Development of a wearable biomedical health-care system

Development of

a

Wearable Biomedical Heath-Care

System

Han-Pang Huang 1, and Lu-Pei Hsu2

'Correspondence, Professor ofDepartmentMechanicalEngineering,National TaiwanUniversity, 106, Taipei, Taiwan E-mail. hanpang@,ntu.edu.tw, TEL/FAX. +886-2-23633875

2Graduate studentofGraduateDepartmentMechanicalEngineering,National TaiwanUniversity, 106, Taipei, Taiwan

Abstract-Thispaper develops a wearablebiomedical health-care system which incorporates the wearable computing

technology with multi-agent software architecture to provide a

solution to the medical-care problems. The embedded Linux

based wearable medical computer connects various sensors

which can detect the patients' vital signs, such as

electrocardiogram (ECG), bodytemperature, andbodyhumidity.

Inorder to get morereliableand accurateinformation,the

multi-sensor fusion algorithm is employed to integrate the sensor

information.Thefilterbanks basedQRScomplexand heart beat

detection algorithm, and fuzzy fusion algorithms are

implemented to construct the diagnosis system of this wearable

biomedical computer. Furthermore,the PersonalNursing Agent (PNA) andmulti-agent basedinfrastructure formodern patient information and management system will be developed. The

experimental results show thatthissystem ishelpful and useful formanagingandmonitoring patients. Accordingly,the wearable

biomedical health-care system is regarded as a highly efficient biomedical system and inherits both features of multi-agent architectureand wearablecomputing, i.e.,decentralized, reliable,

autonomous,unrestrictive,andcooperative features.

Index Terms-wearable, electrocardiogram, embedded Linux, and PNA

I. INTRODUCTION

Since the twenty-first century, aging population is emerged as a preeminent worldwide phenomenon. The large proportion ofelderly population leads to a series of medical problems, such as inadequate medical care resource, low quality of medical service. These incoming problems have strong impact on hospitals and nursing homes. It is getting moredifficulttoprovide good health-careprograms for those patients.Moreover,insufficientamountsof doctors andnurses can not monitor these large numbers of elderly people who have unstable health conditions. In order to solve these medicalproblems, thispaperdevelops awearable biomedical health-care system whichintegrates the wearable computing technology and multi-agent software architecture. This system improves the traditional bedside patient monitoring system that is only usedin ICU(intensive care unit). Theproposed biomedicalsystemprovides modularized functions and has the capability ofmonitoringmobilepatients.

In the past, many researchers were focused on the development of home automation and wearable computing fields, but few of them triedto integrate these two fields. In

this paper, home automation software architecture and wearable computing areintegrated and appliedtothe medical institutions for improving their present medical health-care systems. Multi-agent architectureisadopted and implemented inthis medical health-caresystem.Thepersonalnursing agent concept isproposed. Thisagent serves as apersonalnursethat monitors a patient's health condition 24-hour a day. The embeddedLinuxbased systemwhichwirelessly connects the patient information and management system is constructed. The vitalsigns acquisitionmodules areimplemented, suchas ECG acquisition module, body temperature sensing module, and body humidity sensing module. Multi-sensor fusion technology is applied to this biomedical health-care system. The filter bank based QRS detectionalgorithmisimplemented toprovideahighaccuracyheart beatdetectiontechnique.

This paper is organized as follows. In section 2, an integrated architecture for health-care system is proposed. Thissystem isusedtoimprovethe traditional bed-sidepatient monitoringandmanagement system. Theimplementation and experimental results of the wearable biomedical health-care system willbepresentedinsection3.Finally, conclusions are given in section4.

II. INTEGRATED ARCHITECTURE FOR 24-HOUR

HEALTHCARE SYSTEM

Patient monitoring andmanagementhas beenaresearch topicforyears.As showninFig. 1,patient management and monitoring system can be divided into two major research components: patient monitoring system and patient management system. Ingeneral, thepatient monitoring system is bedside medical monitoring system and only in charge of single patient. Meanwhile, the patient management system manages all hardware and software infrastructures in a building such as hospitals. Thepatient monitoring system is

composed of four units: sensing units, information and monitoringsystem, lifesupportfacilities, and physicians. This monitoring system is aclassical bedside medical monitoring instrument. The vital signs from the patient are sensed,

amplified, filtered, and acquired. Afterward, these signals in the information fusion blockareprocessed by algorithmsthat allow,inasomewhat limitedmanner,waveform detection and delineation. Events are also detected and alarms are then triggeredintermsof different levels. Theseprocessed signals

stored in the database. Watrous described aneural network

ECG monitoring algorithm to reduce the number of ventricular and normal beats classification errors [15].

Muhammad proposed an algorithm based ondigital filtering,

adaptive thresholding, statistical properties, and differencing of local max-min for real-timemeasurement of the fetal and maternal heartrates[5].

0 0 _ _S,zlie>-[l-0il-t

Fig.1Patient Monitoring and Management System

Although there are many relevant researches on the

development of biomedical systems, they seldom address the whole architecture ofpatient monitoring andmanagement. A

review paperwrittenby Mora reported the relevant works on

patient monitoringandmanagement systems [9]. Itcoversthe

traditional bedside patient monitoring, some medical

information fusion flow and algorithms, and detailed information analysis. But the report does not mention about the whole architecture as depictedin Fig. 1. Compared with

other researches, Varady [13] proposed an architecture for

patient monitoring which focuses on the development of an

industry standard based architecture to provide an open and

standard system. The patient monitoring and management architecture described aboveis atypicalconceptthatcanmeet most requirementsingeneral medicalinstitutes.However, the

patient monitoring system in Fig. 1 is a bedside monitoring

system and is useful only when patients lie on the bed.

Emergency might occur while patients are not lying on the

bed. In otherwords, theprevious architecture is not suitable formonitoring "moving"patients.Moreover, themanagement functionality of that architecture will be failed if most of patients do not lie on the bed all day. Obviously, the

management system is not workingin this case. As aresult,

theability of mobilitymanagement should beoneof themain

concerns inthe modern patient monitoring and management

system.

A. Multi-Agent Architecture for Health-Care System Inordertoprovideasafe andconvenient environmentfor patients who livein hospitals andnursing homes, the patient

monitoring and management system should have the following features:

* Full-time health condition tracking: Somepatients might

breakout sometimesbecause of their unstable physical conditions. These unpredictable accidents may cause

sudden death. The system should inform medical personnel immediately when the accidentsoccur.

* Reliability: This system manages important facilities in the building. It can not crash due to failure occurred in the sub-system.

* Autonomous: Due tolimited number of nurses, the system should give apreliminary diagnosis tohelp nurses and doctors monitor the health conditions of patients. The multi-agent system is a highly efficient system which provides reliability and extensibilityinanautomation system. Thus, this paper adopts the multi-agent architecture to fulfill the needs ofpatientinformation andmanagement system. On-line monitoring of the patients' conditions is the most important characteristics in the health-care system. In the proposed architecture, it also provides a mobile monitoring device whichisworn onthepatient.This wearable devicecan gather the vitalsigns, which areECGsignal,heart rate, body temperature, and body humidity. Medical personnel can monitor and act appropriately according to these vital signs. Theoverviewof the proposed architectureisshowninFig. 2.

t N

Fig.2Overview ofProposedArchitecture

The

proposed

system consists of three

major

components:

personal

nursing agent

(PNA),

agent studio and central bulletin board,asshowninthe

'The system can be further divided into several zones.

Eachzone represents an

independent working

group and can

communicate with each other via the central bulletin board. The central bulletin board is a

place

where every component

of the system announces its messages and records important system informnation such as alarmnmessages. A zone consists of an agent studio and several

personal

nursing agents and works as a small

multi-agent

system. In other words, an

individual zone is a

complete

system and can work well without others. In the

hospital

which

adopts

this

integrated

architecture,

generally,

a zone represents an individual room

andis

regarded

as acontrol space. Apatientwho lives inthe

hospital

can wear amobile device whichis apartof

personal

nursingagent. The

personal

nursingagentcarriesthe

patient's

personalinformationsuchasIDand

password.

B. Collaboration betweenPersonal

Nursing

Agentsand

Agent

Studio

Each room is treated as a zone in the

proposed

architecture and each

personal

nursing agent represents a

patient

intheroom,as shown inFig. 3 and Fig. 4. The agent studio

manages

room devices and

personal

nursing

agents.

It

communicates with the

personal

nursing

agents through

IEEE 802.1lb wireless network

protocol.

The agentstudio canturn

:"-on and off the lights and adjust the air conditioner to the desired temperaturewhen the agent studio receives acontrol command either frompersonalnursing agent orfrom the agent outside the zone. Once personal nursing agent sends the registercommandtotheagent studio, theagentstudio checks the IDandpassword sentfrom thepersonal nursing agent. If registration is successful, the agent studio will send the registrationinformationtothe central bulletin board.

Qdtid&

i.AL

PNA V sAle rt \/ 1 gFEr.[ 802.1lb Aii PNA Conditioner PNA H [.ight Rooi-n

Fig.3Collaboration between Agent Studio and PersonalNursingAgents C. Collaboration betweenAgent Studios

In this proposed architecture, every message sentby an agent will be recorded on the central bulletin board. In addition, doctors can post their messages and commands on the central bulletinboard, and then the command will besent totheagent.

Fig.4Collaboration between Agent Studios

D. Multi-Sensor Fusion and Integration for Biomedical Health-CareSystem

Inthis biomedical health-caresystem,multiplesensors are used to sense some useful vital signs. These vital signs are usedto preliminarily diagnose thepatient condition andhelp doctors make decision.Fig.5 shows the functionaldiagram of multi-sensor fusion andintegration of this biomedical health-care system. Multiple sensors sense the vital signs all the times, and vital signs will then be mixedtogether since only

one single-chip processor is in charge of acquiring sensor signals. ECG signals need to be preprocessed orderto filter out the noise. In the stage ofmixed-signal separation, ECG signals are extracted from the mixed signal and sent to the fusionstage. Others are also separated outand interpretedto some meaningful values by interpreter. In the fusion stage, twoalgorithmsareimplementedtocalculate the heartrateand make decisions to the action unit. The filter-banks QRS

detectionalgorithmisused to detect the QRS complex in ECG diagram. QRS complex is animportant wave for determining someuseful heart conditions.Finally, the fuzzy diagnosis unit collects all information to determine the patient's physical condition.

SE,n conlrollcr Uni

W orldModlel SE llhol-1.vel Ll->>DiagnoAsis <

Fusiorll

Fewrl,c lI;storica.,

_____ Filter-B3ank.C1S, tcir * 1'G

> iaLnal-L ee_ ___2i______ _

Sepraate OpcrationB

ixe-- a

-Fig. 5 Multi-Sensor Fusion and Integration of Biomedical Health-Care System E. Software System

The detailed system components and the interaction betweencomponentsof theproposed system are given inFig. 6. Personalnursing agent isthe lower-left block andconsists of five components which are sensor datainterpreter, fuzzy-based diagnosis system, personal profile, communication management, and user agent. In this figure, the personal nursing agent and the agent studio are distinguished by different colors. The personal nursing agent is painted light blue and the other is painted light red. All components are working together on either a room gateway or a wearable computer.

Fig.6Interaction and DetailedComponentsofSystem F. Sensor DataInterpreter

The most

important

task of sensor data

interpreter

is to interpretthe

rough

data to the useful data. There are several

body

sensors for sensing the vital signs of a patient

simultaneously;

and this will leadtothe

following problems:

* Mixed vital signs: The EGG sensor,

body

temperature

sensor and

humidity

sensor sense the vital signs all the times.

* Heart-ratecalculation: The R-R interval of the ECG chart represents the period of heartbeat. Therefore, we can obtain the heartratebyreversingthe period.

Fig. 7 shows the processing flow of the sensor data and the functionality of thesensor datainterpreter. Threetypes of signals aremixed together before they are sent to the sensor datainterpreter. Aftersignalsareprocessed by thesensordata interpreter, ECG signals will be separated and sent to the communicationmanagement. Heart ratewill be calculated and sent to the communication management. Body temperature and humidity will be separated andsent tothecommunication management. The values of heart rate, temperature and humidity will besent tothe fuzzy based diagnosis system,and thepatient condition should be diagnosed andcurrent patient status willbe determined.

FCGi Sensour

1'empcrat,rc 805;Conl" ae

Fig. 7 Flow Chart of Sensor DataInterpreter G. QRS Complex and Heart Beat Detection Algorithm

Detecting QRS complex in the EGG is one of the most important tasks. This stage is crucial in basic EGGmonitoring systems and is important for all other EGG processing applications.

TheEGGcharacteristic shape, shown in Fig. 8, is served as the basis for the automated determination of heart rate. QRS detection is difficult because of various types of noises in the EGG signal. Noise sources include power line interference, baseline wander, artifacts due to electrode motion, and muscle noise. Many detection algorithms have been proposed, for

example,

artificial neural networks [14], genetic algorithms [11], wavelet transforms [12], filter banks [1], and so on. Due to the real-time requirement of this system, the detection algorithm should be less computation load and high accuracy. Fortunately, most important algorithms have beenmade a detailed comparison by Kohler [6]. That research paper reports that different algorithm has different performance. Two parameters are used to evaluate the algorithms; that is,

TPFTn Sensitivity.qureen.. (1)

+p~

TP Positive

TP

+ FP predictivityshul... (2)

where TP denotes the number of true positive detections, FN the number of false negatives, and FP the number of false positives.

The comparisonresults show thatmostof the algorithms have high sensitivity and positivepredictivitymorethan 99%. However, not all of them are tested against a standard database; that means not all the comparison results are reliable. This paper adopts a filter bank based algorithm proposed by Afonso [1]. In that comparison report, the filter bank based algorithm has both advantages of high accuracy and low computational load. The sensitivity and positive predictivity of the algorithm have the values greater than 99.5%. Therefore, the filter bank based algorithm is suitable for the on-line QRS complex and heart beat detection techniques. Somerelevant researches of the filter bank based QRS detection algorithm can be found in references [2][3][4][1O].Afilter bankis anarrayofband-pass filters that span the entire audible frequency spectrum, see Fig. 9. The bank is used to isolate different frequency components in a signal. The amplituderesponse ofthe filter bank is shown in Fig. 9,where w isthe bandwidth of the filter. The dB is the amplitude of thesystemfunction andisreferredto asthegain of the system. Inorderto facilitate the detectionprocess, the ECG signal should be preprocessedprior to the filter-banks detection [10].

QRFS C'11 x

dB

STOR WT

vT-0 ;_

Fig.8Diagramof ECG Waves and Fig.9Filter Banks Interval

H. Fuzzy-based Diagnosis System

This health-caresystemhas theabilitytodetermine which patient isindangerousbyusingfuzzy-based diagnosis system topreliminarily diagnose the patient'sphysical condition. This fuzzy-based diagnosis systemdiagnoses the patient's physical condition interms of three inputvital signs, heart rate, body temperature, and body humidity. A warning level which rangesfrom0 to 10 correspondstothe threeinputvitalsigns, as shown in Fig. 10. The warning levels of the patient will then begroupedinto three levels: normallevel, noticed level, anddangerous level (Fig. 11).

As discussed in previous section, the arrhythmia might occur sometimes and cause sudden death. It can be detected by reading ECG.It isdifficulttogivean accuratediagnosis of

arrhythmia, since it isrelatedto notonlydetectionalgorithms

but also well-understanding of domain knowledge. Thus, in additiontothese three kinds of vitalsigns, suddenincrease or decrease of heart rate can be used to diagnose the patient condition.

The basic architecture ofafuzzy decision logicsystem[7] is shown inFig. 12, whereX means the crispmeasured data and Y the crisp output value, i.e., the input vital signs and outputwarninglevel,respectively. Thefuzzyrule basestores

the empirical knowledge of the process operation of the domain experts.Here,the domainexpertswill bedoctors, and they have the responsibilitytodesign different fuzzy rules for eachpatient.

lcYatRatc * t

Bodv Tenipetatute_{F D}FLzzy-Based_{DainosisSystem} Waming Level

BodyHLtnmiditv P.

Fig.I10 Fuzzy-BasedDiagnosis System

10

|)

-Fig. I11WarningLevel nferenec

x Enin Y

I FuzzyRuilc|

Base

L

Fig. 12 Fuzzy Decision System I. Personal

Profile

The patient information and management system should have the ability to identify each patient inside building. Personalprofile records personal information which provides the system to identify different persons. According to fuzzy theory [7], we can adjust the size of fuzzy partition for differentpatients. Forexample, Fig. 13 and Fig. 14showtwo differentmembership functions fortwodifferentpatients. The medium heart rate for patient A ranges from 62 to 75, meanwhile, the heart rate from67 to 76 is the medium level forpatient B. Since therearethree inputvariables, there will betwenty-sevenfuzzy rulesinthefuzzy rule base.

AMedi;umiIt,\ ligh ALo\- Medii[riiHighl

60 62 67 70 7 ~ 6 7 6~

7 IIc.lrl1t.1l.6s %I~~~~~~~~~~~~~~~~~~~~~~~~~~IlearR8atc

Fig.13MembershipFunction of Fig.14MembershipFunction of

Patient A Patient B

J PriorityBased ControlManagement

The biomedical health-care center is a big system that therearemultiplecomponents andmanypeoplecoexistinthe samespace. In

general,

the following actionsshould be taken by thecenter:

* Emergency event:The alarmmessage isgenerated whena patient isdiagnosedinadangerous level.

* Regular control command: Sometimes the patients or doctorsadjust theroomdevicestothe desiredstate. * Record patient's information: The patient's information,

such as location and log time, will be recorded when theyregister.

* Plot ECG diagram: A doctor sometimes requests the system to plot a specific patient's ECG diagram for helping the doctor diagnose thepatient.

* Transmit requested data to another Agent Studio: The doctor whorequests systemtoplot ECG diagrammight

notbein the same zone asthepatient. Inthis case,the ECG data should be forwardedtoanotherAgent Studio.

The priority based control management which is a preemptive scheduling system schedules tasks according to their priorities. Accordingly, the control management will schedule all tasks accordingtotheprioritiesoftasks,asshown in Fig. 15. Furthermore, ifmultiple task requests are sent to the control management at the same time, the tasks will be re-scheduled according to the users' priority, as shown in Fig. 16.

lighPriority LieigericyEvent

RegularConrol

InformiatioiiRecbording/,

FC GD}iagramPlotlinM ILwPrioritv [)ataForwar-ding

Fig. 15 Priorities of Tasks

HiglbPriorityA

NL_rses_I

NoticedPatient LowPrigrity1 P ormalPatiesfts

Fig.16Priorities of Users K. Log Managementand Personal

Profile

Database

Themainjob of log management is toverify theID and password of the users. All patients' information will be recorded in the personal profile database including ID, password, priority, and so on. Consequently, the user can be identified and priority based control management can then receive the user's request message. After registration is successful, log management will record the user's log time and log location. The log location is the roomgateway's IP that thepatient canbe located and thepatient's vitalsigndata canbe forwardedtoanotherAgent Studio.

L. Hardware System-ECGAcquisitionModule

The electrocardiogram (ECG) is a graphic recording of the changes occurring in the electrical potentials between different locations on the skin (leads) as a result of cardiac activity. In practice,this flow ofelectricitycanalso be sensed by recording electrodes [16]. However,it isdifficulttorecord the ECG duetothe low frequency and low amplitude of the electricalactivity signal of heart. In general, the ECG signal frequency ranges from 0.05Hz to 1OOHz and the amplitude rangesfrom1 mV to 10 mV.Thereare twopredominanttypes of noises that contaminate the ECG signal acquired: the baseline wander(BW)noiseand electrodemotionartifact, and electromyogram-induced noise (EMG). The frequency components of BW noise are usually below 0.5Hz. EMG noise is dominant at higher frequencies, caused by increased muscle activity and by mechanical forces acting on the electrodes [8]. Therefore, the original ECG signal has to be amplified and filteredinordertobeaccurately processed.

III.EXPERIMENTSANDVERIFICATIONS

A. SystemImplementation Overview

Theimplementationoverview isshowninFig. 17, and the Agent Studio is implemented in a Win 2000 server with a database. The wearable computer is implemented in a handheld computer with embedded Linux OS. The filter-banks based QRS complex detection algorithm and Fuzzy

diagnosis system are implemented on the hanldheld computer. The vital signs acquisition moduleis implemented

in a circuitboard and connected to the handheld computerby RS232 serial communication. All programs of wearable computer are developed on Embedded Linux Development Server, and then cross-compiled to the StrongARM based handheld computer.

Since this paper is focused on the development of software architecture and wearable biomedical computer, a model house called NTU-House is used to simulate the realbuilding appliances and devices. The cell phone is used to send alarm messages to the person outside the building and the alarm messages are also sent to relevant persons by e-mail. Fig. 18 show the picture of the wearable biomedical system.

EmbeddeLmx ,i8.l -=j

DevelopmentSevereL___AgnStdo l' =ti

Celemelone g 1

Fig.17ImplementationOverview Fig.18WearableBiomedical System

B. Patient Status

Fig. 19 shows theuserinterface of thepatient status. The central block named Patient Information shows the patient's

current condition, including name, sex, age, currentlocation, bodytemperature,body humidity, heart rate, picture, and the result of preliminary diagnosis. Doctors can easily get the

patientcurrent condition from thisuserinterface. We can see

the current condition of the patient inthe picture is "Good"

and the light is green. Ifthepatient's condition is dangerous,

then the color of the light in Dangerous will be red. Nurses

thencanquickly findoutthepatientaccordingtothepatient's

current location. The bottom right block named ECG Chart shows thecurrent ECG of thepatient, and theECG datacan

be recorded by clicking the Record Data button for off-line analysis. The bottom left block named Log Information shows thelog information ofpatientsinthisroom.

IV.CONCLUSIONS

Comparedwith the traditional bedside patientmonitoring and management system, this wearable biomedical system providesmoreflexible andmorepowerful abilitiesto monitor

and manage the patients in modem hospitals and nursing

homes. In this system, the multi-agent software architecture,

filter-banks based QRS detection algorithm, multi-sensor fusiontechnology, and wearablecomputing are implemented. This system also proves that the multi-agent architecture is feasible in medical system and filter-banks based QRS detectionalgorithmis ahighly efficientand reliablealgorithm for heart beat detection.

Because of the multi-agent architecture, this system does not suffer from a single point failure associating with the centralized system. Each agent hasits decision system to act automatically. Each component is independentto each other, thus,it iseffortlesstoaddanewlyagent tothesystem.Agents of this system can cooperate with each otherto accomplisha commontask. Becauseof the wirelesscommunicationability, every patient cando otherthings whilewearing this wearable biomedical system. Each patient's location can be tracked easily. The embedded system has several sensors which perceive thebody status. Thisultimatelyincreases situational awareness.

REFERENCES

[1] V. X. Afonso, W. J. Tompkins, T.Q. Nguyen, Shen Luo, "ECG beat detection using filter banks," IEEE Transactions on Biomedical Engineering,Vol. 46, issue 2, pp. 192-202, Feb. 1999.

[2] V. X. Afonso, W. J. Tompkins, T.Q. Nguyen, Shen Luo, "Comparing

stress ECG enhancement algorithms," IEEEEngineering in Medicine andBiologyMagazine,Vol. 5, issue3,May-Jun. 1996.

[3] V. X. Afonso, 0. Wjeben, W. J. Tompkins, T.Q. Nguyen, Shen Luo, "Filter bank-based ECG beat classification," IEEE International

Conference onEngineering in Medicine andBiologysociety, Vol. 1, pp.30,Oct.-Nov. 1997.

[4] V. X. Afonso, W. J. Tompkins, T.Q. Nguyen, Shen Luo, "Multirate

processing of the ECG using filter banks," IEEE Computers in

Cardiology, pp.245-248, Sep.1996.

[5] M.I Ibrahimy, F. Ahmed, M.A.M. Ali, E. Zahedi, "Real-time signal processing for fetal heart rate monitoring," IEEE Transactions on BiomedicalEngineering,Vol. 50, issue 2, pp.258-261, Feb. 2003.

[6] B.-U.,Kohler, C., Hennig,R.,Orglmeister, "Theprinciplesof software QRSdetection,"IEEEEngineering inMedicine and BiologyMagazine, Vol.21,issue1,pp.42-57, Jan.-Feb.2002.

[7] Chin-Teng Lin, C.S. George Lee, Neural FuzzySystems, I'tEdition, Singapore:Prentice-Hall Pte Inc., pp.142-166,1996.

[8] GB Moody, WK Muldrow, and RG Mark, "A noise stress test for

arrhythmia detectors," ComputersinCardiology, pp.381-384,1984. [9] F.A Mora, GPassariello,G.Carrault,J.-P LePichon, "Intelligent patient

monitoringand management systems: areview,"IEEEEngineering in

Medicine andBiologyMagazine,Vol.12,issue4, pp.23-33,Dec.1993. [10] J. Pan and W. J.Tompkins,"Areal-time QRS detectionalgorithm,"IEEE

Transactions onBiomedicalEngineering,Vol. 3, pp.230-236, 1985.

[11] R.Poli,S.Cagnoni,and G.Valli,"Geneticdesignofoptimumlinear and nonlinearQRSdetectors,"IEEETrans.Biomed.Eng.,Vol.42,pp. 1137-1141,1995.

[12] G Strangand T. Nguyen, WaveletsandFilterBanks, Cambridge, MA:

Wellesley-Cam-bridge Press,1996.

[13] P.Varady,Z.Benyo B., Benyo, "An openarchitecturepatient monitoring

systemusing standard technologies,"IEEETransactionsonInformation TechnologyinBiomedicine, Vol.6,issue1,Mar.2002.

[14] G Vijaya, V. Kumar, and H.K. Verma, "ANN-based QRS-complex analysisofECG,"J.Med. Eng.Technol.,Vol.22,Issue4,pp. 160-167,

1998.

[15] R.Watrous, GTowell,"Apatient-adaptiveneural network ECGpatient monitoring algorithm,"IEEEComputersinCardiology, pp.229-232, Sep.

1995.

[16] J.G., Webster, MedicalInstrumentation: Application andDesign, 3rd