IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 18, NO. 3, MAY 2014

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Enabling Smart Personalized Healthcare: A Hybrid Mobile-Cloud Approach for ECG Telemonitoring Xiaoliang Wang, Qiong Gui, Bingwei Liu, Zhanpeng Jin, Member, IEEE, and Yu Chen, Member, IEEE

Abstract—The severe challenges of the skyrocketing healthcare expenditure and the fast aging population highlight the needs for innovative solutions supporting more accurate, affordable, flexible, and personalized medical diagnosis and treatment. Recent advances of mobile technologies have made mobile devices a promising tool to manage patients’ own health status through services like telemedicine. However, the inherent limitations of mobile devices make them less effective in computation- or data-intensive tasks such as medical monitoring. In this study, we propose a new hybrid mobile-cloud computational solution to enable more effective personalized medical monitoring. To demonstrate the efficacy and efficiency of the proposed approach, we present a case study of mobile-cloud based electrocardiograph monitoring and analysis and develop a mobile-cloud prototype. The experimental results show that the proposed approach can significantly enhance the conventional mobile-based medical monitoring in terms of diagnostic accuracy, execution efficiency, and energy efficiency, and holds the potential in addressing future large-scale data analysis in personalized healthcare. Index Terms—Electrocardiograph (ECG), medical monitoring, mobile cloud, telemedicine.

I. INTRODUCTION CCORDING to the World Health Organization (WHO) [1], the United States spends about 17.6% of its gross domestic product on healthcare, the highest level in the world and far higher than the percentage for other developed countries (9.3% on average). Nevertheless, the use of healthcare services in U.S. is far below that of comparable countries [2], reflecting greater inefficiency and higher prices for healthcare services in the United States. The skyrocketing medical expenditures and continuous aging of the world’s population demand transformative technological innovations to provide more effective and affordable healthcare services, available to anyone at any time and in any place [3]. A critical and costly part of current healthcare systems is the monitoring of patients’ vital signs and other physiological signals, all of which play significant roles in physicians’

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Manuscript received May 16, 2013; revised August 15, 2013; accepted October 7, 2013. Date of publication October 17, 2013; date of current version May 1, 2014. X. Wang, Q. Gui, B. Liu, and Y. Chen are with the Department of Electrical and Computer Engineering, Binghamton University, State University of New York, Binghamton, NY 13902 USA (e-mail: [email protected]; [email protected]; [email protected]; [email protected]). Z. Jin is with the Departments of Electrical and Computer Engineering, and Bioengineering, Binghamton University, State University of New York, Binghamton, NY 13902 USA (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JBHI.2013.2286157

diagnostic processes. Modern inpatient and outpatient facilities can provide a high level of protection to clinically ill patients, through a set of resting, bedside medical monitoring equipment. However, less attention has been paid to long-term, off-site or in-home care that is believed to be one of the most effective ways for addressing increasingly severe chronic diseases [4]. The highly specialized and extremely expensive medical monitoring equipment found in hospitals is neither easily accessible nor affordable for those scenarios. Recent advances in wireless body sensors and mobile technologies have promoted the use of mobile-based health monitoring and alert systems (usually referred as “mHealth”). Such systems aim at providing real-time feedback about an individual’s health condition, while alerting in case of health-threatening conditions. In the United States, it is reported that 88% of adults are cellphone owners [5], and the number of smartphone users is expected to be approximately 200 million by 2016 [6]. The increasing popularity of mobile devices can forge new opportunities toward the grand vision of “pervasive healthcare” [7]. Recently, many mobile-based medical monitoring devices have been developed with the capability of processing a wide variety of classes of physiological signals [8], [9]. However, the limited computational power, storage space, and battery life of existing mobile devices significantly limit their ability to execute resource-intensive applications [10]. Recently, the fastgrowing cloud computing technology has led to a novel computing paradigm, called mobile cloud computing (MCC), which allows users an online access to unlimited computing power and storage space. This paradigm not only enables users to enjoy convenient, versatile, and efficient computing services, but also raises the possibility of providing more accurate offsite personalized medical diagnosis and treatment, as shown in Fig. 1. In this study, we propose a new solution to renovate and promote the use of mobile devices in healthcare, leveraging the emerging cloud computing. Under the proposed architecture, mobile devices can be used to acquire various physiological signals from a set of ambient/body sensors and perform the regular lightweight on-site diagnostic processing tasks. To accelerate those intensive computations and extend the battery life of mobile devices, the acquired physiological signals will also be transferred to a cloud service environment to perform computation-intensive algorithmic processing (such as the training procedure demanded in most of the machine learning algorithms). The algorithmic processing results, normally in the form of a patient-specific, finer-tuned, or updated diagnostic processing engine, will be immediately deployed on the mobile device through a nearly seamless interaction between the mobile and

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Fig. 1.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 18, NO. 3, MAY 2014

Diverse telemedicine applications based on the cloud.

the cloud. Such an interaction will enable the mobile device to effectively detect physiological abnormalities and, thus, raises diagnostic alarms. This application mode can be of particular significance to patients whose physiological signals need to be monitored continuously. Physicians can also perform more accurate diagnoses by accessing the archived physiological data via web applications. The rest of the paper is organized as follows. Section II provides a survey of related work on the applications of MCC in healthcare. Section III describes the architecture of the proposed hybrid mobile-cloud approach in addressing the needs of personalized pervasive medical monitoring. Section IV presents a case study of mobile-cloud-based electrocardiograph (ECG) monitoring. Section V illustrates a set of experiments to evaluate the efficacy and efficiency of the proposed solution. Conclusions and future work are provided in Section VI. II. RELATED WORK According to Kovachev et al. [11], “Mobile cloud computing is a model for transparent elastic augmentation of mobile device capabilities via ubiquitous wireless access to cloud storage and computing resources, with context-aware dynamic adjusting of offloading in respect to change in operating conditions, while preserving available sensing and interactivity capabilities of mobile devices.” MCC has created a tremendous amount of momentum toward increasing access to healthcare via telemedicine [12]. However, the potential application of cloud computing in telemedicine is not yet clearly recognized and identified [13], [14], and a lot of open problems need to be investigated. A proof-of-concept, patient-centric, home healthcare service platform has been developed by a European Union consortium, based on an advanced trustworthy cloud infrastructure [15]. This cloud-based platform can remotely monitor, diagnose, and assist patients outside of a hospital setting. Patients’ medication records will be stored securely in the cloud and, thus, be acces-

sible by the patient, physicians, and pharmacy staff. The main goal of this project is to demonstrate how the quality of in-home healthcare can be improved cost efficiently through the use of cloud computing. Hsieh and Hsu [16] presented a 12-lead ECG telemedicine cloud service enabling ubiquitous delivery of interhospital ECG records. This cloud-based telemedicine service enables hospitals to store and manage patients’ ECG records via web access through the Internet connection of clinically used ECG instruments, and thus can realize interoperability across various mobile and fixed devices. Two mobile-based self-reporting and monitoring telemedicine sytems were developed on Google App Engine, named “SickleREMOTE” [17] and “caREMOTE” [18], respectively, to facilitate the care of pediatric sickle cell diseases and investigate the health-related quality of life of cancer patients. Shen et al. [19] proposed a cloud-based electroencephalograph signal analysis system to detect epileptic seizures and brain disorder diseases, where the computation-intensive functions of feature extraction, feature selection, and support vector machine (SVM) classifier are implemented and deployed using cloud services. Many other similar studies [20]–[22] have also fully demonstrated the potential of mobile- and cloud-based telemedicine solutions in enabling continuous monitoring of patients’ health status and, thus, ensuring timely treatment and care. III. SYSTEM ARCHITECTURE Wearable body sensors and mobile devices have been widely used to monitor the health status of patients or the elderly and generate alarms in case of imminent medical conditions. However, the limited computational power and energy supply of mobile devices result in either high false alarm rate or short battery life, prohibitive for continuous pervasive medical monitoring. Cloud computing embraces new opportunities of transforming healthcare delivery into a more sustainable manner. In this study, we will examine the performance of a hybrid approach by taking advantage of the real-time, on-site monitoring capability of mobile devices and the abundant computing power of the cloud. We seek to investigate a dynamic workload balancing strategy to meet the needs for both high processing accuracy and high energy efficiency. As shown in Fig. 2, in telemedicine, the mobile devices can be connected wirelessly to physiological body sensors to collect data, such as blood pressure, temperature, heart rate, and ECG [23]. Those medical monitoring data can be routed to the physician for detailed evaluation or to a computer-aided diagnostic program to automatically identify any abnormalities in physiological measurements and provide the alerts or warnings to caregivers for timely response [24]. The use of portable physiological and cognitive monitoring systems will enable physicians (and patients) to closely monitor patients’ health status and effectively prevent major medical conditions without the costly and time-consuming hospital visits. This type of telemonitoring is particularly effective for managing chronic diseases for elderly adults such as diabetes, hypertension, and cardiovascular diseases (CVDs), and has been shown to reduce hospitalization and mortality rates [25].

WANG et al.: ENABLING SMART PERSONALIZED HEALTHCARE: A HYBRID MOBILE-CLOUD APPROACH FOR ECG TELEMONITORING

Fig. 2.

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Telemedicine in a mobile cloud.

Conventional diagnostic analysis is usually based on fixedvalue thresholds, or simple combinations of rules based on predetermined thresholds [26]. However, the lack of individualspecific applicability and adaptivity makes it less effective in capturing real, clinically significant medical conditions and results in substantial number of false alarms. More sophisticated machine learning techniques have also been extensively investigated and used in identifying imminent medical problems by automatically comparing and recognizing the abnormal behaviors from a huge amount of physiological signal and vital sign data. Those algorithms have shown superior performance in correctly classifying various medical conditions, based on well-established supervised training procedures. For instance, according to our earlier studies, the artificial neural network (ANN)-based ECG signal processing algorithm can achieve over 95% accuracy in detecting various cardiac arrhythmia [23], and the SVM-based multiparameter vital sign analysis can significantly reduce the amount of false alarms [10]. However, in order to achieve a satisfactory training performance, those machine learning approaches usually demand a large set of a priori knowledge as the training dataset and iteratively perform the computation-intensive training processes, which make it impossible and unfeasible to execute on a mobile platform. It may take more than 16 weeks to sufficiently train the neural network implemented for cardiac arrhythmia detection [27] using the entire MIT-BIH database [28], [29]. Our previous study [10] also presents the findings that a fully charged smartphone can only last about 6 h when using a moderate fuzzy logic algorithm to perform the multiparameter vital sign analysis. To address the increasing demands on more sustainable use of mobile devices in telemedicine applications, it is imperative to explore novel alternative solutions to strategically manage the workloads on the mobile devices. Cloud computing embraces new opportunities of alleviating the computing loads of mobile devices by providing a flexible and scalable computing platform, which also results in a more cost-effective and energy-efficient solution for future large-scale medical data management. In this study, we propose to migrate the highly involved supervised training procedure, which represents the most computationintensive and resource-consuming tasks in all those machine learning techniques, into the cloud computing infrastructure.

The mobile devices will, thus, transmit all sensing data acquired from wearable body sensors or portable physiological monitors to the cloud, which now can provide a large pool of easily accessible dataset for the supervised training procedures. On the other hand, the implementations of those machine learning algorithms deployed on mobile devices can continue their regular classification processing without any cease, based on the latest trained configurations. Once the supervised training on the cloud is finished, the well-trained configurations will be sent back to the mobile devices and, thus, to update the existing implementation instance of the machine learning algorithms on the mobile. In addition to the benefits of substantially reduced power consumption and extended battery life of the mobile devices, this bidirectional, dynamically adaptive workload migration approach can constantly improve the performance of the deployed machine learning techniques by regularly tuning and updating them based on the most recent training results. As new sensing data of a specific human subject is continuously processed on the mobile and backed up on the cloud, the whole system holds the potential to gradually evolve itself toward an even higher diagnostic accuracy through unrelenting individual-specific training and adaptation. In this paper, we will also show how this synergistic interaction can improve the diagnostic accuracy by incorporating patient-specific physiological characteristics through the periodical fine tuning and training for the deployed processing engine. IV. CASE STUDY: MOBILE-CLOUD-BASED ECG MONITORING AND ANALYSIS Among all medical monitoring services, the monitoring of ECG signals is of particular interest and value to the whole society [30]. CVD is the single leading cause of death in the United States, and according to the American Heart Association [31], an estimated 83.6 million American adults have one or more types of CVD and over 2150 Americans die of CVD each day. Cardiac arrhythmia is a very common type of CVD and is believed to be responsible for most of the sudden cardiac deaths. The most common test for a cardiac arrhythmia is through screening and analyzing the ECG, which measures the electrical impulses of the heart via electrodes on the skin.

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Fig. 3.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 18, NO. 3, MAY 2014

Processing flow of ECG signals on the proposed mobile-cloud platform.

Over the past several decades, ECG-based CVD detection has been extensively studied in both research and clinical settings. A typical ECG processing flow includes the following stages: signal preprocessing, feature extraction, heartbeat classification, and diagnostic decision making. Specifically, the signal preprocessing is intended to remove any noise or artifacts [32], such as baseline drift and power-line interference. The processed ECG signals will then be screened to extract statistical and morphological features, among which the most representative features are the QRS complexes [33]. After that, the extracted features will be compared against standard patterns or individual-specific characteristics to be classified into different categories that represent a wide variety of medical conditions [34]. Based on the classification results as well as the clinical significance and duration of the detected medical conditions, physicians can confirm the presence of CVDs and make diagnostic decisions. Given the increasing demands on pervasive healthcare and the recent advances in mobile technologies, many portable ECG monitoring and diagnosis systems have been developed based on a wide variety of handheld devices, like PDAs and smartphones [8]. However, as discussed in Section III, the limited computational capability, storage space, and battery life of mobile devices make it less effective to perform more sophisticated tasks on site, especially for the computation- and data-intensive training in machine learning approaches that are often used for the classification of heartbeats and other ECG features. For instance, it may take hours to days to complete the training of an ANN-based ECG classification algorithm on the smartphone, according to our previous study [27]. In this case study, we adopt the classic Pan-Tompkins algorithm [35], a very popular real-time QRS complex-based heartbeat detection approach that reports a predictive accuracy of up to 99.3%. The heartbeat classifier is developed based on a 30-neuron ANN whose inputs are the raw ECG signals. For each identified heartbeat, a total 41 sample points are used as the ANN inputs, including 14 points before the fiducial mark and 26 points after [36]. For the training process, without loss of generality, three types of heartbeats are considered: normal, premature ventricular contractions, and other beats. In this way, the target ANN contains 41 inputs and three outputs (i.e., three types of heartbeats). We implemented and deployed the entire ECG processing flow onto a hybrid mobile-cloud prototype. The mobile device was a Google Galaxy Nexus smartphone with the Android 4.2

Jelly Bean system. The smartphone contains a 1.2-GHz dualcore ARM Cortex-A9 microprocessor, 1-GB memory, and a 1750-mAh battery. The cloud computing infrastructure was developed based on the Xen Cloud Platform and a Dell PowerEdge M620 server, equipped with 12 Xeon 2.5-GHz cores and 64GB memory. All the experiments were performed based on the MIT-BIH arrhythmia database [28], [29]. The ECG processing flow on the developed prototype has been illustrated in Fig. 3. All incoming ECG samples will first be processed to detect and extract the heartbeats (not shown in the figure) that are then forwarded to the ANN-based processing engine for heartbeat classification. The ANN engine is initially set to the default configuration, with which all the internal synaptic weights are randomly determined. When the mobile device launches the ECG processing, the patient’s historical ECG data along with physicians’ diagnostic annotations (“golden alarms”) are sent to the cloud server to train the ANN engine that is deployed on the cloud server with exactly same configuration as the one deployed on the mobile device. Given the fact that medical monitoring is supposed to be performed in a continuous (nonstop) manner to ensure any abnormal conditions can be accurately detected and recognized, we implemented two separate execution threads on the mobile device in order to minimize the interference. While the training data (e.g., 1/3 of a patient’s record from the MIT-BIH database) are transmitted to the cloud via Thread 1, the newly acquired ECG signals (e.g., the remaining 2/3 of a patient’s record in our study) will be concurrently processed based on the ANN engine (with default configuration) on the mobile, through the other computing thread—Thread 2. Based on the received training dataset, the ANN engine on the cloud will be well trained (t0 ), which results in an optimized weight matrix and a TRAINING_READY signal. The Thread 1 on the mobile keeps snooping the communication channel with the cloud, and immediately starts to receive the optimized weight matrix when the TRAINING_READY signal is detected. During this period, the Thread 2 on the mobile continues to process all incoming ECG signals (t1 ) through the ANN engine’s feed-forward calculations, though still based on the initial synaptic configurations. When the Thread 1 has completely received the entire trained weight matrix, the Thread 2 will cease the current processing and temporarily discard all incoming ECG samples to update the ANN engine on the mobile using the trained weight matrix. It is manifest that this updating procedure (tc ) will lead to a vulnerable off-line state for users

WANG et al.: ENABLING SMART PERSONALIZED HEALTHCARE: A HYBRID MOBILE-CLOUD APPROACH FOR ECG TELEMONITORING

TABLE I EXPERIMENTAL DATASET (NUMBER OF HEARTBEATS) AND CARDIOVASCULAR ARRHYTHMIA DETECTION ACCURACY (IN PERCENTAGE) ON THE MOBILE-CLOUD PLATFORM

because no ECG analysis will be performed during this period of time. Therefore, one of our design goals is to minimize the off-line duration (tc ) caused by updating the ANN engine on the mobile. Once the ANN engine has been updated, it will immediately resume the standard feed-forward calculations for all new incoming ECG samples (t2 ), with the optimized synaptic weight configurations. This mobile-cloud interaction will be periodically iterated when the current ANN configuration is no longer effective in capturing the patients’ personal physiological characteristics, given the patient’s changing physical activities or the changing surveillant environment. V. EXPERIMENTAL RESULTS In order to evaluate the performance of the proposed hybrid mobile-cloud solution for ECG signal processing and demonstrate the potential of this approach in addressing future largescale data analysis in personalized telemedicine, we conducted a set of experiments to investigate the efficacy and efficiency of the developed prototype, in terms of CVD detection accuracy and execution efficiency. We chose nine representative patients’ records (#118, #119, #200, #203, #205, #207, #208, #221, and #223) from the MIT-BIH database [28], [29]. Table I presents the composition of the experimental datasets used in our study. About 1/3 of the ECG data in each patient record has been used for training purpose. In order to make an objective comparison of ECG classification accuracy between the stage with untrained/dated ANN (t1 ) and the stage with well-trained ANN (t2 ), we use exactly same testing dataset in both stage t1 and stage t2 . Table I also shows the ECG classification accuracy for the testing dataset based on different ANN configurations (i.e., untrained/dated ANN versus welltrained ANN). The “accuracy” metric is defined as Accuracy(%) =

TP + TN × 100% TP + FN + TN + FP

(1)

where the true positives (TP) indicate correctly classified abnormal beats and the true negatives (TN) indicate correctly classified normal beats. Accordingly, the false positives (FP) represent the normal beats that are classified as abnormal ones and the false negatives (FN) represent the real abnormal beats that are not correctly detected. It is shown that the classification accuracy based on a well-trained ANN classifier is very satisfactory (i.e., over 98% on average) and higher than the one

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TABLE II EXECUTION EFFICIENCY ON THE MOBILE-CLOUD PLATFORM (IN SECONDS)

based on an untrained/dated ANN classifier (i.e., around 65% on average). More interestingly, we can observe that patients #118, #200, and #208 have very low classification accuracy (i.e., 48.16%, 37.30%, and 12.62%), which is caused by the default synaptic weight configuration of the ANN classifier (i.e., randomly generated weight matrix), while remaining six patients have relatively higher accuracy in stage t1 . This scenario clearly demonstrate the impact of a well-calibrated training process on the classification results. All remaining six patients are using an ANN classifier that is well trained based on another patient’s data (i.e., optimized for another patient’s data), named as “dated ANN.” Such dated ANN may give generally correct results since it is able to capture the general characteristics of human population through the training for a specific patient (i.e., #118, #200, or #208); however, it cannot precisely recognize individual-specific characteristics of the current human subject and the performance can be further improved by applying new training processes. The execution efficiency is another critical metric of concern. Table II illustrates the elapsed time for difference stages, including the training time on the cloud (timet 0 ), the training time on the cloud plus the data communication overhead (timet 1 ), and the off-line time (timet c ) used for updating the ANN classifier with a newly trained weight matrix. The results show that the training process on the cloud can be done using a fairly small amount of time (i.e., less than 1 min), which still remains at an acceptable level even considering the data communication overhead (i.e., about 1 min). Since during the off-line time, the ANN classifier on the mobile has to be terminated in order to update the weight matrix, so our goal is to minimize the off-line time to reduce the risk of patients due to lack of monitoring. It is shown that the off-line time has been reasonably maintained at the level of only a few seconds. The most significant impact of the proposed solution is to improve the execution accuracy and efficiency. Through the periodical training, the ANN classifier on the mobile can accurately detect cardiovascular arrhythmia for difference patients, or even for the same patient who have varying ECG conditions due to changing physical behaviors. In terms of execution efficiency, we thus make explicit comparison between the mobile and the cloud. It is worthy to note that we will consider the data communication overhead in the training time needed by the cloud, and compare it against the training time needed on the mobile (no communication overhead), as shown in Table III. The results unsurprisingly highlight the superior advantage of the

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 18, NO. 3, MAY 2014

TABLE III COMPARISON OF EXECUTION EFFICIENCY (I.E., ELAPSED TRAINING TIME) BETWEEN THE MOBILE-CLOUD APPROACH AND THE MOBILE-ONLY APPROACH (IN SECONDS)

TABLE IV COMPARISON OF ENERGY EFFICIENCY (I.E., REDUCED BATTERY LEVEL) BETWEEN THE MOBILE-CLOUD APPROACH AND THE MOBILE-ONLY APPROACH (IN PERCENT)

proposed approach (about 37X speedup) in handling computation- and data-intensive tasks in medical monitoring. This advantage could be further enhanced by optimizing the data communications in our approach. Another far-reaching advantage of the proposed solution is the substantial energy saving. The limited battery life of existing mobile devices has dramatically limited their use in telemedicine applications, which normally demand continuous processing of incoming medical data and cannot afford frequent battery charging. It is manifest that we can achieve energy saving by strategically offloading computation-intensive workloads from the mobile devices to the cloud. However, the intensive data communications between the mobile and the cloud may introduce and impose new energy burdens. Therefore, it is imperative to investigate and evaluate the actual energy consumption level for our proposed approach. Table IV presents the energy efficiency, in terms of the percentage of battery level reduction on the mobile device, for both our proposed approach and the mobile-only approach. It is seen that we can achieve approximately an average of 88% energy saving for each patient record, by using our proposed hybrid mobile-cloud approach. It is worthy to mention that the battery measurement mechanism in Android has the resolution limit of 1% (i.e., the minimum level of battery reduction we can show), so that we can reasonably argue that our proposed approach may have even higher energy saving given the fact that most of their battery reduction readings are 1%. Though the proposed approach has shown significant advantage in energy efficiency, we would like to explore other methods to further reduce the energy consumption, which will be of

TABLE V COMPARISON OF DETECTION ACCURACY AND ENERGY EFFICIENCY (I.E., REDUCED BATTERY LEVEL) BETWEEN RAW ECG DATA AND DOWNSAMPLED ECG DATA ON THE MOBILE-CLOUD PLATFORM

particular interest to scenarios when extended battery life is required or the battery level is low. A common approach of reducing the amount of computation as well as the energy consumption is to downsample the input data stream [37], which however, may unavoidably affect the processing accuracy. In this study, we seek to achieve an optimal balance between the accuracy and execution efficiency, by investigating the system performance based on downsampled data. Table V compares the detection accuracy and energy efficiency between the scheme based on raw ECG data and the one based on half-downsampled ECG data. According to the table, we can only observe a slight degradation in detection accuracy: less than 2% for the well-trained ANN and less than 8% for the untrained ANN. For energy consumption, given the resolution limitation of battery measurement in Android, we aggregated all nine patients’ records and kept track of the overall energy consumption. It is observed that, by downsampling the input data stream, we can achieve a further energy saving of 40% (i.e., energy level reduction from 10% to 6%). Such observation will allow us to pursue high-accuracy, ultralow-power, personalized telemedicine solutions. VI. CONCLUSION Medical monitoring and diagnosis usually involve largescale, knowledge-based data processing and analysis. Recently mobile-based personalized medical monitoring has emerged as a promising solution in providing affordable and effective healthcare services and allowing people to keep track of their own health status in a more convenient manner. However, the limited computational power, storage space, and battery life of mobile devices make it inefficient to use those mobile devices for computation- and data-intensive tasks, which significantly limit the potential and opportunity of mobile-based healthcare. In this study, we propose a new hybrid solution to address the increasing demands for large-scale data processing in personalized healthcare, leveraging the fast-growing mobile and cloud computing technologies. The combination of mobile and cloud computing provides a unique way to synergistically utilize the strengths of each party. Based on a case study of mobilecloud-based medical monitoring, we show the application of the proposed hybrid approach in real healthcare applications. Moreover, the experimental results fully demonstrate the efficacy and efficiency of the proposed approach. Unfortunately, this study

WANG et al.: ENABLING SMART PERSONALIZED HEALTHCARE: A HYBRID MOBILE-CLOUD APPROACH FOR ECG TELEMONITORING

also suffers from some practical issues. For instance, the security is an another key factor of concern for mobile-based healthcare, besides the diagnostic accuracy, computational performance, and energy efficiency. Though our proposed approach shows tremendous advantages and holds the promise of transforming future personalized healthcare, the intensive data communications between the mobile and the cloud may expose users to higher security and privacy threats, which need to be specifically addressed. In our future study, we will precisely profile and characterize the power consumption caused by various data communications and, thus, explore alternative ways to extend the battery life (e.g., optimize the training dataset to be transmitted), while maintaining the desired performance and accuracy. Also, the application of the proposed hybrid mobile-cloud approach in other healthcare areas will also be investigated. REFERENCES [1] WHO, World Health Statistics 2012. France: WHO Press, 2012. [2] G. Anderson, U. Reinhardt, P. Hussey, and V. Petrosyan, “It’s the prices, stupid: Why the United States is so different from other countries,” Health Affairs, vol. 22, no. 3, pp. 89–105, Mar. 2003. [3] U. Varshney, Pervasive Healthcare Computing: EMR/EHR, Wireless and Health Monitoring. New York, NY, USA: Springer-Verlag, 2009. [4] X. Teng and Y.-T. Zhang, “Towards affordable and accessible healthcare systems,” in Career Development in Bioengineering and Biotechnology, vol. 5, ser. Series in Biomedical Engineering. J. H. Nagel, Ed. New York, NY, USA: Springer-Verlag, 2008, pp. 1–5. [5] Pew Research Center, “Nearly half of American adults are smartphone owners,” Washington, DC, USA, Report of Pew Internet & American Life Project, May 2012. [6] Statista, “Smartphone users in the U.S. 2010-2016,” Hamburg, Germany, Statistics Report, 2012. [7] U. Varshney, “Pervasive healthcare,” IEEE Computer, vol. 36, no. 12, pp. 138–140, Dec. 2003. [8] J. Oresko, Z. Jin, J. Cheng, S. Huang, Y. Sun, H. Duschl, and A. C. Cheng, “A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing,” IEEE Trans. Inform. Technol. Biomed., vol. 14, no. 3, pp. 734–740, May 2010. [9] A. Pantelopoulos and N. G. Bourbakis, “A survey on wearable sensorbased systems for health monitoring and prognosis,” IEEE Trans. Syst., Man, Cybern. C: Appl. Rev., vol. 40, no. 1, pp. 1–12, Jan. 2010. [10] X. Wang, Q. Gui, B. Liu, Y. Chen, and Z. Jin, “Leveraging mobile cloud for telemedicine: A performance study in medical monitoring,” in Proc. 39th Northeast Bioeng. Conf., Apr. 2013, pp. 49–50. [11] D. Kovachev, Y. Cao, and R. Klamma, “Mobile cloud computing: A comparison of application models,” Comput. Res. Repository (CoRR), vol. abs/1107.4940, 2011. [12] M. T. Nkosi and F. Mekuria, “Cloud computing for enhanced mobile health applications,” in Proc. Int. Conf. Cloud Comput. Technol. Sci., 2010, pp. 629–633. [13] S. Ahmed and A. Abdullah, “Telemedicine in a cloud—A review,” in Proc. IEEE Symp. Comput. Informat., 2011, pp. 776–781. [14] S. K. Chowdhary, A. Yadav, and N. Garg, “Cloud computing: Future prospect for e-Health,” in Proc. Int. Conf. Electron. Comput. Technol., 2011, vol. 3, pp. 297–299. [15] IBM. (2010). “European Union consortium launches advanced cloud computing project with hospital and smart power grid provider,” [Online]. Available: http://www-03.ibm.com/press/us/en/pressrelease/33067.wss [16] J.-C. Hsieh and M.-W. Hsu, “A cloud computing based 12-lead ECG telemedicine service,” BMC Med. Informat. Decision Making, vol. 12, no. 77, pp. 1–12, 2012. [17] C. Cheng, C. Brown, T. New, T. H. Stokes, C. Dampier, and M. D. Wang, “SickleREMOTE: A two-way text messaging system for pediatric sickle cell disease patients,” in Proc. IEEE-EMBS Int. Conf. Biomed. Health Informat., 2012, pp. 408–411. [18] C. Cheng, T. H. Stokes, and M. D. Wang, “caREMOTE: The design of a cancer reporting and monitoring telemedicine system for domestic care,” in Proc. Int. Conf. IEEE Eng. Med. Biol. Soc., 2011, pp. 3168–3171.

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[19] C.-P. Shen, W.-H. Chen, J.-M. Chen, K.-P. Hsu, J.-W. Lin, M.-J. Chiu, C.-H. Chen, and F. Lai, “Bio-signal analysis system design with support vector machine based on cloud computing service architecture,” in Proc. Int. Conf. IEEE Eng. Med. Biol. Soc., 2010, pp. 1421–1424. [20] Y. A. Alqudah and E. A. AlQaralleh, “A cloud based web analysis and reporting of vital signs,” in Proc. Int. Conf. Digital Info. Process. Commun., 2012, pp. 185–189. [21] S. K. Mouleeswaran, A. Rangaswamy, and H. A. Rauf, “Harnessing and securing cloud in patient health monitoring,” in Proc. Int. Conf. Comput. Commun. Inform., 2012, pp. 1–5. [22] C. O. Rolim, F. L. Koch, C. B. Westphall, J. Werner, A. Fracalossi, and G. S. Salvador, “A cloud computing solution for patient’s data collection in health care institutions,” in Proc. Int. Conf. eHealth, Telemed., Social Med., 2010, pp. 95–99. [23] Z. Jin, Y. Sun, and A. C. Cheng, “Predicting cardiovascular disease from real-time electrocardiographic monitoring: An adaptive machine learning approach on a cell phone,” in Proc. Int. Conf. IEEE Eng. Med. Biol. Soc., 2009, pp. 6889–6892. [24] H. Alemzadeh, Z. Jin, Z. Kalbarczyk, and R. Iyer, “An embedded reconfigurable architecture for patient-specific multiparameter medical monitoring,” in Proc. Int. Conf. IEEE Eng. Med. Biol. Soc., 2011, pp. 1896–1900. [25] J. P. Riley and M. R. Cowie, “Telemonitoring in heart failure,” Heart, vol. 95, no. 23, pp. 1964–1968, 2009. [26] G. B. Smith, D. R. Prytherch, P. E. Schmidt, and P. I. Featherstone, “Review and performance evaluation of aggregate weighted ‘track and trigger’ systems,” Resuscitation, vol. 77, no. 2, pp. 170–179, May 2008. [27] Y. Sun and A. C. Cheng, “Machine learning on-a-chip: A highperformance low-power reusable neuron architecture for artificial neural networks in ECG classifications,” Comput. Biol. Med., vol. 42, no. 7, pp. 751–757, Jul. 2012. [28] G. B. Moody and R. G. Mark, “The impact of the MIT-BIH arrhythmia database,” IEEE Eng. Med. Biol., vol. 20, no. 3, pp. 45–50, May/Jun. 2001. [29] A. Goldberger, L. A. N. Amaral, L. Glass, J .M. Haursdoff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “PhysioBank, physiotoolkit, and PhysioNet: Components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215–e220, Jun. 2000. [30] X. Liu, Y. Zheng, M. W. Phyu, B. Zharo, M. Je, and X. Yuan, “Multiple functional ECG signal is processing for wearable applications of long-term cardiac monitoring,” IEEE Trans. Biomed. Eng., vol. 58, no. 2, pp. 380– 389, Feb. 2011. [31] A. S. Go, D. Mozaffarian, V. L. Roger, E. J. Benjamin, J. D. Berry, W. B. Borden, D. M. Bravata, S. Dai, E. S. Ford, C. S. Fox, S. Franco, H. J. Fullerton, C. Gillespie, S M. Hailpern, J. A. Heit, V. J. Howard, M. D. Huffman, B. M. Kissela, S. J. Kittner, D. T. Lackland, J. H. Lichtman, L. D. Lisabeth, D. Magid, G. M. Marcus, A. Marelli, D. B. Matchar, D. K. McGuire, E. R. Mohler, C. S. Moy, M. E. Mussolino, G. Nichol, N. P. Paynter, P. J. Schreiner, P. D. Sorlie, J. Stein, T. N. Turan, S. S. Virani, N. D. Wong, D. Woo, M. B. Turner, American Heart Association Statistics Committee, and Stroke Statistics Subcommittee, “Heart disease and stroke statistics - 2013 update: A report from the American Heart Association,” Circulation, vol. 127, no. 1, pp. e6–e245, Jan. 2013. [32] Y.-D. Lin and Y. H. Hu, “Power-line interference detection and suppression in ECG signal processing,” IEEE Trans. Biomed. Eng., vol. 55, no. 1, pp. 354–357, Jan. 2008. [33] V. X. Afonso, W. J. Tompkins, T. Q. Nguyen, and S. Luo, “ECG beat detection using filter banks,” IEEE Trans. Biomed. Eng., vol. 46, no. 2, pp. 192–202, Feb. 1990. [34] W. Jiang and S. G. Kong, “Block-based neural networks for personalized ECG signal classification,” IEEE Trans. Neural Netw., vol. 18, no. 6, pp. 1750–1761, Nov. 2007. [35] J. Pan and W. J. Tompkins, “A real-time QRS detection algorithm,” IEEE Trans. Biomed. Eng., vol. 32, no. 3, pp. 230–236, Mar. 1985. [36] R. Ledesma and Z. Jin, “Resiliency analysis and modeling for real-time cardiovascular diagnostic devices,” in Proc. IEEE Signal Process. Med. Biol. Symp., Dec. 2012, pp. 1–6. [37] M. Mitra, J. N. Bera, and R. Gupta, “Electrocardiogram compression technique for global system of mobile-based offline telecardiology application for rural clinics in India,” IET Sci., Meas. Technol., vol. 6, no. 6, pp. 412– 419, 2012.

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Enabling smart personalized healthcare: a hybrid mobile-cloud approach for ECG telemonitoring.

The severe challenges of the skyrocketing healthcare expenditure and the fast aging population highlight the needs for innovative solutions supporting...
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