language using CLDC(connected limited device configuration) 1.0 andMIDP (mobile information device profile) 2.0 on aLinux OS. For a preliminary test of the real-timeECG classification, a volunteer carried the Holterfor 24 h. The number of initial templates (neuronunits) selected from the previous recording of thevolunteer was 40 in Routine 3.2. Because the volun-teer had no heart diseases, the number of final tem-plates remained the same. During the 24 h, nooverload was observed. Fig. 6 shows the result ofthe real-time R wave detection, in which the Rwaves are marked by circles.

Next, we used the MIT/BIH Arrhythmia Data-base [21], sampled at 360 Hz with 11 bits resolution,to test the real-time ECG classification algorithm.This database contains a wide variety of QRS com-plex morphologies and different types of noise andartifacts. It consists of 48 records of 30 min, and

Fig. 5. The prototype of the Holter is composed of three double-side PCBs. PCB1 includes an LCD module and a DC-DC fly-back converter for supplying a higher voltage for the display.PCB2 includes a port for the connection with the ECG signalacquisition module (not shown), an ADC, a dual-core processor,an external NAND flash memory of 1 G bytes, and an Ethernetchip. PCB3 includes a power-management circuit, a GSM/GPRSmodule, and a GPS module.

each of them has two channels (leads) of ECG sig-nals. Accompanying each record is an annotationfile in which the R wave location and beat type ofeach ECG beat has been labeled by expert cardiolo-gist annotators. Excluding records containing thepaced beats (record 102, 104, 107, and 217), we used44 records of the MIT/BIH database. Raw datafrom channel 1 (with some beats missing) were usedin this experiment for detecting arrhythmias. Tosimulate the effect of the filters in the ECG signalacquisition module, we used digital filters to filterthe raw data in a PC. Then, the pre-filtered datawas stored in the external memory of the Holter.The processor read a sampled data every 1/360 sfor real-time analysis.

The AAMI recommended practice [22] was usedto combine the MIT/BIH beat types into four clas-ses. Class V contained ventricular ectopic beats,class F contained fusion of ventricular and normalbeats, class Q contained unclassifiable beats, andclass N contained normal beats, bundle branchblock beats, supraventricular ectopic beats, andother beats that do not fall into the V, F, or Q clas-ses. Accordingly, in the algorithm, a class mightcontain several subclasses (beat types).

The beat classification was done record byrecord. Each record had its own initial templatesselected from that record. In addition to the fourclasses, a special cluster, named cluster R, was pre-set without a template, i.e., this cluster was not usedto classify input beats, but for collecting beatswhose R wave were incorrectly located by Routine3.1. For each record, the number of final templateswas limited to 40, according to the result of the pre-liminary test. In the test, when an input beat wasclassified into an abnormal (V, F, and Q) class ora new cluster was created on-line, the Holterreported it to the server. To simulate the functionof the doctor, the R wave location and beat typeof each ECG beat provided by the MIT/BIH data-

Table 2Details of the classification

Record#

q Initialtemplates

Finaltemplates

Accuracy

100 0.78 1N 1N1F 100.00101a 0.78 1N1Q 1N1Q 100.00103a 0.75 1N 2N 100.00105a 0.77 1N1V 19N1V2Q 99.53106 0.82 1N2V 1N2V 99.65108a 0.93 7N3V2Q 12N10V1F2Q 99.49109 0.90 4N3V1F 8N6V1F 99.96111a 0.82 4N 4N1V 100.00112a 0.77 1N 3N 100.00113 0.84 2N 2N 100.00114a 0.88 1N1V 1N1V 99.73115a 0.81 1N 2N 100.00116 0.82 1N1V 3N2V 99.96117a 0.80 2N 2N 100.00118a 0.89 2N1V2Q 4N2V2Q 99.91119 0.86 1N1V 1N2V 100.00121a 0.76 2N 5N1V 100.00122 0.79 1N 1N 100.00123 0.81 2N 2N1V 100.00124 0.89 1N1V1F 1N2V2F 99.69200a 0.89 1N2V 1N5V 98.19201a 0.86 4N 7N4V1F3Q 99.75202 0.84 1N1V 1N1V1F 99.86203a 0.80 1N1V 8N4V 95.87205 0.89 1N2V 1N4V 99.59207a 0.92 3N12V 3N13V 99.01208a 0.90 2N3V3F 6N12V5V 97.16209 0.77 1N 3N1V 100.00210a 0.88 1N2V 3N2V 98.45212a 0.78 2N 2N 100.00213 0.88 1N2V2F 2N8V14F 94.37214a 0.88 1N6V 10N7V 96.06215a 0.88 1N4V 1N5V 99.76219 0.88 2N3V 2N3V1Q 97.20220 0.79 1N 1N 100.00221a 0.90 1N4V 1N8V 99.96

a

C. Wen et al. / Measurement 41 (2008) 463–470 469

base was used in the server. When the serverreceived a message, it first checked the correctnessof the R wave location found by the Holter. In casethat the R wave location was incorrect, the serverlabeled the current ECG beat as an ‘‘R’’ beat andsent the answer back to the Holter. If the R wavelocation was correct and beat type was blank, whichmeant that a new cluster was created, the serverlabeled the ECG beat according to its annotationand sent it back for labeling the new cluster in thealgorithm. When the beat type was labeled, the ser-ver checked the correctness of the beat type. If thebeat type was right, the server recorded an alarm.If it was wrong, the server corrected the beat type,recorded a false alarm, and sent it back to the Hol-ter for creating a new cluster. Note that the on-lineadjustment of the vigilance threshold value was nottested in this experiment; instead a fixed value foreach record was used (see Table 2).

Over the total 101,371 testing beats, there are 514beats in 44 R clusters (one for each record). TheMIT/BIH database annotation file is used to com-pare beats outside the R clusters. The accuracy is100%. This means the overall accuracy of the Rwave detection procedure is about 99.5%. For thosebeats outside the R clusters, Table 1 shows the clas-sification result in terms of a confusion matrixdivided into the different classes. The main diagonalshows the number of correctly classified beats.From Table 1, we see that, in class N, there are92,456 beats classified correctly, but 470 are classi-fied incorrectly. The reason why there are no beatsin N being misclassified into V or F is that the servercorrects those beats. On the other hand, since theHolter is designed to report abnormal beats, thereis no chance to correct an abnormal beat when itis misclassified into N. As a consequence, Routine3.2 misclassifies 469 beats (253 in V, 154 in F, and62 in Q) into N. There are 684 false alarms duringthe entire classification procedure. Among which,

Table 1Classification result

N V F Q

n 92,412 253 154 62v 0 7189 0 0f 0 0 642 0q 0 0 0 145

The results from the individual records are added together. Theannotation classes, N, V, F, and Q, are depicted on the horizontalaxis, whereas the resulting classes, n, v, f, and q, are shownvertically.

514 false alarms are caused by the incorrect detec-tion of the R wave, 111 are caused by reporting thatnew clusters were on-line created, and 59 are causedby misclassification. The total number of beats inthe algorithm created clusters is 15,281, and number

222 0.79 2N 5N 100.00223a 0.87 1N4V 5N14V3F 88.68228a 0.88 1N4V 1N4V 99.85230a 0.79 2N 3N1V 100.00231 0.87 1N1V2Q 1N1V2Q 100.00232a 0.77 1N 5N 100.00233 0.87 1N4V 1N4V 99.45234a 0.79 1N 1N1V 100.00

This table shows the vigilance threshold value q, the number ofinitial templates, the number of final templates for each selectedrecord, in which xNyV, for example, denotes that the number oftemplates in class N is x, and in V is y, and the classificationaccuracy of the entire algorithm including R wave detection.

a This record is noisy.

470 C. Wen et al. / Measurement 41 (2008) 463–470

of beats in the doctor created clusters is 570. Thetotal number of misclassified beats is 253 + 154 +62 + 59 = 528, which corresponds a classificationaccuracy of 99.48%. Including the number of beatsin R clusters, the entire classification accuracy is98.98%. Table 2 shows the details of the classifica-tion. It is interesting to know the performance ofthe proposed design against noise and movementartifacts. From Table 2, we see that there are 27noisy records containing 62,708 beats, and 17 cleanrecords containing 38,663 beats. The algorithmincluding Routines 3.1 and 3.2 achieves an overallaccuracy of 99.24% for clean records, and 98.81%for noisy records. The two accuracy values differnot much from each other. This shows that the pro-posed algorithm is robust against noise and move-ment artifacts.

5. Conclusion

We have presented the design and implementa-tion of an ECG telemonitoring system, in whichthe real-time analysis of ECG was performed in theplace where the ECG was acquired. Experimentalresults showed that the proposed system couldachieve ECG classification in real time with an over-all classification accuracy of 98.98%. During the 22 h(30 min per record) of the experiment, the Holtersent 8493 messages (about 8.38% of the testing beats)including 652 false alarms. When comparing with themethod of using the GSM/GPRS network continu-ously for real-time transmission of ECG, the com-munication cost of the proposed design is reduced.

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