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WE-CARE: An Intelligent Mobile TelecardiologySystem to Enable mHealth Applications

Anpeng Huang, Member, IEEE, Chao Chen, Student Member, IEEE, Kaigui Bian, Member, IEEE, Xiaohui Duan,Min Chen, Hongqiao Gao, Chao Meng, Qian Zheng, Yingrui Zhang, Bingli Jiao, Member, IEEE,

and Linzhen Xie, Member, IEEE

AbstractRecently, cardiovascular disease (CVD) has becomeone of the leading death causes worldwide, and it contributes to41% of all deaths each year in China. This disease incurs a costof more than 400 billion US dollars in China on the healthcareexpenditures and lost productivity during the past ten years. It hasbeen shown that the CVD can be effectively prevented by an inter-disciplinary approach that leverages the technology developmentin both IT and electrocardiogram (ECG) fields. In this paper, wepresent WE-CARE, an intelligent telecardiology system using mo-bile 7-lead ECG devices. Because of its improved mobility resultfrom wearable and mobile ECG devices, the WE-CARE systemhas a wider variety of applications than existing resting ECG sys-tems that reside in hospitals. Meanwhile, it meets the requirementof dynamic ECG systems for mobile users in terms of the detectionaccuracy and latency. We carried out clinical trials by deployingthe WE-CARE systems at Peking University Hospital. The clinicalresults clearly showed that our solution achieves a high detectionrate of over 95% against common types of anomalies in ECG, whileit only incurs a small detection latency around one second, both ofwhich meet the criteria of real-time medical diagnosis. As demon-strated by the clinical results, the WE-CARE system is a usefuland efficient mHealth (mobile health) tool for the cardiovasculardisease diagnosis and treatment in medical platforms.

Manuscript received December 19, 2012; revised May 27, 2013; August4, 2013; accepted August 13, 2013. Date of publication; date of current ver-sion. This work was supported in part by the National Science and Technol-ogy Major Projects in Wireless Mobile Healthcare Projects under Contracts2012ZX03005013 and Contract 2013ZX03005008, and in part by the OkawaFoundation. This paper was presented in part at the IEEE ICC 2013 conference.

A. Huang is with the Mobile Health Lab, PKU-UCLA Joint Research In-stitute, the State Key Lab of Advanced Optical Communication Systems andNetworks, and also with the Center for Wireless Communication and Signal Pro-cessing, Peking University, Beijing 100871, China (e-mail: hapku@pku.edu.cn).

C. Chen, M. Chen, H. Gao, C. Meng, Q. Zheng, and Y. Zhang arewith the Mobile Health Lab, Peking University, Beijing 100871, China (e-mail: chao.chen@pku.edu.cn; chenmin7571@pku.edu.cn; hongqiaogao@pku.edu.cn; pkumengchao@gmail.com; qianzheng@pku.edu.cn; yingrui_zhang@pku.edu.cn).

K. Bian is with the Institute of Network Computing and Information System,Peking University, Beijing 100871, China (e-mail: bkg@pku.edu.cn).

X. Duan is with the Mobile Health Lab and also with the Center for Wire-less Communication and Signal Processing, Peking University, Beijing 100871,China (e-mail: duan@pku.edu.cn).

B. Jiao is with the Mobile Health Lab and also with the Center for Wire-less Communication and Signal Processing, Peking University, Beijing 100871,China (e-mail: jiaobl@pku.edu.cn).

L. Xie is with the Mobile Health Lab, and also with the State Key Lab ofAdvanced Optical Communication Systems and Networks, Peking University,Beijing 100871, China (e-mail: tydxlz@pku.eu.cn).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JBHI.2013.2279136

Index TermsClinical trial, cardiovascular disease (CVD),health risk monitoring, mobile health (mhealth), wearable efficienttelecardiology system(WE-CARE).

I. INTRODUCTION

THE cardiovascular disease (CVD) has become the leadingcause of human deaths, counting up to 29% of the totalglobal deaths, based on the WHOs The World Health Report2008 [1]. The main symptoms of cardiovascular disease includeserious myocardial ischemia (acute myocardial infarction), heartfailure, malignant arrhythmia, etc. As shown in [2], most ofthese symptoms can be foreknown by observing certain specificmanifestations of electrocardiogram (ECG) signals. The ECGmonitoring system has been used to detect such manifestations,and early detection can save valuable time for taking precau-tions against the cardiovascular disease. Thus, the prevention ofcardiovascular disease using mobile ECG monitoring systems isof paramount significance, which has garnered great attentionsfrom the research community.

The implementation of an efficient cardiovascular diseaseprevention system requires tremendous medical resources. Earlyalarm and medical instructions can be provided upon the detec-tion of early signs of the disease or disease progression [3]. Thedisease progression can be avoided by improving lifestyle, andmonitoring physiology parameters of out-hospital-patients [4].However, it is difficult to implement a long-term monitor foreach outpatient or home user due to limited medical resources.

Recent advances in wireless mobile networking technologieshave provided an opportunity to alleviate this problem; thisconcept is known as mobile health (mHealth) [5][8], which ischanging the way of health-care delivery today and hence, is atthe core of responsive health systems [9].

In this paper, we present WE-CARE, a Wearable EfficientteleCARdiology systEm, that can provide 24/7 health monitor-ing service with the help of wearable and mobile 7-lead ECGdevice.1 The use of five ECG electrodes helps collecting suffi-cient 7-lead ECG data to guarantee the detection accuracy with-out impairing the mobility of the system. More importantly,WE-CARE employs a two-step approach that distributes thedetection task to both the mobile device and the server suchthat the diagnosis capability of ECG devices can be exploited,

1The WE-CARE system has passed the test of Pharmaceutical Industry Stan-dards of China: Electrocardiographic Monitors, YY 1079-2008, GB9706.1-2007, and GB9706.25-2005.

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thereby reducing the length of the feedback cycle. Specifically,automatic ECG analysis algorithms are introduced to detectanomalies in ECG data, which can help significantly reduce thetime physicians spend in checking users ECG by 75% accord-ing to our clinical results.

For better understanding, the significance of WE-CARE sys-tem in this study, let us look at existing systems first. So far, theexisting 1-lead or 3-lead wireless ECG systems are for homecare users, in which the collected data are only for reference,and lack necessary clinical values [8]. In hospitals, 12-lead or18-lead systems are typically used but lack user mobility [10].It is desired to design a system that combines user mobilityand intelligent clinical function with heath-risk alert [11]. Mo-tivated by this trend, the WE-CARE system is developed for7-lead ECG real-time monitoring service over mobile networks(please note, a wireless network may be not mobile, but a mobilenetwork must be wireless).

The rest of the paper is organized as follows. Section II brieflydescribes the technical background. Section III introduces thesystem architecture and the design of the ECG device. Sec-tion IV describes the ECG detection mechanism. The perfor-mance evaluation of the system is demonstrated in Section V,and we conclude the paper in Section VI.

II. PRELIMINARIES AND RELATED WORK

A. Principles for Devising a Wireless ECG SystemThe wireless ECG system can significantly save the medi-

cal resources by remotely monitoring the cardiac status fromECG. However, there are three requirements for devising sucha system.

1) Support of mobile and wireless ECG device: Remote ECGmonitoring is of vital importance to out-of-hospital pa-tients who are exposed to a high rate of recurrence, and itrequires the support of mobile and wireless ECG devices.

2) Sufficient ECG data collection: Different cardiovasculardiseases may cause anomalies on different leads of ECG[12], and thus a wireless ECG system has to collect theECG data as complete as possible to guarantee the accuratedetection and diagnosis of cardiovascular diseases.

3) A small cycle of updating ECG data: The early warningmechanism in wireless ECG systems requires the real-time analysis of ECG signals. A small cycle of updatingthe collected ECG data to the data center will guarantee thereal-time alerts if the early sign of cardiovascular diseaseappears. As a result, the efficacy of a wireless ECG systemdepends on the cycle length that the device updates theECG data.

The wireless ECG monitoring system with a large numberof leads [13] are only designed for clinical usage (e.g., the 12leads system), which restricts the mobility of users that arelocated outside the hospital. For enabling the out-of-hospitalECG monitoring, many existing wireless ECG systems havemobile ECG devices with only one or three leads [14][17].However, the reduced number of leads limits the amount of ECGdata that can be collected in unit time, which further degradesthe performance of the real-time diagnosis and causes delay

Fig. 1. System Architecture. At the sensing layer, WE-CARE device collectsthe raw physiological parameter (ECG), and completes the task of QRS complexdetection. At the network layer, the ECG data collected and alerts generated atthe sensing layer are transmitted to the data center. At the application layer,WE-CARE server completes the computing-intensive task (T wave detection),and generates alerts if necessary; physicians get access to the alert and ECGdata to perform further in-depth diagnosis.

to the early warning/treatment against cardiovascular diseases.Moreover, the cycle of updating ECG data in existing dynamicECG systems used in hospitals are typically more than 24 h [18],which is too long for providing the real-time ECG alerts.

Therefore, no existing wireless ECG systems (either thosefor home use, clinical use, or those with Holter) can fully fulfillthe above design requirements. In this paper, we built 7-leadwearable and mobile ECG devices into the telecardiology sys-tem that leverages the tradeoff between the mobility supportand the sufficient collection of ECG data. Meanwhile, our builtsystem can meet the design requirements for the feedback cycleand response delay.

III. OVERVIEW OF WE-CAREIn this section, we present an overview of the WE-CARE sys-

tem, which provides a 24/7 ECG monitoring service for patientswith cardiovascular diseases or people that may have potentialcardiovascular problems.

A. Architecture of WE-CAREAs illustrated in Fig. 1, the system consists of three compo-

nents, namely, the mobile 7-lead ECG device, the ECG datacenter, and the relay device.

The ECG device completes four ECG monitoring tasks: thecollection, processing, display, and transmission of ECG data.The QRS and T-wave detection algorithms are implementedat the data processing step to detect the heart rate and certainabnormal phenomena of the ECG. Meanwhile, it transmits col-lected data to the data center for more complex diagnosis suchas data mining [19]. Note that the collected ECG data will bestored locally in the TF card of the device, and then transmittedto the data center via mobile networks (e.g., WCDMA or LTE-

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Advanced networks). As shown in Fig. 1, the five electrodes(RA, LA, LL, C, and a reference electrode) in WE-CARE sys-tem collect 7-lead ECG information, namely, I, II, III, aVR,aVL, aVF, and V1; and our detection algorithms are based onthe lead II. The clinicians are free to choose any other leads forexecuting detection algorithms when necessary.

The data center works as a diagnosis platform for the doctors.When the ECG data are uploaded, the agent program providesdoctors a real-time display of the ECG via the web server. Fromthe ECG database of the data center, the doctor is able to acquirea history of an individual users heart healthiness status.

Next, we introduce the ECG device in more details.

B. Operating ModesWe have implemented three operating modes on the ECG

device.1) In the conventional power-saving mode, the ECG device

continually collects the users ECG data, and stores itlocally on the device, without any automatic uploadingto the data center. The collected data will be delivered orcopied to the server of hospitals manually, and this modeis adopted by most existing dynamic ECG systems.

2) In the real-time mode, the ECG device continually collectsthe users ECG data, and then forward all of the real-timecollected data to the data center over mobile networks.The doctors are able to check the real-time or historicalECG data of a user via the web interface.

3) In the efficient monitoring mode, the ECG device continu-ally collects the users ECG data, and only transmits partsof the collected data to the data centeri.e., the 60-s-longECG per hour. Meanwhile, the ECG device performs alocal real-time diagnosis over all the collected data. If thelocal diagnosis mechanism identifies a potential risk, orif a manual alert is triggered by the user, the device willincrease the sample rate from 250 to 500 Hz for the 60-slong ECG collected, and then send it to the data center.The 60-s ECG data are obtained from 30 s before to 30 safter the anomaly/manual alert point. As long as the datacenter receives an alert, the doctor will be able to see thealert at the earliest convenience, and take actions for morein-depth diagnosis or even early treatment. Note that de-vice exceptions such as lead-off and connection-failurewill also generate an alert.

C. ECG Detection ProcessThe diagnosis of cardiovascular diseases depends on the ob-

servation of ECG owing to its convenience, reliability, and non-invasiveness. Many factors are useful to reflect the cardiac ac-tivity and help the observation, such as the P, QRS and T waves,ST segment, RR interval, and other parameters. The ECG detec-tion process includes a denoising phase and two ECG detectionphases.

1) In general, denoising is a necessary step before processingand analyzing the collected data to remove the noise in thedataset.

Fig. 2. PCB of the ECG device.

2) The QRS complex detection algorithm is implemented atthe device side in order to locate the R wave and detectthe R wave anomalies. Only the ECG data regarding Rwave anomalies will be uploaded to the data center in theefficient-monitoring mode.

3) On the server side, using the obtained locations of R waves,a T wave detection algorithm is implemented to furtherlocate the ST segment and detect the ST anomalies.

D. ECG Device1) Hardware System: The hardware modules of the mobile

ECG monitoring device are built on a printed circuit board(PCB), as shown in Fig. 2. The core of the hardware systemis an ARM microprocessor STM 32, which is used as the mi-cro controller unit (MCU) of the ECG device. It has abundantperipheral resources to meet the requirements of ECG moni-toring. The MCU controls various hardware modules/interfacesto complete the four ECG monitoring tasks. For example, theECG data collection of ECG is implemented by the ECG dataADS module via SPI bus. Note that the ECG lead wire is thehardware interface for input while the mobile module is thehardware interface for output.

The device measures 100 mm 50 mm 15 mm, weighsabout 200 g with a 1500 mAh Li-ion battery. Our clinical resultsshowed that the battery life of our device for one full chargingcycle is 6 h in the real-time mode, 72 h in power saving mode,and 48 h in monitoring mode, respectively. More information ofthe hardware can be found in [20].

2) Software System: The software system of the device is de-veloped on the transplanted C/OS-II system. The task managerhas the highest priority and it manages all the four ECG monitor-ing tasks. The collected ECG data has to be delivered to severaloutput modules, such as the WCDMA/LTE-Advanced transmis-sion module, the TF card slot, the LCD interface, etc. The datatransmission between on-device modules is implemented by theinterprocess communication mechanism of message queues.

E. Clinical Data Transmission MechanismThe clinical use of WE-CARE has posed constraints on the la-

tency and the error rate of clinical data transmission. To achieve

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Fig. 3. Frame structure.

these goals, we optimize the data transmission mechanism inthe following ways.

1) Data Compression and Data Frame Assembling at Trans-mitter: Before data transmission, we apply the Huffman encod-ing [21] and Run Length encoding (RLE) [22] methods to com-press the ECG data collected by the device. Since these appliedencoding methods are lossless, this data compression processwill not affect the accuracy of the ECG data. The compressedECG data will be transmitted in data frames, and each framecontains a 19-Byte-long frame header that specifies the uniqueuser ID, the serial number of the frame, CRC information, andother control information. The frame structure is illustrated inFig. 3.

2) Data Frame Reassembling at Receiver: To recover theECG data, the receiver (i.e., the server) has to collect all of ECGdata frames that are error-free, and put them in order according tothe serial numbers. However, data frames may be lost during thetransmission process using mobile module over wireless links;moreover, data frames may arrive at the receiver out-of-order.Hence, WE-CARE employs the reception window technique toaddress these problems.

Reception window: The server maintains a reception windowfor each device that is uploading ECG data by allocating atemporary reception cache of 512 KB for that device. The cacheis initialized as a 1-D array with array indexes corresponding tothe serial number of the data frames to be received. Accordingto our experimental statistics, a 512 KB cache is generally ableto cache the ECG data for half an hour.

Upon each data frame arrival, the frame header is parsedand the CRC checksum is verified. The frame that passes theverification is then recorded into the reception cache (the array)based on its ID and serial number. The server will periodicallysend out a retransmission request to the device regarding themissing frames or frames that fail to pass the verification untilthe number of retransmission requests reaches a limit. Generally,in our system, the maximally allowed number of retransmissionrequests is set to 5 for the ECG data without alert, and it is setto 10 for the ECG data with alert.

The server decides to terminate the reception process in thecurrent reception window when the cache is fulfilled withoutmissing frames or when the number of retransmission requestsreaches its limit. Once the server terminates the current receptionprocess, it decompresses data frames in the cache, records thedecompressed into an ECG record file, inserts the file as a newrecord entry into the database, and then flushes the receptionwindow.

Owing to the data compression and the reception windowtechniques employed, the transmission latency and the rate offrame loss can be significantly reduced.

Fig. 4. Error Rate of Transmission in We-Care. RT: Retransmission, C:Compression.

3) Transmission Efficiency: In order to evaluate the trans-mission efficiency of WE-CARE, we chose the transmissionerror rate and the data compression ratio as the evaluation mea-sures.

We calculated the number of data frames processed on eachdevice, and the number of data frames decompressed on theserver, and then derived the transmission error rate for device das follows:

d =Fl,d + Fe,d

Fd

where Fd denotes the number of frames processed at device d;Fl,d and Fe,d denote the numbers of lost and error frames corre-sponding to the specific device d at the server side, respectively.

The experimental results of transmission error rate are givenin Fig. 4, which clearly show that the transmission error ratesare significantly reduced by our transmission mechanism.

The data compression ratio at the device side is defined asCR = BrBc , where Br is the number of bytes of the raw data andBc is the number of bytes of the compressed data. According toour experimental results, the average compression ratio is 5.98.

IV. ECG DETECTION MECHANISM OF WE-CAREIn this section, we describe the ECG detection mechanism

of WE-CARE, which includes a denoising scheme, two ECGdetection algorithms, and the anomaly detection strategies.

A. Denoising of the ECG SignalDue to the presence of noise, the collected ECG data may

not be ready for display or readable for diagnosis, and thusthe preprocessing of the raw data is necessary. The ECG sig-nal detected by body surface electrodes contains seven differenttypes of interference, including power-line interference, base-line drift, electrode contact noise, electrode polarization noise,electromyogram signals, internal noise of amplifiers, and move-ment interference.

Among these sources of interference, the power-line interfer-ence near 50 Hz and its harmonics and the baseline drift below0.7 Hz are the two most contributing ones, which significantly

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Fig. 5. Performance of the denoising filter.

degrade the detection accuracy of the QRS complex. In the phaseof preprocessing of ECG signals, we focused on the removal ofthese two types of noises. Inspired by the filtering method pro-posed in [23], we combined a 50 Hz Notching Comb filter anda 0.7 Hz FIR high-pass filter as our digital filter to eliminate thetwo types of interference mentioned previously.

We applied our filtering method to an ECG dataset obtainedfrom the MIT-BH database, and performance of the filter isshown in Fig. 5. The blue curve (i.e., the bottom line) representsthe originally collected signal coupled with the baseline driftand other minor interference. The red curve (i.e., the upper line)represents the output of the filter. By comparing the originalsignal with the output, we can observe that the baseline driftof the ECG signal are completely eliminated. Meanwhile, thenoise in the horizontal segment is significantly reduced, whichimplies that the power-line interference is also removed.

B. QRS Complex Detection AlgorithmTo determine the start and end points of the QRS waves, it

is necessary to obtain the accurate location of R wave. Manyalgorithms have been used to locate the feature points of Rwaves, including the slope method, the amplitude method, thearea method, etc. [24].

The QRS complex detection algorithm in the WE-CAREsystem is based on the difference threshold arithmetic, whichcombines multiple existing methods. To meet the requirementof real-time ECG monitoring, a dynamic threshold adjustmentmechanism was implemented in the algorithm. Algorithm 1shows the pseudocode, and we briefly explain the procedurenext.

1) First, the thresholds of slope and amplitude are initializedas 60% of the highest values in the first-second ECG datastream respectively.

2) Then, we choose a nonoverlapping time window (usuallyset to three seconds in the WE-CARE system) that slidesalong the stream.

3) For every collected data sample in the time window, theECG device has to complete the following two tasks.

a) Location of R waves: The ECG device concludesthat an R wave is detected, if the slope betweenthe ith and (i + 2)th points and the amplitude ofthe ith point is equal or greater than their thresholds,respectively. Then, we search forward from the pointuntil the first extreme point is detected and the Rpeak is identified. We use the time stamp whenthe data point is collected as the location of the Rwave.

b) Calculation of heart beat: According to two adjacentR waves locations, the real-time heart rate can becalculated and written back into the frame header ofthe ECG data.

4) When the location of R wave is determined, we searchforward and backward for the first negative extreme pointthat could help locate the Q and S peaks, respectively.

5) The threshold values will be adjusted dynamically. If thehighest value increases, the threshold will be updated to60% of the maximum. If all points amplitude or slopevalues in current time window are less than the currentthreshold, the threshold value will be reduced by 20% un-til they reach the critical value. Both of the adjustmentpercentages aforementioned are obtained from our exper-iments.

Anomaly detection regarding R wave detection: Based on theresults of R wave detection, the WE-CARE device is able tocomplete the simple anomaly detection tasks, such as detectionagainst RR interval anomaly, R wave amplitude anomaly, heartbeat anomaly, etc. For example, the heart beat value can be

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obtained by calculating the interval between two R waves, whichis a useful tool to observe the symptom of ventricular fibrillation.

C. T Wave Detection AlgorithmThe ECG device transmits the sampled ECG data stream that

piggybacks the location information of QRS complex to the datacenter. The T wave detection is a computing-intensive task andrunning at the server side to further analyze the uploaded ECGdata.

To locate the T wave, we need to figure out the location of theJ point first, which is the point where the QRS complex joinsthe ST segment. We define d as the first-order difference of theECG signal, and the first zero-point after R wave is the S peak.Then we search forward along the ECG data from the S peakfor the first peak point p and get its amplitude d[p]. Based onthe value, we define a threshold,

= d[p] k,

where k = 13 is an empirical constant calibrated by experiments.The first point that is equivalent to the threshold is the J point.

Let LJ denote the obtained location of the J point, we define awindow (bw , ew ) as a function of the RR interval (RRI) value.

(bw , ew ) = (LJ , LJ + t RRI),

where t (0, 1) and it is adjusted according to the waveform.In our proposed system, the detection of T wave is based on

the characteristics of wavelet transform coefficient modules inthe window. To ensure the detection rate of T wave, we have tocarefully select the characteristic scale.

Since the energy of the QRS complex is higher than that ofthe T wave, the QRS complex affects the recognition of T wave.To address this problem, we lower the QRS complex to thebase line such that the T wave can be highlighted. Then, we de-compose the ECG signal with wavelet functions of Daubechies(db) series. Via the five-layer-decomposition with Daubechies 4(db4) wavelet, the T peak can be detected on the fifth scale andthe noise can be obviously restrained. Note that the db4 waveletis (2)(t) symmetric wavelet, and the T wave peak correspondsto the extreme point of wavelet transform. The T wave detectionalgorithm (see Algorithm 2) traverses all the extreme points inthe window including the false extreme points, and the extremepoint with the maximum amplitude of its corresponding originalsignal is recognized as the T peak.

Moreover, inspired by Mallats theory [25], our T-wave algo-rithm is also implemented on board. The process of wavelet de-composition is achieved via a group of orthogonal digital filters,which are employed to decrease the computational complexityof the wavelet transform.

Anomaly detection regarding T wave detection: The resultsof T wave detection obtained by the WE-CARE server are use-ful for detecting complex anomalies. For example, if the QT-interval (between Q and T peaks) is too wide, the WE-CAREsystem will generate an alert regarding the myocardial ischemiaand myocardial damage. Similarly, the symptom of T-wave in-version is typically a sign of chronic myocardial ischemia, left

ventricular hypertrophy, or an indication of acute period of my-ocardial infarction.

A more detailed description of our T-wave detection algo-rithm is illustrated in [26].

V. PERFORMANCE EVALUATION

In order to evaluate the performance and efficiency of theWE-CARE system, we chose a total number of 84 users atPeking University Hospital as experimental subjects, and thediagnosis was based on ECG data acquired by other hospitalfacilities (e.g., desktop ECG units).

A. Data CollectionUsers participating in the experiment were required to wear

the ECG acquisition device for 24 h a day, and they were dividedinto two groups. One group called the group of normal subjects(NS) included users without cardiovascular disease detected.The second group represented the set of users with cardiovascu-lar disease (CVD). All the subjects were free to stay in or leavethe hospital. Generally, the WE-CARE system is designed for24/7 daily health risk monitoring. In applications, the clinicianswould help users apply the electrodes and show them how touse the system. A manual of instructions was also provided topatients. To further evaluate the performance of our system, wealso carried out experiments based on the ECG data obtainedfrom the European ST-T Database (ESD).

When no anomaly was detected, 60-s long, 250 Hz samplingrate, 7-lead ECG data were uploaded to server periodically.When an anomaly was detected, the device uploads the datathat is obtained from 30-s before to 30-s after the anomalypoint, which is 60-s long, 7-lead ECG data with a sampling rateof 500 Hz. In order to avoid the potential error caused by packetloss during wireless communication, we backed up the last 24 hdata on the local device.

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Fig. 6. Evaluation of R Wave and T Wave Detection. FNR represents the falsenegative rate, and FPR represents the false positive rate.

B. Error Rates

To validate the performance of R wave detection algorithmin our system, we compared all 48 ECG cases in MIT-BIHArrhythmia Database [27] whose QRS complex locations wereincluded with our detection results. As the MIT-BIH databasegave the beat counts for the first 5 min of each record and theremainder of the record, we used the former part for validation.The result is shown in Table I.

For the 18348 QRS complexes above, the QRS complex de-tection ratio is 99.3%, which shows an outstanding performance.We also validated our algorithms on ECG data in European ST-T database [28] for R and ST detection performance, part of theresults are shown in Fig. 7, Tables II and III (Table III can bealso found in [26]).

As shown in the result, the T wave detection ratio of ouralgorithm is 97.5%, which is an improvement while current T-wave detecting ratio is no more than 95% [29]. Note that whenthe ST segment is with unnegligible noises (e0405.dat fromESD), the detection algorithm of WE-CARE is able to maintaina detection rate over 95%.

In our experiments, the collected data by the ECG devicewere also compared with physicians observation. As for datafrom European ST-T Database, the results were compared withthe note files. These two types of data are of a length of 300 s.We applied a similar approach to the performance evaluationof T wave detection. We measured the error rate and missingrate of T wave detection using the first 5 min of the record. Theresults are shown in Fig. 6, and we observed that a detection rateof 99.4% for R wave detection and that of 97.7% for T wavedetection.

C. Categorical Anomaly DetectionIn this experiment, we mainly focused on five common cat-

egories of anomalies, as listed in Fig. 8. We compared thedetected proportion of anomalies by our method with the manualtest statistics.

Fig. 7. Results of R and ST detection performance for data in European ST-Tdatabase. (a) No. e0103 record, (b) No. e0166 record, (c) No. e0405 record,(d) No. e0607 record.

As shown in Fig. 8, our system yields a high anomaly detec-tion rate in every category. This implies that physicians can sim-ply focus on those ECG data samples that receive an anomalydetection alert in most categories. As a result, it will save atleast 75% of time spent in anomaly judgment and localizationcompared with manual check, which greatly improves the ef-ficiency of the cardiology diagnosis system. However, specificECG anomalies such as ST segment elevation and depressionare difficult for wireless ECG system to automatically recognize,which still require humans efforts to identify.

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TABLE IQRS DETECTION PERFORMANCE FOR ECG DATA IN MIT-BIH ARRHYTHMIA DATABASE

TABLE IIQRS DETECTION PERFORMANCE FOR ECG DATA IN EUROPEAN ST-T DATABASE

TABLE IIIT WAVE DETECTION PERFORMANCE FOR ECG DATA IN EUROPEAN ST-T DATABASE

D. Response DelayWe synchronized the clocks of the data center server and the

device. Then, we calculated the response delay as the differencebetween the time point when the ECG dataare collected by thedevice and the time point when the server makes a decision(e.g., generates an alert). Table IV shows the response delay

of four types of anomalies: heart rate anomaly (HR), lead off(LO), data center connection failure (DCF), and manual alert(MA). To evaluate the response delay under high-concurrencyenvironment, we also simulated a scenario where 1000 devicesuploaded ECG data at the same time, and used a real device tovalidate the system performance. No increase of delay or othersystem performance degradations were observed.

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Fig. 8. Performance of detection algorithms in five categories of anomalies.(1) Resting RR interval normal range is between 0.6 (100 bpm) and 1 s(60 bpm), (2) T wave inversion is a feature of myocardial infarction and angina,(3) Abnormally prolonged or shortened QT interval means a risk of developingventricular arrhythmias, (4) Normal QRS complex is 80 to 120 ms in duration,(5) A high R wave amplitude may indicate an unusually large heart musclemass, an unbalanced size of a ventricle, or a large heart in relation to the chestsize or closeness to the chest wall.

TABLE IVEVALUATION OF AVERAGE RESPONSE DELAY FOR ANOMALY DETECTION

(UNIT: SECOND)

TABLE VCOMPARISON OF EXISTING REMOTE ECG SYSTEMS WITH WE-CARE

In addition, we also made a comprehensive comparisonamong the WE-CARE system and a number of other remoteECG systems, and the results are shown in Table V. In general,there is no any real clinical meaning when the number of leadsis less than seven. On the other hand, user mobility is limited ifthe number of ECG electrodes is greater than five (which crossover top-down body). Since the WE-CARE is devised for 24/7daily public healthcare monitoring, collected data of which isonly used for medical assistant in clinical diagnosis, the 7-leadsolution is the tradeoff choice for considering a combination ofadequate clinical information collection and user mobility re-quirement. To our best knowledge, this is the first 7-lead mobileECG system which passed medical standard tests and got thenational medical equipment production license.

VI. CONCLUSIONIn this paper, we present WE-CARE, an intelligent telecar-

diology system over mobile wireless networks. The ECG de-tection mechanism of WE-CARE includes two algorithms thatguarantee a high detection rate for anomaliesa rate of 99.3%for the QRS complex detection, and a rate of 97.7% for Twave detectionaccording to the clinical trial results. In theefficient-monitoring mode, the WE-CARE system saves themedical resources in terms of communication bandwidth andthe time of physicians. Moreover, the WE-CARE system meetsthe clinical requirements and can be applied to both inpatientsand outpatients, especially for the cardiovascular disease-pronepopulation. This study demonstrated that mHealth concept canbe turned into real applications with promising future. Our fur-ther research will focus on the detection against certain hard-to-recognize anomalies such as the ST segment elevation anddepression.

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[12] G. S. Wagner and H. J. L. Marriott, Marriotts Practical Electrocardiog-raphy. Baltimore, MD, USA: Williams & Wilkins, 2001.

[13] S. Barro, J. Presedo, P. Flix, D. Castro, and J. A. Vila, New trends inpatient monitoring, Disease Manag. Health Outcomes, vol. 10, no. 5 pp.291306, May 2002.

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[16] S. H. Liou, Y. H. Wu, Y. S. Syu, Y. L. Gong, H. C. Chen, and S.T. Pan, Real-time remote ECG signal monitor and emergency warn-ing/positioning system on cellular phone, in Intelligent Informationand Database Systems. New York, NY, USA: Springer-Verlag, 2012,pp. 336345.

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[26] Q. Zheng, C. Chen, Z. Li, A. Huang, B. Jiao, X. Duan, and L. Xie, Anovel multi-resolution SVM (MR-SVM) algorithm to detect ECG signalanomaly in WE-CARE project, in Proc. Biosignals Biorobot. Conf., Int.Conf. Intell. Sens., Sens. Netw. Inf. Process., Rio de Janerio, Brazil, Feb.2013, pp. 16.

[27] G. B. Moody and R. G. Mark, The impact of the MIT-BIH arrhythmiadatabase, IEEE Eng. Med. Biol. Mag., vol. 20, no. 3, pp. 4550, May/Jun.2001.

[28] A. Taddei, G. Distante, M. Emdin, P. Pisani, G. B. Moody, C. Zeelenberg,and C. Marchesi, The European ST-T database: Standard for evaluatingsystems for the analysis of ST-T changes in ambulatory electrocardiogra-phy, Eur. Heart J., vol. 13, no. 9, pp. 11641172, Sep. 1992.

[29] S. C. Bulusu, M. Faezipou, V. Ng, M. Nourani, L. S. Tamil, and S. Baner-jee, Transient ST-segment episode detection for ECG beat classification,in Proc. IEEE/NIH Life Sci. Syst. Appl. Workshop, Maryland, DC, USA,Apr. 2011, pp. 121124.

[30] MegaKoto Ltd., KOTO eHealthMonitor. [Online]. Available: http://www.megakoto.fi/resources/public/File//EN//800618-Cardiology-1.0-EN-web.pdf

[31] Labtech Ltd., Labtech EC-12RT. [Online]. Available: http://www.labtech.hu/products/resting-and-stress-ecg-systems/ec-12rt

[32] Labtech Ltd., Labtech EC-12H, [Online]. Available: http://www.labtech.hu/products/resting-and-stress-ecg-systems/ec-12h

[33] Zenicor Medical Systems AB, ZENICOR-EKG. [Online]. Available:http://www.zenicor.se/sites/default/files/2010-1005

[34] HealthFrontier Ltd., HealthFrontier clickHolter, [Online]. Available:http://www.labtech.hu/products/resting-and-stress-ecg-systems/ec-12h

[35] Card Guard Scientific Survial Ltd. Card Guard CG-7000DX-BT,[Online]. Available: http://www.lifewatch.com/upload/infocenter/info_images/25012010232255@PN_BC00031_Rev03_CG7000DXBT.pdf

Anpeng Huang (M05) received the M.S. degree from the University of Elec-tronic Science and Technology of China, Sichuan, China, in July 2000, andthe Ph.D. degree from Peking University, Beijing, China, in June 2003. FromMay 2004 to January 2005, he was a Visiting Scholar at the University of Wa-terloo, Waterloo, ON, Canada. From February 2005 to March 2008, he was aPostdoctoral Researcher in the Department of Computer Science at the Univer-sity of California, Davis (UC Davis), CA, USA. Since November 2007, he hasbeen an Associate Professor in the state key lab of advanced optical commu-nication systems and networks, wireless communications lab, and PKU-UCLAjoint research institute of Peking University (PKU), China. He has more than40 journals and conference papers, is the holder of 36 patents and US pendingpatents (with PCT application), the advisor of Best Student Paper Award win-ner at 2012 14th IEEE HEALTHCOM conference, and the founder of mobilehealth lab in PKU. His research interest includes mobile health.

Chao Chen (S13) received the B.S. degree from the School of Electronic En-gineering and Computer Science at Peking University, Beijing, China, in July2011. He is currently working toward the Graduate degree at Peking University.

His research interests include mobile health, clinical data mining, and ma-chine learning.

Kaigui Bian (M11) received the Ph.D. degree in computer engineering fromVirginia Tech, Blacksburg, VA, USA in 2011.

He is currently an Assistant Professor in the School of EECS, Institute of Net-work Computing and Information Systems, Peking University, Beijing, China.His research interests include mobile computing, cognitive radio networks, net-work security, and privacy.

Xiaohui Duan received the B.S. and M.S. degrees in electrical engineeringfrom Peking University, Beijing, China, in 1989 and 1992, respectively.

He is currently a Professor with the School of Electronics Engineering andComputer Science, Peking University. His current research interests includecommunication signal processing, biomedical signal processing, and sensorsystem.

Min Chen photographs and biographies not available at the time of publication.

Hongqiao Gao photographs and biographies not available at the time of pub-lication.

Chao Meng photographs and biographies not available at the time of publica-tion.

Qian Zheng photographs and biographies not available at the time of publica-tion.

Yingrui Zhang photographs and biographies not available at the time of pub-lication.

Bingli Jiao (M05) received the B.S. and M.S. degrees from Peking University,Beijing, China, in 1983 and 1988, respectively, and received the Ph.D. degreefrom the University of Sarrbruecken, Saarbrucken, Germany, in 1995.

He became an Associate Professor and Professor with Peking Universityin 1995 and 2000, respectively. His current interests include communicationtheory and techniques and sensor design.

Linzhen Xie received the B.S. degree from Peking University, Beijing, China,in 1963.

He was a Visiting Scholar at the Department of Electrical Engineering andComputer Sciences, University of California, Berkeley, CA, USA, from 1980 to1982. He has been a Professor at Peking University in China, since 1978. He isthe founder of the State Key Laboratory of Advanced Optical CommunicationSystems and Networks at Peking University . One of his Ph.D. students was thewinner of 100 Distinguished Ph.D. Dissertations in China in 2000. He haspublished more than 140 papers in journals and at conferences in these areas,and is the holder of 21 patents. His research interests focus on optical networkand switching, optical waveguide technology, and wireless communications.

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WE-CARE: An Intelligent Mobile TelecardiologySystem to Enable mHealth Applications

Anpeng Huang, Member, IEEE, Chao Chen, Student Member, IEEE, Kaigui Bian, Member, IEEE, Xiaohui Duan,Min Chen, Hongqiao Gao, Chao Meng, Qian Zheng, Yingrui Zhang, Bingli Jiao, Member, IEEE,

and Linzhen Xie, Member, IEEE

AbstractRecently, cardiovascular disease (CVD) has becomeone of the leading death causes worldwide, and it contributes to41% of all deaths each year in China. This disease incurs a costof more than 400 billion US dollars in China on the healthcareexpenditures and lost productivity during the past ten years. It hasbeen shown that the CVD can be effectively prevented by an inter-disciplinary approach that leverages the technology developmentin both IT and electrocardiogram (ECG) fields. In this paper, wepresent WE-CARE, an intelligent telecardiology system using mo-bile 7-lead ECG devices. Because of its improved mobility resultfrom wearable and mobile ECG devices, the WE-CARE systemhas a wider variety of applications than existing resting ECG sys-tems that reside in hospitals. Meanwhile, it meets the requirementof dynamic ECG systems for mobile users in terms of the detectionaccuracy and latency. We carried out clinical trials by deployingthe WE-CARE systems at Peking University Hospital. The clinicalresults clearly showed that our solution achieves a high detectionrate of over 95% against common types of anomalies in ECG, whileit only incurs a small detection latency around one second, both ofwhich meet the criteria of real-time medical diagnosis. As demon-strated by the clinical results, the WE-CARE system is a usefuland efficient mHealth (mobile health) tool for the cardiovasculardisease diagnosis and treatment in medical platforms.

Manuscript received December 19, 2012; revised May 27, 2013; August4, 2013; accepted August 13, 2013. Date of publication; date of current ver-sion. This work was supported in part by the National Science and Technol-ogy Major Projects in Wireless Mobile Healthcare Projects under Contracts2012ZX03005013 and Contract 2013ZX03005008, and in part by the OkawaFoundation. This paper was presented in part at the IEEE ICC 2013 conference.

A. Huang is with the Mobile Health Lab, PKU-UCLA Joint Research In-stitute, the State Key Lab of Advanced Optical Communication Systems andNetworks, and also with the Center for Wireless Communication and Signal Pro-cessing, Peking University, Beijing 100871, China (e-mail: hapku@pku.edu.cn).

C. Chen, M. Chen, H. Gao, C. Meng, Q. Zheng, and Y. Zhang arewith the Mobile Health Lab, Peking University, Beijing 100871, China (e-mail: chao.chen@pku.edu.cn; chenmin7571@pku.edu.cn; hongqiaogao@pku.edu.cn; pkumengchao@gmail.com; qianzheng@pku.edu.cn; yingrui_zhang@pku.edu.cn).

K. Bian is with the Institute of Network Computing and Information System,Peking University, Beijing 100871, China (e-mail: bkg@pku.edu.cn).

X. Duan is with the Mobile Health Lab and also with the Center for Wire-less Communication and Signal Processing, Peking University, Beijing 100871,China (e-mail: duan@pku.edu.cn).

B. Jiao is with the Mobile Health Lab and also with the Center for Wire-less Communication and Signal Processing, Peking University, Beijing 100871,China (e-mail: jiaobl@pku.edu.cn).

L. Xie is with the Mobile Health Lab, and also with the State Key Lab ofAdvanced Optical Communication Systems and Networks, Peking University,Beijing 100871, China (e-mail: tydxlz@pku.eu.cn).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JBHI.2013.2279136

Index TermsClinical trial, cardiovascular disease (CVD),health risk monitoring, mobile health (mhealth), wearable efficienttelecardiology system(WE-CARE).

I. INTRODUCTION

THE cardiovascular disease (CVD) has become the leadingcause of human deaths, counting up to 29% of the totalglobal deaths, based on the WHOs The World Health Report2008 [1]. The main symptoms of cardiovascular disease includeserious myocardial ischemia (acute myocardial infarction), heartfailure, malignant arrhythmia, etc. As shown in [2], most ofthese symptoms can be foreknown by observing certain specificmanifestations of electrocardiogram (ECG) signals. The ECGmonitoring system has been used to detect such manifestations,and early detection can save valuable time for taking precau-tions against the cardiovascular disease. Thus, the prevention ofcardiovascular disease using mobile ECG monitoring systems isof paramount significance, which has garnered great attentionsfrom the research community.

The implementation of an efficient cardiovascular diseaseprevention system requires tremendous medical resources. Earlyalarm and medical instructions can be provided upon the detec-tion of early signs of the disease or disease progression [3]. Thedisease progression can be avoided by improving lifestyle, andmonitoring physiology parameters of out-hospital-patients [4].However, it is difficult to implement a long-term monitor foreach outpatient or home user due to limited medical resources.

Recent advances in wireless mobile networking technologieshave provided an opportunity to alleviate this problem; thisconcept is known as mobile health (mHealth) [5][8], which ischanging the way of health-care delivery today and hence, is atthe core of responsive health systems [9].

In this paper, we present WE-CARE, a Wearable EfficientteleCARdiology systEm, that can provide 24/7 health monitor-ing service with the help of wearable and mobile 7-lead ECGdevice.1 The use of five ECG electrodes helps collecting suffi-cient 7-lead ECG data to guarantee the detection accuracy with-out impairing the mobility of the system. More importantly,WE-CARE employs a two-step approach that distributes thedetection task to both the mobile device and the server suchthat the diagnosis capability of ECG devices can be exploited,

1The WE-CARE system has passed the test of Pharmaceutical Industry Stan-dards of China: Electrocardiographic Monitors, YY 1079-2008, GB9706.1-2007, and GB9706.25-2005.

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thereby reducing the length of the feedback cycle. Specifically,automatic ECG analysis algorithms are introduced to detectanomalies in ECG data, which can help significantly reduce thetime physicians spend in checking users ECG by 75% accord-ing to our clinical results.

For better understanding, the significance of WE-CARE sys-tem in this study, let us look at existing systems first. So far, theexisting 1-lead or 3-lead wireless ECG systems are for homecare users, in which the collected data are only for reference,and lack necessary clinical values [8]. In hospitals, 12-lead or18-lead systems are typically used but lack user mobility [10].It is desired to design a system that combines user mobilityand intelligent clinical function with heath-risk alert [11]. Mo-tivated by this trend, the WE-CARE system is developed for7-lead ECG real-time monitoring service over mobile networks(please note, a wireless network may be not mobile, but a mobilenetwork must be wireless).

The rest of the paper is organized as follows. Section II brieflydescribes the technical background. Section III introduces thesystem architecture and the design of the ECG device. Sec-tion IV describes the ECG detection mechanism. The perfor-mance evaluation of the system is demonstrated in Section V,and we conclude the paper in Section VI.

II. PRELIMINARIES AND RELATED WORK

A. Principles for Devising a Wireless ECG SystemThe wireless ECG system can significantly save the medi-

cal resources by remotely monitoring the cardiac status fromECG. However, there are three requirements for devising sucha system.

1) Support of mobile and wireless ECG device: Remote ECGmonitoring is of vital importance to out-of-hospital pa-tients who are exposed to a high rate of recurrence, and itrequires the support of mobile and wireless ECG devices.

2) Sufficient ECG data collection: Different cardiovasculardiseases may cause anomalies on different leads of ECG[12], and thus a wireless ECG system has to collect theECG data as complete as possible to guarantee the accuratedetection and diagnosis of cardiovascular diseases.

3) A small cycle of updating ECG data: The early warningmechanism in wireless ECG systems requires the real-time analysis of ECG signals. A small cycle of updatingthe collected ECG data to the data center will guarantee thereal-time alerts if the early sign of cardiovascular diseaseappears. As a result, the efficacy of a wireless ECG systemdepends on the cycle length that the device updates theECG data.

The wireless ECG monitoring system with a large numberof leads [13] are only designed for clinical usage (e.g., the 12leads system), which restricts the mobility of users that arelocated outside the hospital. For enabling the out-of-hospitalECG monitoring, many existing wireless ECG systems havemobile ECG devices with only one or three leads [14][17].However, the reduced number of leads limits the amount of ECGdata that can be collected in unit time, which further degradesthe performance of the real-time diagnosis and causes delay

Fig. 1. System Architecture. At the sensing layer, WE-CARE device collectsthe raw physiological parameter (ECG), and completes the task of QRS complexdetection. At the network layer, the ECG data collected and alerts generated atthe sensing layer are transmitted to the data center. At the application layer,WE-CARE server completes the computing-intensive task (T wave detection),and generates alerts if necessary; physicians get access to the alert and ECGdata to perform further in-depth diagnosis.

to the early warning/treatment against cardiovascular diseases.Moreover, the cycle of updating ECG data in existing dynamicECG systems used in hospitals are typically more than 24 h [18],which is too long for providing the real-time ECG alerts.

Therefore, no existing wireless ECG systems (either thosefor home use, clinical use, or those with Holter) can fully fulfillthe above design requirements. In this paper, we built 7-leadwearable and mobile ECG devices into the telecardiology sys-tem that leverages the tradeoff between the mobility supportand the sufficient collection of ECG data. Meanwhile, our builtsystem can meet the design requirements for the feedback cycleand response delay.

III. OVERVIEW OF WE-CAREIn this section, we present an overview of the WE-CARE sys-

tem, which provides a 24/7 ECG monitoring service for patientswith cardiovascular diseases or people that may have potentialcardiovascular problems.

A. Architecture of WE-CAREAs illustrated in Fig. 1, the system consists of three compo-

nents, namely, the mobile 7-lead ECG device, the ECG datacenter, and the relay device.

The ECG device completes four ECG monitoring tasks: thecollection, processing, display, and transmission of ECG data.The QRS and T-wave detection algorithms are implementedat the data processing step to detect the heart rate and certainabnormal phenomena of the ECG. Meanwhile, it transmits col-lected data to the data center for more complex diagnosis suchas data mining [19]. Note that the collected ECG data will bestored locally in the TF card of the device, and then transmittedto the data center via mobile networks (e.g., WCDMA or LTE-

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Advanced networks). As shown in Fig. 1, the five electrodes(RA, LA, LL, C, and a reference electrode) in WE-CARE sys-tem collect 7-lead ECG information, namely, I, II, III, aVR,aVL, aVF, and V1; and our detection algorithms are based onthe lead II. The clinicians are free to choose any other leads forexecuting detection algorithms when necessary.

The data center works as a diagnosis platform for the doctors.When the ECG data are uploaded, the agent program providesdoctors a real-time display of the ECG via the web server. Fromthe ECG database of the data center, the doctor is able to acquirea history of an individual users heart healthiness status.

Next, we introduce the ECG device in more details.

B. Operating ModesWe have implemented three operating modes on the ECG

device.1) In the conventional power-saving mode, the ECG device

continually collects the users ECG data, and stores itlocally on the device, without any automatic uploadingto the data center. The collected data will be delivered orcopied to the server of hospitals manually, and this modeis adopted by most existing dynamic ECG systems.

2) In the real-time mode, the ECG device continually collectsthe users ECG data, and then forward all of the real-timecollected data to the data center over mobile networks.The doctors are able to check the real-time or historicalECG data of a user via the web interface.

3) In the efficient monitoring mode, the ECG device continu-ally collects the users ECG data, and only transmits partsof the collected data to the data centeri.e., the 60-s-longECG per hour. Meanwhile, the ECG device performs alocal real-time diagnosis over all the collected data. If thelocal diagnosis mechanism identifies a potential risk, orif a manual alert is triggered by the user, the device willincrease the sample rate from 250 to 500 Hz for the 60-slong ECG collected, and then send it to the data center.The 60-s ECG data are obtained from 30 s before to 30 safter the anomaly/manual alert point. As long as the datacenter receives an alert, the doctor will be able to see thealert at the earliest convenience, and take actions for morein-depth diagnosis or even early treatment. Note that de-vice exceptions such as lead-off and connection-failurewill also generate an alert.

C. ECG Detection ProcessThe diagnosis of cardiovascular diseases depends on the ob-

servation of ECG owing to its convenience, reliability, and non-invasiveness. Many factors are useful to reflect the cardiac ac-tivity and help the observation, such as the P, QRS and T waves,ST segment, RR interval, and other parameters. The ECG detec-tion process includes a denoising phase and two ECG detectionphases.

1) In general, denoising is a necessary step before processingand analyzing the collected data to remove the noise in thedataset.

Fig. 2. PCB of the ECG device.

2) The QRS complex detection algorithm is implemented atthe device side in order to locate the R wave and detectthe R wave anomalies. Only the ECG data regarding Rwave anomalies will be uploaded to the data center in theefficient-monitoring mode.

3) On the server side, using the obtained locations of R waves,a T wave detection algorithm is implemented to furtherlocate the ST segment and detect the ST anomalies.

D. ECG Device1) Hardware System: The hardware modules of the mobile

ECG monitoring device are built on a printed circuit board(PCB), as shown in Fig. 2. The core of the hardware systemis an ARM microprocessor STM 32, which is used as the mi-cro controller unit (MCU) of the ECG device. It has abundantperipheral resources to meet the requirements of ECG moni-toring. The MCU controls various hardware modules/interfacesto complete the four ECG monitoring tasks. For example, theECG data collection of ECG is implemented by the ECG dataADS module via SPI bus. Note that the ECG lead wire is thehardware interface for input while the mobile module is thehardware interface for output.

The device measures 100 mm 50 mm 15 mm, weighsabout 200 g with a 1500 mAh Li-ion battery. Our clinical resultsshowed that the battery life of our device for one full chargingcycle is 6 h in the real-time mode, 72 h in power saving mode,and 48 h in monitoring mode, respectively. More information ofthe hardware can be found in [20].

2) Software System: The software system of the device is de-veloped on the transplanted C/OS-II system. The task managerhas the highest priority and it manages all the four ECG monitor-ing tasks. The collected ECG data has to be delivered to severaloutput modules, such as the WCDMA/LTE-Advanced transmis-sion module, the TF card slot, the LCD interface, etc. The datatransmission between on-device modules is implemented by theinterprocess communication mechanism of message queues.

E. Clinical Data Transmission MechanismThe clinical use of WE-CARE has posed constraints on the la-

tency and the error rate of clinical data transmission. To achieve

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Fig. 3. Frame structure.

these goals, we optimize the data transmission mechanism inthe following ways.

1) Data Compression and Data Frame Assembling at Trans-mitter: Before data transmission, we apply the Huffman encod-ing [21] and Run Length encoding (RLE) [22] methods to com-press the ECG data collected by the device. Since these appliedencoding methods are lossless, this data compression processwill not affect the accuracy of the ECG data. The compressedECG data will be transmitted in data frames, and each framecontains a 19-Byte-long frame header that specifies the uniqueuser ID, the serial number of the frame, CRC information, andother control information. The frame structure is illustrated inFig. 3.

2) Data Frame Reassembling at Receiver: To recover theECG data, the receiver (i.e., the server) has to collect all of ECGdata frames that are error-free, and put them in order according tothe serial numbers. However, data frames may be lost during thetransmission process using mobile module over wireless links;moreover, data frames may arrive at the receiver out-of-order.Hence, WE-CARE employs the reception window technique toaddress these problems.

Reception window: The server maintains a reception windowfor each device that is uploading ECG data by allocating atemporary reception cache of 512 KB for that device. The cacheis initialized as a 1-D array with array indexes corresponding tothe serial number of the data frames to be received. Accordingto our experimental statistics, a 512 KB cache is generally ableto cache the ECG data for half an hour.

Upon each data frame arrival, the frame header is parsedand the CRC checksum is verified. The frame that passes theverification is then recorded into the reception cache (the array)based on its ID and serial number. The server will periodicallysend out a retransmission request to the device regarding themissing frames or frames that fail to pass the verification untilthe number of retransmission requests reaches a limit. Generally,in our system, the maximally allowed number of retransmissionrequests is set to 5 for the ECG data without alert, and it is setto 10 for the ECG data with alert.

The server decides to terminate the reception process in thecurrent reception window when the cache is fulfilled withoutmissing frames or when the number of retransmission requestsreaches its limit. Once the server terminates the current receptionprocess, it decompresses data frames in the cache, records thedecompressed into an ECG record file, inserts the file as a newrecord entry into the database, and then flushes the receptionwindow.

Owing to the data compression and the reception windowtechniques employed, the transmission latency and the rate offrame loss can be significantly reduced.

Fig. 4. Error Rate of Transmission in We-Care. RT: Retransmission, C:Compression.

3) Transmission Efficiency: In order to evaluate the trans-mission efficiency of WE-CARE, we chose the transmissionerror rate and the data compression ratio as the evaluation mea-sures.

We calculated the number of data frames processed on eachdevice, and the number of data frames decompressed on theserver, and then derived the transmission error rate for device das follows:

d =Fl,d + Fe,d

Fd

where Fd denotes the number of frames processed at device d;Fl,d and Fe,d denote the numbers of lost and error frames corre-sponding to the specific device d at the server side, respectively.

The experimental results of transmission error rate are givenin Fig. 4, which clearly show that the transmission error ratesare significantly reduced by our transmission mechanism.

The data compression ratio at the device side is defined asCR = BrBc , where Br is the number of bytes of the raw data andBc is the number of bytes of the compressed data. According toour experimental results, the average compression ratio is 5.98.

IV. ECG DETECTION MECHANISM OF WE-CAREIn this section, we describe the ECG detection mechanism

of WE-CARE, which includes a denoising scheme, two ECGdetection algorithms, and the anomaly detection strategies.

A. Denoising of the ECG SignalDue to the presence of noise, the collected ECG data may

not be ready for display or readable for diagnosis, and thusthe preprocessing of the raw data is necessary. The ECG sig-nal detected by body surface electrodes contains seven differenttypes of interference, including power-line interference, base-line drift, electrode contact noise, electrode polarization noise,electromyogram signals, internal noise of amplifiers, and move-ment interference.

Among these sources of interference, the power-line interfer-ence near 50 Hz and its harmonics and the baseline drift below0.7 Hz are the two most contributing ones, which significantly

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Fig. 5. Performance of the denoising filter.

degrade the detection accuracy of the QRS complex. In the phaseof preprocessing of ECG signals, we focused on the removal ofthese two types of noises. Inspired by the filtering method pro-posed in [23], we combined a 50 Hz Notching Comb filter anda 0.7 Hz FIR high-pass filter as our digital filter to eliminate thetwo types of interference mentioned previously.

We applied our filtering method to an ECG dataset obtainedfrom the MIT-BH database, and performance of the filter isshown in Fig. 5. The blue curve (i.e., the bottom line) representsthe originally collected signal coupled with the baseline driftand other minor interference. The red curve (i.e., the upper line)represents the output of the filter. By comparing the originalsignal with the output, we can observe that the baseline driftof the ECG signal are completely eliminated. Meanwhile, thenoise in the horizontal segment is significantly reduced, whichimplies that the power-line interference is also removed.

B. QRS Complex Detection AlgorithmTo determine the start and end points of the QRS waves, it

is necessary to obtain the accurate location of R wave. Manyalgorithms have been used to locate the feature points of Rwaves, including the slope method, the amplitude method, thearea method, etc. [24].

The QRS complex detection algorithm in the WE-CAREsystem is based on the difference threshold arithmetic, whichcombines multiple existing methods. To meet the requirementof real-time ECG monitoring, a dynamic threshold adjustmentmechanism was implemented in the algorithm. Algorithm 1shows the pseudocode, and we briefly explain the procedurenext.

1) First, the thresholds of slope and amplitude are initializedas 60% of the highest values in the first-second ECG datastream respectively.

2) Then, we choose a nonoverlapping time window (usuallyset to three seconds in the WE-CARE system) that slidesalong the stream.

3) For every collected data sample in the time window, theECG device has to complete the following two tasks.

a) Location of R waves: The ECG device concludesthat an R wave is detected, if the slope betweenthe ith and (i + 2)th points and the amplitude ofthe ith point is equal or greater than their thresholds,respectively. Then, we search forward from the pointuntil the first extreme point is detected and the Rpeak is identified. We use the time stamp whenthe data point is collected as the location of the Rwave.

b) Calculation of heart beat: According to two adjacentR waves locations, the real-time heart rate can becalculated and written back into the frame header ofthe ECG data.

4) When the location of R wave is determined, we searchforward and backward for the first negative extreme pointthat could help locate the Q and S peaks, respectively.

5) The threshold values will be adjusted dynamically. If thehighest value increases, the threshold will be updated to60% of the maximum. If all points amplitude or slopevalues in current time window are less than the currentthreshold, the threshold value will be reduced by 20% un-til they reach the critical value. Both of the adjustmentpercentages aforementioned are obtained from our exper-iments.

Anomaly detection regarding R wave detection: Based on theresults of R wave detection, the WE-CARE device is able tocomplete the simple anomaly detection tasks, such as detectionagainst RR interval anomaly, R wave amplitude anomaly, heartbeat anomaly, etc. For example, the heart beat value can be

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obtained by calculating the interval between two R waves, whichis a useful tool to observe the symptom of ventricular fibrillation.

C. T Wave Detection AlgorithmThe ECG device transmits the sampled ECG data stream that

piggybacks the location information of QRS complex to the datacenter. The T wave detection is a computing-intensive task andrunning at the server side to further analyze the uploaded ECGdata.

To locate the T wave, we need to figure out the location of theJ point first, which is the point where the QRS complex joinsthe ST segment. We define d as the first-order difference of theECG signal, and the first zero-point after R wave is the S peak.Then we search forward along the ECG data from the S peakfor the first peak point p and get its amplitude d[p]. Based onthe value, we define a threshold,

= d[p] k,

where k = 13 is an empirical constant calibrated by experiments.The first point that is equivalent to the threshold is the J point.

Let LJ denote the obtained location of the J point, we define awindow (bw , ew ) as a function of the RR interval (RRI) value.

(bw , ew ) = (LJ , LJ + t RRI),

where t (0, 1) and it is adjusted according to the waveform.In our proposed system, the detection of T wave is based on

the characteristics of wavelet transform coefficient modules inthe window. To ensure the detection rate of T wave, we have tocarefully select the characteristic scale.

Since the energy of the QRS complex is higher than that ofthe T wave, the QRS complex affects the recognition of T wave.To address this problem, we lower the QRS complex to thebase line such that the T wave can be highlighted. Then, we de-compose the ECG signal with wavelet functions of Daubechies(db) series. Via the five-layer-decomposition with Daubechies 4(db4) wavelet, the T peak can be detected on the fifth scale andthe noise can be obviously restrained. Note that the db4 waveletis (2)(t) symmetric wavelet, and the T wave peak correspondsto the extreme point of wavelet transform. The T wave detectionalgorithm (see Algorithm 2) traverses all the extreme points inthe window including the false extreme points, and the extremepoint with the maximum amplitude of its corresponding originalsignal is recognized as the T peak.

Moreover, inspired by Mallats theory [25], our T-wave algo-rithm is also implemented on board. The process of wavelet de-composition is achieved via a group of orthogonal digital filters,which are employed to decrease the computational complexityof the wavelet transform.

Anomaly detection regarding T wave detection: The resultsof T wave detection obtained by the WE-CARE server are use-ful for detecting complex anomalies. For example, if the QT-interval (between Q and T peaks) is too wide, the WE-CAREsystem will generate an alert regarding the myocardial ischemiaand myocardial damage. Similarly, the symptom of T-wave in-version is typically a sign of chronic myocardial ischemia, left

ventricular hypertrophy, or an indication of acute period of my-ocardial infarction.

A more detailed description of our T-wave detection algo-rithm is illustrated in [26].

V. PERFORMANCE EVALUATION

In order to evaluate the performance and efficiency of theWE-CARE system, we chose a total number of 84 users atPeking University Hospital as experimental subjects, and thediagnosis was based on ECG data acquired by other hospitalfacilities (e.g., desktop ECG units).

A. Data CollectionUsers participating in the experiment were required to wear

the ECG acquisition device for 24 h a day, and they were dividedinto two groups. One group called the group of normal subjects(NS) included users without cardiovascular disease detected.The second group represented the set of users with cardiovascu-lar disease (CVD). All the subjects were free to stay in or leavethe hospital. Generally, the WE-CARE system is designed for24/7 daily health risk monitoring. In applications, the clinicianswould help users apply the electrodes and show them how touse the system. A manual of instructions was also provided topatients. To further evaluate the performance of our system, wealso carried out experiments based on the ECG data obtainedfrom the European ST-T Database (ESD).

When no anomaly was detected, 60-s long, 250 Hz samplingrate, 7-lead ECG data were uploaded to server periodically.When an anomaly was detected, the device uploads the datathat is obtained from 30-s before to 30-s after the anomalypoint, which is 60-s long, 7-lead ECG data with a sampling rateof 500 Hz. In order to avoid the potential error caused by packetloss during wireless communication, we backed up the last 24 hdata on the local device.

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Fig. 6. Evaluation of R Wave and T Wave Detection. FNR represents the falsenegative rate, and FPR represents the false positive rate.

B. Error Rates

To validate the performance of R wave detection algorithmin our system, we compared all 48 ECG cases in MIT-BIHArrhythmia Database [27] whose QRS complex locations wereincluded with our detection results. As the MIT-BIH databasegave the beat counts for the first 5 min of each record and theremainder of the record, we used the former part for validation.The result is shown in Table I.

For the 18348 QRS complexes above, the QRS complex de-tection ratio is 99.3%, which shows an outstanding performance.We also validated our algorithms on ECG data in European ST-T database [28] for R and ST detection performance, part of theresults are shown in Fig. 7, Tables II and III (Table III can bealso found in [26]).

As shown in the result, the T wave detection ratio of ouralgorithm is 97.5%, which is an improvement while current T-wave detecting ratio is no more than 95% [29]. Note that whenthe ST segment is with unnegligible noises (e0405.dat fromESD), the detection algorithm of WE-CARE is able to maintaina detection rate over 95%.

In our experiments, the collected data by the ECG devicewere also compared with physicians observation. As for datafrom European ST-T Database, the results were compared withthe note files. These two types of data are of a length of 300 s.We applied a similar approach to the performance evaluationof T wave detection. We measured the error rate and missingrate of T wave detection using the first 5 min of the record. Theresults are shown in Fig. 6, and we observed that a detection rateof 99.4% for R wave detection and that of 97.7% for T wavedetection.

C. Categorical Anomaly DetectionIn this experiment, we mainly focused on five common cat-

egories of anomalies, as listed in Fig. 8. We compared thedetected proportion of anomalies by our method with the manualtest statistics.

Fig. 7. Results of R and ST detection performance for data in European ST-Tdatabase. (a) No. e0103 record, (b) No. e0166 record, (c) No. e0405 record,(d) No. e0607 record.

As shown in Fig. 8, our system yields a high anomaly detec-tion rate in every category. This implies that physicians can sim-ply focus on those ECG data samples that receive an anomalydetection alert in most categories. As a result, it will save atleast 75% of time spent in anomaly judgment and localizationcompared with manual check, which greatly improves the ef-ficiency of the cardiology diagnosis system. However, specificECG anomalies such as ST segment elevation and depressionare difficult for wireless ECG system to automatically recognize,which still require humans efforts to identify.

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TABLE IQRS DETECTION PERFORMANCE FOR ECG DATA IN MIT-BIH ARRHYTHMIA DATABASE

TABLE IIQRS DETECTION PERFORMANCE FOR ECG DATA IN EUROPEAN ST-T DATABASE

TABLE IIIT WAVE DETECTION PERFORMANCE FOR ECG DATA IN EUROPEAN ST-T DATABASE

D. Response DelayWe synchronized the clocks of the data center server and the

device. Then, we calculated the response delay as the differencebetween the time point when the ECG dataare collected by thedevice and the time point when the server makes a decision(e.g., generates an alert). Table IV shows the response delay

of four types of anomalies: heart rate anomaly (HR), lead off(LO), data center connection failure (DCF), and manual alert(MA). To evaluate the response delay under high-concurrencyenvironment, we also simulated a scenario where 1000 devicesuploaded ECG data at the same time, and used a real device tovalidate the system performance. No increase of delay or othersystem performance degradations were observed.

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Fig. 8. Performance of detection algorithms in five categories of anomalies.(1) Resting RR interval normal range is between 0.6 (100 bpm) and 1 s(60 bpm), (2) T wave inversion is a feature of myocardial infarction and angina,(3) Abnormally prolonged or shortened QT interval means a risk of developingventricular arrhythmias, (4) Normal QRS complex is 80 to 120 ms in duration,(5) A high R wave amplitude may indicate an unusually large heart musclemass, an unbalanced size of a ventricle, or a large heart in relation to the chestsize or closeness to the chest wall.

TABLE IVEVALUATION OF AVERAGE RESPONSE DELAY FOR ANOMALY DETECTION

(UNIT: SECOND)

TABLE VCOMPARISON OF EXISTING REMOTE ECG SYSTEMS WITH WE-CARE

In addition, we also made a comprehensive comparisonamong the WE-CARE system and a number of other remoteECG systems, and the results are shown in Table V. In general,there is no any real clinical meaning when the number of leadsis less than seven. On the other hand, user mobility is limited ifthe number of ECG electrodes is greater than five (which crossover top-down body). Since the WE-CARE is devised for 24/7daily public healthcare monitoring, collected data of which isonly used for medical assistant in clinical diagnosis, the 7-leadsolution is the tradeoff choice for considering a combination ofadequate clinical information collection and user mobility re-quirement. To our best knowledge, this is the first 7-lead mobileECG system which passed medical standard tests and got thenational medical equipment production license.

VI. CONCLUSIONIn this paper, we present WE-CARE, an intelligent telecar-

diology system over mobile wireless networks. The ECG de-tection mechanism of WE-CARE includes two algorithms thatguarantee a high detection rate for anomaliesa rate of 99.3%for the QRS complex detection, and a rate of 97.7% for Twave detectionaccording to the clinical trial results. In theefficient-monitoring mode, the WE-CARE system saves themedical resources in terms of communication bandwidth andthe time of physicians. Moreover, the WE-CARE system meetsthe clinical requirements and can be applied to both inpatientsand outpatients, especially for the cardiovascular disease-pronepopulation. This study demonstrated that mHealth concept canbe turned into real applications with promising future. Our fur-ther research will focus on the detection against certain hard-to-recognize anomalies such as the ST segment elevation anddepression.

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