challenges and opportunities in cardiovascular health informatics

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 3, MARCH 2013 633

Challenges and Opportunities in CardiovascularHealth Informatics

Yuan-Ting Zhang∗, Fellow, IEEE, Ya-Li Zheng, Wan-Hua Lin, He-Ye Zhang, and Xiao-Lin Zhou

Abstract—Cardiovascular health informatics is a rapidly evolv-ing interdisciplinary field concerning the processing, integration/interpretation, storage, transmission, acquisition, and retrieval ofinformation from cardiovascular systems for the early detection,early prediction, early prevention, early diagnosis, and early treat-ment of cardiovascular diseases (CVDs). Based on the first author’spresentation at the first IEEE Life Sciences Grand Challenges Con-ference, held on October 4–5, 2012, at the National Academy ofSciences, Washington, DC, USA, this paper, focusing on coronaryarteriosclerotic disease, will discuss three significant challenges ofcardiovascular health informatics, including: 1) to invent unobtru-sive and wearable multiparameter sensors with higher sensitivityfor the real-time monitoring of physiological states; 2) to developfast multimodal imaging technologies with higher resolution forthe quantification and better understanding of structure, function,metabolism of cardiovascular systems at the different levels; and3) to develop novel multiscale information fusion models and strate-gies with higher accuracy for the personalized predication of theCVDs. At the end of this paper, a summary is given to suggestopen discussions on these three and more challenges that face thescientific community in this field in the future.

Index Terms—Body sensor networks, CVD, fast imaging, healthinformatics, information fusion, unobtrusive sensing, wearabledevices.

I. INTRODUCTION

CARDIOVASCULAR DISEASE (CVD) is still the leadingcause of death over the world. As reported by the World

Health Organization, around 17.3 million people died from CVD

Manuscript received December 16, 2012; revised January 28, 2013 andJanuary 29, 2013; accepted January 29, 2013. Date of publication February1, 2013; date of current version March 7, 2013. This work was supported inpart by the National Basic Research Program 973 (2010CB732606), the Guang-dong Innovation Research Team Fund for Low-Cost Healthcare Technologies inChina, the External Cooperation Program of the Chinese Academy of Sciences(GJHZ1212), the Key Lab for Health Informatics of Chinese Academy of Sci-ences, and the National Natural Science Foundation of China (81101120). Y.-L.Zheng, W.-H. Lin, and H.-Y. Zhang contributed equally to this work. Asteriskindicates corresponding author.

∗Y.-T. Zhang is with the Joint Research Centre for Biomedical Engineering,Department of Electronic Engineering, The Chinese University of Hong Kong,Hong Kong, and also with the Key Laboratory for Health Informatics of theChinese Academy of Sciences (HICAS) at SIAT, Shenzhen 518055, China (e-mail: [email protected]).

Y.-L. Zheng is with the Joint Research Centre for Biomedical Engineering,Department of Electronic Engineering, The Chinese University of Hong Kong,Hong Kong (e-mail: [email protected]).

W.-H. Lin, H.-Y. Zhang, and X.-L. Zhou are with the SIAT-Institute ofBiomedical and Health Engineering, Chinese Academy of Sciences, and alsowith the Key Laboratory for Health Informatics of the Chinese Academy of Sci-ences (HICAS) at SIAT, Shenzhen 518055, China (e-mail: [email protected];[email protected]; [email protected]).

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

Digital Object Identifier 10.1109/TBME.2013.2244892

in 2008, accounting for 30% of global deaths [1]. In the nextdecades, because of the impacts of hypertension, obesity, anddiabetes, and increasing number of the elderly, the number ofCVD deaths is estimated to be up to 23.6 million by 2030 [1].Among all the deaths caused by CVD, about two-thirds of themhappen in out-of-hospital settings [2]. Furthermore, the CVDwill increase the economic burden largely upon our society.

As the most important chronic disease, CVD can be causedby long-term cumulative effects of behavioral risk factors oftobacco use, unhealthy diet, or insufficient physical activity, inmost of cases together with metabolic and physiological risk fac-tors of high blood pressure (BP), raised serum cholesterol, andimpaired glucose metabolism [3]. Moreover, because of the lackof effective monitoring of health status and accurate predictivetools, a large number of people die from acute cardiovascularevents without prior symptoms [4]. Thus, in recent decades, theconsensus on effective prevention and control of CVDs is tomonitor and reduce the risk factors, and improve the manage-ment and healthcare through early detection and timely treat-ment at an early stage before obvious symptoms develop [5].

Advancing health informatics, determined as one of the 14grand challenges for engineering in the 21st century by the U.S.National Academy of Engineering [6], is crucial for realizingthe preventive medicine and implementing other p-Health tech-nologies including Predictive, Personalized, Precise, Pervasive,Participatory, Preemptive Healthcare [7]. From a biomedical en-gineering perspective, the development of cardiovascular healthinformatics requires the collection, processing, and analysis ofhealth information of the cardiovascular system, in order to un-derstand the mechanism of CVD and prevent the developmentof CVD in its early stage. This development eventually willbenefit early detection, early prediction, early diagnosis, andearly treatment of CVD [7]. To accelerate the development ofcardiovascular health informatics and engineering, a series ofinternational funding projects have been announced. In 1990s,the Cardiome Project, a part of Physiome Project, developedan integrated model of normal working heart upon an interna-tional efforts [8]. In 2004, the Heart Physiome Project funded bythe Wellcome Trust developed a multiscale cardiac functionalmodeling platform between New Zealand and U.K. One well-known output of this project is the demonstration of the mech-anisms that underlie cardiac arrhythmia and fibrillation [9]. In2008, the European Commission provided EUR13.90 million tosupport the euHeart Project (FP7-2008-IST-224495) for devel-oping patient-specific cardiovascular modeling framework forintegrated cardiac care [10]. In the same year, the HeartCycleproject was supported with a budget of around EUR 20.7 mil-lion, of which EUR 14.1 million was funded by the European

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634 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 3, MARCH 2013

Union as part of the EU 7th Framework Program, for improvingcare of heart patients through the development of innovativetelemonitoring solutions and closed-loop patient managementsystems [11]. In 2009, the Chinese National Basic Research 973Program supported a joint research project (2010CB732606)with a budget of RMB 28 million for developing multimodalhigh-resolution imaging technologies, unobtrusive multiparam-eter devices, and multiscale computation models for the per-sonalized screening and objective risk assessment of vulnerablepatients with blood plaque.

Despite a number of major grants and significant efforts de-voted to developing screening and diagnostic methods that canbe used for the prediction of CVDs, identifying victims beforeacute cardiovascular events occur remains a grand challenge.Therefore, there is still a strong need to advance cardiovascu-lar health informatics with the emphasis on the development ofscreening tools with higher sensitivity and risk assessment meth-ods with higher specificity for accurately and early identifyingpeople at CVD risk.

Previous studies reported by 58 medical experts have shownthat vulnerable patients were characterized as having vulnerableplaques (prone to thrombotic complications and rapid progres-sion), vulnerable blood (prone to thrombosis), and vulnerablemyocardium (prone to fatal arrhythmia), placing those people athigh risk of developing acute cardiovascular events [4]. There-fore, cardiovascular information acquisition can focus on devel-oping theories, technologies, and systems that are essential foraddressing vulnerable patients problems such as screening vul-nerable plaques, vulnerable blood, and vulnerable myocardiumusing the solutions of biomarker detection and biomedical imag-ing [4], [12], [13].

In addition, the Ohasama study suggested that daytime sys-tolic ambulatory BP variability and heart rate (HR) variabil-ity should be considered as independent risk factors for CVDmortality in the general population [14]. The Dublin studyshowed that the nighttime BP can be more effective in pre-dicting CVD mortality [15]. Those important clinical findingsindicate a strong need for unobtrusive devices for the continu-ous and real-time monitoring of risk factors for CVD mortalityprediction.

It has been shown recently that computational models inte-grating multiscale health information can provide another per-spective for a quantitative assessment of the physiological andpathological activities of organism from simulation environ-ment [8]. However, the lack of patient-specific features is anobstacle for these models to be applied into daily clinical prac-tices. Even a personalized model can be constructed; the realchallenge in modeling is to develop ones that can be invertedto allow a particular set of observations to predict the cardiacmechanisms that are producing the observed signals. A notableexample is Y. Rudy’s work to reconstruct the distribution ofaction potential or the activation pattern over the surface of epi-cardium using an individual’s body surface of electrocardiogram(ECG) and a Poisson model constraint [16]. Another notableexample of the inverse problem is B. He’s effort to develop amodel-based framework to reconstruct the electrophysiologicalactivities of the whole heart from noninvasive body surface ECG

measurements [17]–[19]. The potential of this model-based in-verse framework has been demonstrated by others [20], [21].

There are many roadblocks to progress. In this paper, wepropose and discuss following three core topics as the grandchallenges of cardiovascular health informatics for the comingdecades to achieve the ultimate goal of early detection, earlyprediction, early diagnosis, and early treatment of acute CVD.

1) To invent unobtrusive sensors for the real-time and contin-uous collection of physiological information, from whichcritical risk index can possibly be extracted for the predic-tion of the rupture of vulnerable plaques and the occur-rence of acute CVD events.

2) To develop fast high-resolution imaging technologies forthe objective evaluation of atherosclerotic risk, vulnerableplaque, and vulnerable myocardium.

3) To solve the inverse cardiovascular problem and developinformation fusion theories and technologies over thecomputational modeling platform for integrating healthinformation across multiscales, from molecules, cells, tis-sues, organs, to systems levels, for patient-specific quan-titative assessment of CVD.

II. TO INVENT UNOBTRUSIVE SENSORS FOR THE REAL-TIME

AND CONTINUOUS ACQUISITION OF CARDIOVASCULAR

HEALTH INFORMATION

Unobtrusive monitoring devices for CVD applications are de-signed to acquire physiological and behavioral signals and vari-ables without interrupting the subject’s daily life or even withoutconsciousness, such as ECG, photoplethysmogram (PPG), res-piratory rate, HR, temperature, blood oxygen saturation, BP,posture and kinematic activity, etc. Unobtrusive monitoringdevices can be implemented in two ways: 1) by embeddingwearable sensors into clothing [22] or accessories, such as ear-ring [23], ring [24], glove [25], and 2) by embedding ambientsensors in everyday objects, such as furniture, appliance, con-struction [26]–[28], etc. Unobtrusive sensing can also be clas-sified into two types according to the source of the signals, i.e.,passive and active. Some of passive sensing methods can be re-alized for unobtrusive detection of signals from the human body,such as the capacitive-coupling-based ECG measurements. Un-obtrusive active sensing, on the other hand, emits energy tohuman body and then detects the reflected or backscattered ra-diation from it, such as the radio or radar approach to detect HRremotely or remote infrared temperature measurements.

Connected through the wireless communication technologies,the acquired physiological health information from unobtrusivedevices can be transmitted to a remote control center. In thisway, the patients’ CVD state can be remotely monitored in out-of-hospital conditions in real time. Not only can it greatly reducethe medical cost caused by frequent visits to hospital, but alsoallow taking preemptive actions in response to the acute CVDevents. More importantly, some out-of-hospital measurementsmay be more valuable for the clinical diagnosis and treatment ofCVDs. It has been gradually recognized that clinical BP may failto provide adequate information of the true BP and might givemisleading information in clinical diagnosis, such as white-coat


hypertension. A joint scientific statement recommended thathome BP monitoring should become a routine component ofBP measurement in patients with known or suspected hyperten-sion [29]. Moreover, other clinical studies have demonstratedthat 24-h BP measurement can provide more valuable and in-dependent risk factors for the prediction of CVD mortality. TheDublin study showed that the relative hazard ratios (RHR) asso-ciated with 10 mmHg increase in systolic BP for CVD mortalitywere 1.02 (95% CI, 0.99 to 1.05), 1.12 (1.06 to 1.19), 1.21 (1.13to 1.28), 1.19 (1.13 to 1.27) for office BP, daytime, nighttime,and 24-h ambulatory BP, respectively [15]. The Ohasama studyshowed that daytime systolic ambulatory BP variability has asignificant linear relation with RHR for CVD mortality [14].These results indicated that ambulatory BP and its variability,especially nighttime BP, are superior risk predictors of CVDevents over clinical BP [15]. Therefore, it holds great signifi-cance to develop unobtrusive devices for real-time and continu-ous monitoring physiological states of CVD patients overnightor for a whole day.

Researchers have devoted great efforts in recent years to pro-mote the application of unobtrusive monitoring devices in CVDhealthcare. For ECG measurement, instead of adhering wet gelelectrodes directly to the surface of body, dry/noncontact elec-trodes based on capacitive coupling and embedded in furni-ture provide an effective solution for unobtrusive sensing ofECG [30]–[32]. A simple and noncontact pulse sensing tech-nique proposed by Poh et al. can automatically calculate the HRfrom digital color video recordings of the human face region,from which the blood volume pulse can be extracted by the inde-pendent component analysis method [33]. K. Humphreys et al.also built a noncontact PPG system with dual wavelength viacamera-based instrument towards remotely measuring the bloodoxygen saturation [34]. The pulse-wave-velocity (PWV)-basedBP estimation technique provides a very promising method forcontinuous and cuff-less BP measurement. Pulse transit time(PTT), defined as the time it takes for the pulse to travel from theheart to the peripheral, can be simply measured from ECG andPPG signals. Extensive studies have been conducted to justifythe potential of PTT as a surrogate of BP [35]–[41]. The resultsof an experimental test on 85 subjects showed that the estimatedsystolic and diastolic BP by PTT differed from the reference BPby 0.6 ± 9.8 mmHg and 0.9 ± 5.6 mmHg, respectively, whichis comparable to the Association for the Advancement of Medi-cal Instrumentation (AAMI) requirement, i.e., 5 ± 8 mmHg forsystolic and diastolic BP estimation, as shown in Fig. 1(a) [36].Some efforts have been devoted to establishing new standardsfor the evaluation of the accuracy of cuff-less BP measurementdevices [42]. This kind of cuff-less BP estimation method canbe easily implemented into furniture such as a normal chair [43],and a sleeping cushion [32] for the continuous and unobtrusivemonitoring of HR, PTT, BP, and other physiological parameters,as shown in Fig. 1(b).

Another concern in the development of unobtrusive devicesis the comfort of use and user-friendly design. For wearableimplementation, technologies of smart textiles have undergoneextensive development in recent years, aiming to provide aneffective and practical solution for seamlessly integration of

Fig. 1. (a) Mean and standard deviation of the difference in BP by differentmethods compared with AAMI standard [36]. (b) Prototypes of unobtrusivedevices: a smart health chair [43] and a sleeping cushion for unobtrusive HRand BP measurement [32].

all wearable sensors into a single garment, which is the essen-tial part of our lives, offering promise for future wearable de-vices [44]. Moreover, the emerging area of flexible electronicsprovides a brand new way to design the wearable devices [45].The critical features of these flexible electronics including theflexible structure, light weights, and biocompatibility will pushwearable devices a step further toward completely unobtrusivemonitoring.

Though significant progresses have been made in the pastfew years, there are still several great technical challenges tobe overcome to implement unobtrusive monitoring in real life,some of which are proposed below to call for more emphasis inthe future work:

1) To develop real-time, unobtrusive, and continuous meth-ods for measuring multiphysiological parameters andmonitoring human behaviors including physical activities.

2) To invent model-based approaches for the unobtrusive andcuff-less estimation of BP including central BP. ThoughPTT-based BP estimation is very promising for cuff-lessBP measurements, some confounding factors limited theaccuracy of this technique, such as preejection period [39],[46]–[48] and vascular tone [46], [49]. Quantitative mod-els taking into account these effects are needed to gaindeeper insight into the PTT-BP or PWV-BP relationshipto improve the accuracy of this technique.

3) To develop effective motion artifacts (MA) reduction orremoval methods. Though numerous solutions have beenproposed to address this problem of MA, such as designingmotion-resistant sensing units together with advanced sig-nal processing algorithms like adaptive noise cancellationmethods [23], and estimating features through multisen-sor fusion based on signal quality assessment and MA


identification [50], it is still a major obstacle to be over-come for achieving unobtrusive monitoring in real life.

4) To design the wearable devices of “MINDS” (i.e., the de-vices with the features of Miniaturization, Intelligence,Networking, Digitizing and Standardization (MINDS)[51] for the dynamic measurements of physiological andphysical signals). Miniaturization is required to makethe wearable devices being more comfortably and con-veniently worn by the patients without affecting theirdaily activities. Intelligent feature allows a user to op-erate wearable devices easily and ideally can support on-set decision-making or warning if needed. Networkingwearable or implantable devices [52] can form a bodysensor/area network enabling m-Health applications [30],[53], [54]. Digitization of all the analogue signals is a mustfor information acquisition, transmission, and processing.Standardization will ensure the objective evaluation ofperformance of these devices as well as for their industri-alization [42].

III. TO DEVELOP HIGH-RESOLUTION IMAGING FOR

DETERMINING ATHEROSCLEROTIC RISK AND SCREENING

VULNERABLE PLAQUE AND VULNERABLE MYOCARDIUM

Atherosclerosis is the main cause of acute CVD [55]. Thedevelopment of atherosclerosis may lead to unstable atheroscle-rotic plaque or vulnerable plaque which is characterized as ac-tive inflammation, a thin fibrous cap with a large lipid core,erosion or fissure of the plaque surface, intraplaque hemor-rhage, and superficial calcified nodules [55], [56]. Vulnerableplaque could narrow blood vessels or even occlude the vessel,resulting in the block of blood flow to vital organs, such asthe heart and the brain. If the treatment of atherosclerosis isdelayed, subsequently the rupture of vulnerable plaque wouldcause acute coronary death or stroke [55]. In addition, otherkinds of cardiac diseases, such as myocarditis, electrophysio-logical disorders, heart valvular disease, and other cardiomy-opathies (hypertrophic, dilated, orrestrictive), are often relatedto vulnerable myocardium [4]. Therefore, the successful pre-vention of CVD depends on whether atherosclerotic risk, vul-nerable myocardium, and vulnerable plaques, which have highlikelihood of thrombotic complications and rapid progression,can be characterized as early as possible [13].

Both invasive and noninvasive imaging modalities have pro-vided an insight into the structure and progression of asymp-tomatic atherosclerosis, vulnerable myocardium and plaque[12], [13]. Some invasive imaging modalities such as catheter-ization or mini-invasive tests such as optical coherence tomog-raphy and intravascular ultrasound (IVUS) have been used clin-ically for evaluating the vulnerability of plaque. Photoacousticimaging technique opens a new area for intravascular imag-ing [57]. In comparison with IVUS, the photoacoustic imagingtechnique with high spatial resolution can provide more detailedinformation of plaques.

Because of the convenience and cost-effectiveness, noninva-sive imaging modalities are more frequently used for screeningsubclinical atherosclerosis, high-risk plaque, and myocardium

Fig. 2. Noninvasive imaging techniques for early identification of CVD. Re-produced from [13]. (a) Example of an image captured by ultrasonography.The arrow indicates plaque burden. (b) Example of an image captured by CTtechnology. The arrow shows calcification. (c) Example of an image capturedby MRI. The arrow indicates lipid deposit. (d) Examples of images captured byPET, CT, and combined PET/CT. The visual target outlines the carotid artery.

vulnerability. Now many well-established risk factors are mea-sured by standard clinical imaging modalities, such as carotidintima-media thickness and ankle brachial index captured byultrasound (US) imaging, and the coronary artery calcium scorecalculated by computerized tomography (CT) imaging [58].These risk factors have been proved to be highly associatedwith the occurrence of fatal events, and are widely used inthe clinical guideline for the assessment of cardiovascular riskin asymptomatic adults [59]. Fig. 2 provides some examplesof using noninvasive imaging techniques for identification ofCVD [13]. Prominently, CT technology can assess plaque com-position, level of calcification, and coronary stenosis [13]. Mag-netic resonance imaging (MRI) can reliably detect and quantifycarotid/aortic plaque components such as lipids, fibro-cellulartissue, calcium, intraplaque hemorrhage [60], assess atheroscle-rotic burden, and potentially, plaque perfusion in noncoronaryarteries [13]. Several promising approaches to improve the MRIresolution have been reported such as multichannel coil design[61], compressive sensing algorithms [62], partially separablefunctions [63], parallel imaging algorithm [64], graphic process-ing unit technology [65], and non-Fourier encoding methodol-ogy [66]. Molecular imaging built on platforms of MRI, positronemission tomography (PET), single-photon emission computedtomography (SPECT), CT, US, and combined PET-CT, suchas [18F] fluorodeoxyglucose PET-CT [60] can help in detect-ing plaque inflammation [13], [67]. Currently, single moleculedetection has gained great attentions because of its potentialin locating plaques through biomakers. The quantum-dot-basedmolecular imaging with ultrasensitivity has great potential forcardiovascular applications [68], [69]. Molecular beacon imag-ing technology can illuminate beacons of disease state withoutthe need to reconstruct an image of the entire heart [70].

Table I presents the sensitivity and application status of usingnoninvasive imaging techniques for screening atheroscleroticplaque, which was collected by Kips et al. [12], etc. As can beseen from the Table, each of the current technologies has one


TABLE INONINVASIVE IMAGING FOR SCREENING ATHEROSCLEROTIC PLAQUE

or more limitations upon spatial/axial resolutions, penetrationdepth, and tissue characterizing capability, which will affect de-tection of vulnerable plaques in time [12]. Other limitations,such as the high investment of imaging machines (e.g., MRI,Multidetector CT), high expenditure on the tests using thesedevices, and potential risk derived by radiation exposure of CT,SPECT, PET, prevent current imaging modalities from beingused for conventional examinations in daily clinical practices.In the end, because the movement of cardiac system, respira-tory, swallow, blood flow, and casual body movement will causeblurred images, poor contrast ratio, and even failure of the ex-amination, the duration of the 3-D volume imaging of the heart,especially for MRI, should be greatly reduced [71]. Therefore,improving both temporal and spatial resolution of biomedicalimaging is crucially important for CVD applications. Higherresolution imaging will allow the early screening of vulnera-ble plaque and vulnerable myocardium, and early identifica-tion of atherosclerosis during clinical practices. Grand technicalchallenges in this area are to develop high-resolution imagingmodalities with high sensitivity and high contrast that can beused for the objective evaluation of vulnerable plaque includ-ing its morphostructure and components, and for the detectionof pathological markers. The challenges and opportunities incardiovascular imaging include but not limited to the followingtopics:

1) To develop real-time and high spatial resolution imagingmethods (especially for MRI) with the removal of the MAof dynamic organs for screening the vulnerable patientswith plaque.

2) To develop molecular imaging technology with high sensi-tivity for detecting biomarkers of inflammation activitiesand pathologies in atherosclerotic plaque, myocardium,etc.

3) To develop multimodal imaging modalities for identifyingand quantifying plaque components (e.g., fibrous cap andlipid core) with high accuracy.

IV. TO DEVELOP INFORMATION FUSION MODEL FOR EARLY

PREDICTION OF CVD

The increasing number of technologies, such as physiologi-cal monitoring, high-resolution imaging, biomarker detection,gene sequencing and so on, have provided a huge amount of in-formation of the heart, spinning multiscales from gene, protein,cell, tissue, organ, to the system [7], [8]. Therefore, integrating

multiscale information of the cardiovascular system for under-standing the CVD progress comprehensively and precisely andpredicting the CVDs early is becoming one great challengeat present. Because of advances in computing power and theunderstanding of the cardiac system, the electrophysiological,biomechanical, and the hydrodynamic models of cardiovascularsystem have been able to simulate many kinds of physiologicalbehaviors of the heart. Hence, the challenge of information fu-sion for early prediction of CVD should be met by developingmodel-based fusion frameworks or technologies to personalizemultiphysics models using personal multiscale health informa-tion obtained by multiparameter sensing and multimodal imag-ing techniques [72].

In order to understand the cause of CVD, quite a large numberof efforts have been spent on developing computational modelsfor simulating the physiology and pathology of cardiovascu-lar system, and applying these models for finding the causesof CVD. The capabilities of these models vary significantlyfrom simulating of the heart function, hemodynamics of arte-rial system, to the whole cardiovascular system including bloodcirculation [73], [74]; so is the complexity of these models,from simple zero-dimensional Windkessel model to complexmultiscale, multiphysics models which incorporating from cellsto systemic circulation [74]. One particularly great collabora-tion should be emphasized here is Cardiac Physiome Project.With an international contribution, this project has made a greatprogress in developing a multiphysics model, which has thecoupling of metabolic, electrophysiological, and biomechani-cal process, for integrating the cardiac structure-function re-lations at multiscale across from cell, tissue, to organ levels asshown in Fig. 3 [8]. In this project, the biomodel-based couplingapproaches have been extensively used for combining cardiaccontinuum tissue mechanics with electrophysiology, ventricularblood flow, and coronary hemodynamics in a meaningful physi-ological sense [75]. Another similar multiscale framework is theVirtual Physiological Rat Project, which develops a multimodelplatform with a coupling of metabolic and electrophysiologicalprocesses [76]. Furthermore, computational modeling of the hu-man vascular system enables the simulation of the hemodynam-ics in arterial bifurcations and arterial stenosis, where the heartattack or stroke is prone to occur [77]. In these fields of research,computational models were applied to simulate two main pro-cesses (plaque rupture [78] and thrombus formation [79]) thatlead to heart attack or stroke, in coronary and carotid arter-ies, respectively. The success in modeling of the cardiovascular


Fig. 3. Diagram of Cardiac Physiome Project. Reproduced from [8].

Fig. 4. Framework of the proposed prediction model [83].

system has greatly improved the understanding of clinical ap-plications. For example, a multiscale model that includes mitralvalve dynamics was used to understand ischemic mitral insuf-ficiency [74]. Another two models were used for evaluatingthe effect of cardiovascular system drugs [80] or planning ofsurgery [81].

Despite the great achievements in computational modelingof physiology and pathology of the cardiovascular system, theinformation fusion for personalizing the model of cardiovas-cular system, which eventually will benefit for quantitative as-sessment of the risk of specific patient, is still a challenge.Since atherosclerotic plaque rupture with subsequent thrombo-sis accounts for most of fatal acute myocardial infarction and/orsudden coronary deaths [82], many strategies of information fu-sion have been developed for understanding or predicting thisclinical event. Our group has proposed a personalized modelfor quantitative assessment of the risk of acute cardiovascularevents based on vulnerable plaque rupturing mechanism [83].The framework of this personalized prediction model is shown inFig. 4. The model not only takes traditional risk factors, sensitivebiomarkers, blood biochemistry, vascular morphology, plaque

Fig. 5. Illustration of multiscale modeling of cardiovascular system [84].

information, and functionality image information as inputs ofthe prediction system, but also gathers physiological informa-tion continuously from unobtrusive devices and body sensornetworks for near-term risk assessment of acute cardiovascularevents. In another example, a multiscale computational mod-eling framework (see Fig. 5) of the entire cardiovascular sys-tem was established as one closed loop for studying the globalhemodynamic influences of aortic valvular and arterial locatedin various regions [84]. In order to understand the informationcollected by different means, the control theories have been in-troduced to our community as one powerful information fusionstrategy [85]–[88]. Furthermore, this control-theory approachwas also applied to detect the pathologies of cardiac electro-physiology through the reconstruction of cardiac action poten-tial over myocardium [20], [89]. Though the potentials of infor-mation fusion strategies are gradually recognized, because ofthe modalities and the quantities of measurements of cardiovas-cular parameters are still increasing, the challenge of informa-tion fusion for early prediction of CVD becomes more difficult.Recently, a computing platform named OpenCMISS has beendeveloped for solving the problem caused by the huge side ofdata to information fusion [90]. In this platform, simulation ofcardiac system from cellular to organ level, and information fu-sion using imaging or biosignal data are integrated together overan efficient parallel computing system, which makes the pro-cess of big data and information fusion become feasible in nearfuture. Furthermore, OpenCMISS is an open-source project,which means anyone can use it freely as the starting point tobuild his or her own fusion framework. This will greatly benefitthe defeat of challenge in information fusion.

To overcome the difficulties and achieve the goal, there is stillmuch to be done. The challenges of information fusion for earlyprediction of CVD are outlined as follows:

1) To extract new risk factors at multiple scales from cardio-vascular systems with both high sensitivity and specificity,and to integrate physiological information, biomarker in-formation, and blood biochemistry with cardiovascular


imaging information for screening vulnerable plaque andvulnerable patient.

2) To include physiological mechanisms, especially whole-cell function, subcellular process, metabolic and signaltransduction pathways in the information fusion model forunderstanding the progress of a series of cardiovascularevents, such as atherosclerotic plaque rupture, thrombusformation [8], [73], [91], etc.

3) To refine the cardiovascular models at different scales in-corporating metabolism and functions remains an ongo-ing challenge. The advances of continuous measurementof physiological parameters, dynamic biology imaging,high-resolution imaging, genomic and proteomic data, andcellular and tissue properties can provide a wealth infor-mation of cardiovascular system, which offers a significantopportunity to refine the model with more features [75].For instance, with the advances in diffusion tensor MRI forquantifying fiber orientation [92], cardiac fiber structurenow can be well modeled and simulated.

4) To design the boundary conditions based on biophysicaland biochemical laws for coupling models with differ-ent dimensions/scales across common boundaries and forassimilating different information. Common boundariesbased on models of biomechanics were introduced byNordsletten et al. for coupling multiphysics inside onecardiac model [75]. Such couplings could be extendedto other parts of the cardiovascular system, such as theinteraction of inflammatory cells and fibrous cap tissue,with vessel hemodynamics that regulate the formulation,progression, and rupture of plaque.

5) To improve the efficiency of computing algorithms.Highly efficient computing algorithms should be carefullydesigned for managing and processing the huge amountsof multimodal data with acceptable time cost.

V. SUMMARY

The emerging technologies in cardiovascular health infor-matics, which mainly deals with the acquisition, processing,fusion, and interpretation of multiscale and multimodal cardio-vascular health information, open new opportunities for betterunderstanding the pathologies of CVDs, developing preferablepersonalized risk assessment tools to screen high-risk patients atearly stage, motivating the patients identified at cardiovascularrisk adhere to therapeutic lifestyle modification, and eventuallyachieving the goal of early detection, early predication, earlydiagnosis, and early treatment of CVDs. Health informatics,in general, can accelerate the paradigm shift from traditionalmedicine to the 6-P’s health care or p-Health, i.e., the participa-tion of the whole nation for the prevention of illnesses or earlyprediction of diseases such that preemptive treatment can bedelivered to realize pervasive and personalized healthcare [51].

In this paper, we have discussed three grand challenges re-lated to sensing, imaging, and information fusion with the aimsat promoting the fast development of reliable technologies forthe early identification and prediction of acute CVD in the nearfuture. The discussion of these three challenges may also be

applicable to other CVDs with appropriate modifications, suchas arrhythmia, cardiac myocarditis, heart valvular disease, andother cardiomyopathies. In summary, the ideal technologies forearly identification of CVD should be cost-effective, relativelynoninvasive, and widely reproducible, so that the technologiescan be adopted for monitoring and screening of the asymp-tomatic population. After that, stepwise approaches should bedesigned to further stratify risk, provide reliable diagnosis, andoffer avenues for convenient treatment or intervention. Thoughthe main focus of this paper is to discuss the noninvasive oreven unobtrusive techniques, it cannot be ignored that the pop-ular mini-invasive imaging techniques, such as photoacousticimaging, also show great potentials in early screening of CVD.Furthermore, the biochemical measurements from blood alsocan provide reliable CVD biomarkers, where the microfluidicdevices or bio-MEMS using emerging nanotechnology havebeen able to achieve high-sensitivity detection of the mark-ers [93], and multiplexed detection of different markers simul-taneously [94]. Meanwhile, it is noteworthy that the effective-ness of the novel cardiovascular risk markers developed by newtechnologies should be validated before it is adopted in standardclinical care according to the following procedures: primaryproof of concept, prospective validation in independent indi-viduals, proof of extra information when added to establishedrisk markers, evaluation of effect on patient managements, andfinally cost-effectiveness, which are defined by the AmericanHeart Association [95].

At the end, we should point out that the prevention of CVDis easier than the cure of CVD. Most of CVD is preventable byadopting a healthy lifestyle, such as exercise, diet modification,and nonpharmacological means of BP regulation (reduced salt,etc.).

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Yuan-Ting Zhang received the undergraduate andMaster’s degree in telecommunication from the De-partment of Electronics, Shandong University, China,in 1976 and 1981, respectively, and the Ph.D. degreein electrical engineering from the University of NewBrunswick, NB, Canada, in 1990.

He is currently the Director of Joint ResearchCenter for Biomedical Engineering and Professor ofDepartment of Electronic Engineering at the ChineseUniversity of Hong Kong (CUHK). He serves con-currently the Director of the Key Lab for Health In-

formatics of the Chinese Academy of Sciences (HICAS) at SIAT. He is thefounding Director of the CAS-SIAT Institute of Biomedical and Health Engi-neering and the founding Head of the Division of Biomedical Engineering atCUHK. His current research interests include wearable medical devices, bodysensor networks, physiological modeling, neural engineering, cardiovascularhealth informatics, and m-u-p-Heath technologies.

Dr. Zhang holds the fellowships from the International Academy of Medicaland Biological Engineering (IAMBE), the Institute of Electrical and ElectronicsEngineers (IEEE), and the American Institute of Medical and Biological Engi-neering (AIMBE) in recognition of his outstanding contributions to the devel-opment of wearable medical devices and mobile health technologies. He servescurrently on IEEE-EMBS Technical Committee on Information Technology inBiomedicine, HK-ITC Projects Assessment Panel, IAMBE Fellow Member-ship Committee, and the Editor-in-Chief of IEEE JOURNAL OF BIOMEDICAL

AND HEALTH INFORMATICS which was retitled from T-ITB in Jan. 2013. Hereceived the IEEE-EMBS outstanding service award in 2006. His research workhas won him and his students/teams numerous honors/awards including thebest journal paper awards from the IEEE-EMBS, best conference paper awards,and the Grand Award in e-Health at the Asia-Pacific ICTAAC in Melbourne in2009. He was elected to the AdCom of IEEE Engineering in Medicine and Biol-ogy Society (EMBS) in 1999 and became previously the Vice-President of theIEEE-EMBS in 2000. He served as the Technical Program Chair and the Gen-eral Conference Chair of the 20th and 27th IEEE-EMBS Annual InternationalConferences in 1998 and 2005, respectively. He also served on the IEEE Medalon Innovations in Healthcare Technology Award Committee and IEEE FellowElevation Committee, and he was Editor-in-Chief of IEEE TRANSACTIONS ON

INFORMATION TECHNOLOGY IN BIOMEDICINE (T-ITB).

Ya-li Zheng received the B.E. degree in electronicscience and technology from Beijing Jiaotong Uni-versity, China, in 2007, and the M.S. degree in micro-electronics and solid states electronics from PekingUniversity, Beijing, China, in 2010. She is currentlyworking toward the Ph.D. degree in electronic en-gineering at the Chinese University of Hong Kong,Hong Kong.

Her current research interests include noninvasiveblood pressure measurement, modeling of physio-logical systems, and wearable medical devices for

p-health.

Wan-Hua Lin received the B.E. and M.E. degreesfrom Central South University, Changsha, China, in2006 and 2009, respectively.

She is currently working as an Assistant Professorat the Institute of Biomedical and Health Engineer-ing, Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences, ShenZhen, China, andthe Key Laboratory for Health Informatics of the Chi-nese Academy of Sciences (HICAS), Shenzhen.

Heye Zhang received the B.S. and M.E. degreesfrom Tsinghua University, Beijing, China, in 2001and 2003, respectively, and the Ph.D. degree fromHong Kong University of Science and Technology,Hong Kong, in 2007.

He is currently an Associate Professor at the Insti-tute of Biomedical and Health Engineering, Shen-zhen Institutes of Advanced Technology, ChineseAcademy of Sciences (CAS), ShenZhen, China. Heis also with the Key Laboratory for Health Informat-ics of the Chinese Academy of Sciences (HICAS),

China. His research interests include cardiac electrophysiology and cardiac im-age analysis.

Xiaolin Zhou received the Ph.D. degree in infor-mation systems from the University of Aizu, Aizu-Wakamatsu, Fukushima, Japan, in 2011.

He is now an Assistant Professor at the Instituteof Biomedical and Health Engineering, Shenzhen In-stitutes of Advanced Technology, Chinese Academyof Sciences, ShenZhen, China. He has authored andco-authored over 20 journal and conference papers,and holds two patents. His research interests includebiomedical signal processing, smart monitoring sys-tem, and relevant software development.

challenges and opportunities in cardiovascular health informatics

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