Mobile Device Assisted Remote Heart Monitoringand Tachycardia Prediction

Ibrahim Khalil(*), Fahim Su� (**)Faculty of Science, Engineering and Technology

RMIT University,Melbourne 3000, Australia

Email: (*)ibrahimk@cs.rmit.edu.au, (**) fahim.su�@student.rmit.edu.au

Abstract� Cardiovascular diseases not only kill hundreds ofthousands of people each year around the globe but also costbillions of dollars. It is economically and socially advantageous toreduce the burden of disease treatment by enhancing preventionand early detection. Existing mobile care systems [6], [8], [7], [4]constantly monitor sick and elderly patients and send alerts tohealthcare providers when they detect abnormalities in heartrates. These systems are reactive as they take action onlywhen abnormal heart condition, such as Tachycardia, has beendetected. However, abnormal heart condition may develop intoTachycardia over some time and there might be an increasingtrend in heart rates observed before it is diagnosed as full blownTachycardia. In fact, it has been found that increase in heart ratePrecedes episodes of Ventricular Tachycardia and VentricularFibrillation in some patients [9].

In this paper, we propose to use an advanced prediction modelto estimate the heart rates of selected patients in a mobile caresystem that would send alerts to a designated medical centrefor appropriate action to be taken when estimated rates exceeda prede�ned threshold. Our proposed system is preventive asit is capable of predicting heart rate abnormality, and possiblyTachycardia, well in advance.

I. INTRODUCTION

Due to demographical changes of the ageing populationthat can be attributed to the declining fertility and rising lifeexpectancy, the demand for the already overstretched publichealth services is expected to increase rapidly. The worldwidepopulation over 65 is projected to increase from 6.9% in 2000to 12.0% in 2030 [1]. Further, 13.1% of the population in Aus-tralia is over 65 [2]. Coupled with increasing life expectancy,the ageing population will lead to increased healthcare costas care for the elderly is much more expensive than that ofother aged groups. Among different healthcare expenditures,care for cardiovascular diseases in growing ageing populationimposes one of the greatest challenges for the public healthcareservices. An early detection of heart diseases is not onlyeconomically bene�cial but also socially advantageous as thiswould reduce the burden of disease treatment. However, thisrequires a shift from expert driven crisis-care model to a morepreventive type system which would monitor patients remotelyand take actions when vital signs in monitored patients becomeobvious.

Among heart patients a large number are known to haveTachycardia which is a form of cardiac arrhythmia and refersto a rapid beating of the heart. By convention the term refers

to heart rates greater than 100 beats per minute in the adultpatient. In extreme cases, tachycardia can be life threatening.Several mobile health care systems [6], [8], [7], [4] havebeen proposed for patients that have Tachycardia or a knowncardiovascular disease and need to be monitored around theclock. However, these systems can notify doctors/medicalcenters only when Tachycardia attains maturity in patients.

Recently, it has been found that increase in heart rateprecedes episodes of some deadly forms of Tachycardia incardiovascular patients [9]. There is a possibility that if weobserve a patient known to have symptoms of cardiovasculardisease for some period of time then there might be anincreasing trend in heart rates. We are encouraged by this fact,and therefore, propose a mobile health care system capableof predicting the future value (of heart rate) and comparingit against a prede�ned threshold value. When the predictedvalue goes beyond the prede�ned threshold (e.g. set between90-100) the system can trigger action by sending alert message(via SMS, internet) to a centralized server in a medical centerfor appropriate action to be taken. Typical action could bean appointment with a doctor for medical checkups. Thusthe system predicts abnormalities in advance and may preventTachycardia from attaining maturity.

II. BACKGROUND

Tachycardia refers to the very fast beating of heart. Medi-cally, heart rate over 140 creates concern for the patient [5].Persistent tachycardia may signal impending heart failure orcardiogenic shock. Tachycardia can even lower cardiac outputby reducing ventricular �lling time and the amount of bloodpumped by the ventricles during each contraction of the heart.Tachycardia is one of the four major signs of critical illness [1],[5], [2] . These four signs are Tachypnoea, Tachycardia, Hy-potension and altered conscious [5]. Detection of tachycardiaamong patients omens upcoming cardiovascular deterioration.One speci�c type of tachycardia called ventricular tachycardiacan be rapidly fatal [2] . Ventricular Fibrillation is anothergrave concern for the patient, since it can lead to cardiacarrest, when the blood �ow through the cardiovascular circuitterminates [2]. Atrial Tachycardia in long run results in theformation of blood clot (unless detected and treated early)within the heart, because of the lack of blood �ow. It ishighly unlikely that tachycardia develops overnight. Rather,

978-1-4244-2255-5/08/$25 ©2008 IEEE

Proceedings of the 5th International Conference on Information Technology and Application in Biomedicine, in conjunction withThe 2nd International Symposium & Summer School on Biomedical and Health EngineeringShenzhen, China, May 30-31, 2008

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0 500 1000 1500 2000 2500−4

−2

0

2A

mpl

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(m

V)

Number of Samples

Fig. 1. Tachycardia Affected Patient With a Heart Rate of 200

these tachycardias usually results from increased myocardialirritability [2]. Myocardial ischemia, MI, coronary artery dis-ease, valvular heart disease, heart failure, cardiomyopathy,electrolyte imbalance, and drug intoxication can lead to spe-ci�c tachycardias [2]. Therefore, patients treated or diagnosedfor any of these diseases can be monitored for pre-detectionof any possible development of tachycardia. If these deteri-orated physiological signs are identi�ed early, and patientsare treated accordingly, cardiopulmonary arrest may then beprevented [1]. it has been found [9], [10], [3] that increase inheart rate Precedes episodes of Ventricular Tachycardia andVentricular Fibrillation in some patients. This justi�es ourresearch in tachycardia prediction from increasing heart rate.Figure 1 depicts ECG of a tachycardia affected patient witha heart rate of 200. This patient data was downloaded fromMIT-BIH CU Ventricular Tachyarrhythmia Database (cudb,http://physionet.org/physiobank/database/cudb/) and plottedwith MatLab. It shows the �rst 10 seconds ECG of Recordcudb/cu28. This signal was digitized at 250 Hz with 12-bitresolution. For a severely affected patient like him, tachycardiawas developed over a period of time. With our system inplace, this patient could have been noti�ed even before histachycardia attained maturity.

III. ADVANCED ARRHYTHMIAS PREDICTION MODEL

As earlier explained in section II heart rate in certainpatients may slowly increase over a period of time in caseof Tachycardia, and may tend to decrease in Bradycardia. Themain goal of the model is to detect in advance if there is anincreasing or decreasing trend or not, and if there is any, itwould then notify the healthcare centre. Since the data mayhave trends we propose to use Holt's linear method of timeseries analysis to forecast a future value of heart rate fromthe available data set. This method, an extension of singleexponential smoothing, allows forecasting of data with trends.Holt's linear method can be expressed by following equations:

Lt = αYt + (1− α)(Lt−1 + bt−1) (1)bt = β(Lt − Lt−1) + (1− β)bt−1 (2)

Ft+m = Lt + btm (3)

In the above set of equations (1)-(3), Lt is an estimation ofthe level of the series (i.e. available heart monitoring data) attime t and bt denotes an estimate of the slope of the seriesat time t. Equation (1) adjusts Lt directly for the trend of

the previous period of heart monitoring, bt−1, by adding itto the last smoothed value of heart rate, Lt−1. This helps toeliminate the lag and brings Lt to the approximate level ofthe current data value of heart rate. By taking the differencebetween the last two smoothed values of heart rates, equation(2) updates the trend. This is further modi�ed by smoothingwith β the trend in the last period (Lt−Lt−1), and adding itto the the previous estimate of the trend multiplied by (1−β).Equation (3) is �nally formulated from the equations (1) and(2) to forecast ahead the value of heart rate.

Table I shows the application of Holt's linear smoothing toa heart rate series data with trend. For the sake of simplicity,smoothing parameters α = 0.5 and β = 0.5 were chosen toforecast future values of heart rate to predict the possibility ofTachycardia. The calculations can be illustrated by looking atthe forecast for period 22:

F23 = L22 + b22(1)

Here,

L22 = αY22 + (1− α)(L21 + b21)= 0.5× 77 + 0.5(77 + 1.1)= 38.5 + 39.05 = 77.55

and,

b22 = β(L22 − L21) + (1− β)b21

= 0.5(77.55− 77) + 0.5× 1.1= 0.275 + 0.55 = 0.825

Therefore,

F23 = L22 + b22(1)= 77.55 + 0.825= 78.375

Here, for F23 , m = 1. If the forecast to be made is 2 periodsahead (i.e. m = 2),

F24 = L22 + b22(2)= 77.55 + 1.65= 79.2

Similarly, F25 = 80.025 for m = 3, and F26 = 80.85 form = 4. The model can be used to predict both Tachycardia andbradycardia. The example in table I shows an increasing trendof heart rates which may possibly develop into tachycardia.

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TABLE IESTIMATION OF HEART RATES)

Period Observed Heart Smoothing of Heart Smoothing of Forecast ofof heart monitoring Rate Rate Data Trend Heart Rate

t Yt Lt bt Ft

1 75 75 1.0 �2 76 ... ... ...... ... ... ... ...... ... ... ... ...21 76 77 1.1 78.122 77 77.55 0.825 78.123 79 78.66 0.96875 78.375

Mobile Telephone Network

IP/

Central Server

Patient ProfilePast

Medical History

Alert Receiver AppointmentScheduler

Proprietary Comm Email

Messanger

SMS

ECG data

Alert, Summary HeartRate Data

Mobile Smart Phone

Fig. 2. Remote Heart Monitoring and Prediction Architecture

Fig. 3. Alive Hear Monitor Device

IV. SYSTEM ARCHITECTURE

The system architecture of a proposed prototype is shownin Figure 2. It mainly consists of: Wireless ECG data col-lection, Heart Rate Predictor using an advanced predictionmodel, communication module to send alerts to a medicalcenter, and a central server for receiving alerts and sendingappointments to patients via sms. It should be noted that thewhole procedure is completely automated and usually doesnot require the intervention of human operators. The majorfunctional components are brie�y described below:• ECG data collection: A Patient can be monitored on a

regular basis and ECG data sent to a mobile device usingAlive's Heart Monitoring device. Alive Heart Monitoras shown in Figure 3 is a wireless health monitoringsystem that can be used for remote real time monitoringusing wireless Bluetooth class 1 having 100m range. Itstransmitter can send data to mobile phone or PDA alsohaving bluetooth. Alive's portable monitoring device is alight-weight (60 g with battery) wearable sensing device.It features ef�cient power management that allows forcontinuous monitoring for up to one week. It has a singlechannel electrocardiogram (ECG) with 2 electrodes (300samples (8 bit) per second), a 2-axis accelerometer (75samples (8 bit) per second), an event button, a SecureDigital card for local storage. It is small enough to beworn without being noticed by people. For data collectionand storage typical smart phones supporting bluetooth canbe used. For our application we used DOPOD 838 proas shown in Figure 4

• Heart Rate Predictor : The working mechanism of theprediction model is very simple. After some data has beencollected and stored in the wireless mobile device it will

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Fig. 4. DOPOD 838 PRO Mobile PDA smart phone

Fig. 5. A patient Monitored Using Wireless Alive Heart Monitor

start to predict future values. If the estimated future valueis more than the a prede�ned threshold, the mobile devicewould send message (i.e. via SMS, MMS, internet etc.)to a designated medical centre for appropriate action tobe taken. For the purpose of prediction future values ofheart rates we use Holt's linear method as explained insection III.

• Communication Module: This module in a mobile deviceconsists of a data extractor and a message sender. Thedata extractor uses bluetooth protocol to receive ECG datafrom Alive Heart monitor. It is also capable of extractingheart rate data over a period of time to calculate the meanheart rate. The computed mean heart rate data is passedto the prediction model for a decision to be made. If thedecision is to send an alert message then the messagesender sends a message along with mean heart rate datato central server located in medical center.

• Central Server: The central server in a medical cen-ter continuously receive messages from patient mobiledevices. Although it can receive complete ECG data,the amount of data would be tremendous and trans-fer could be expensive. For the current application itsimply receives alert message and stored mean heart

rate data. The central server also has client pro�le andmedical history databases. Once the server receives analert message via SMS or TCP/IP socket it automaticallyassigns a specialist doctor to the patient (assisted byan appointment scheduler) and sends appointment viaSMS, E-mail or TCP/IP socket to the patient. The alertmessages can be prioritized by the appointment schedulerbased on the severity of heart condition.

V. CONCLUSION

In this paper we have proposed a remote predictive heartmonitoring system that can estimate future heart rate from re-cent and past mean heart rate data. As in some cases, increasein heart rate precedes episodes of Ventricular Tachycardia andVentricular Fibrillation in Patients, it is quite possible that ifwe observe a patient for some period of time then there mightbe an increasing trend in heart rates. By predicting the futurevalue and comparing it against a threshold value, the systemcan trigger action by sending alert SMS to a centralized serverin a medical center if the predicted value goes beyond theprede�ned threshold. Thus, unlike most the existing systemsthat only try to detect various types of Tachycardia, our systemcan take preventive action even before Tachycardia developsinto a patient.

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