Combination of Body Sensor Networks and On-Body Signal Processing Algorithms: the practical case of MyHeart project

Luprano, Jean; Solà, Josep; Dasen, S.; Koller, Júlia; Chételat, Olivier

doi:10.1109/bsn.2006.15

Cited by 66 publications

(33 citation statements)

References 8 publications

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“…From our study, we found that the majority of which capture, process and send ECG data to specialists in order to remotely follow their patients, but they generally don't detect cardiac anomalies within the collected data or achieves a basic ECG analysis like heart rate variability. This is the main limitation of these systems like CodeBlue [5], LiveNet [6], MyHeart [7], MEDISN [8] or industrial products like AliveCor [9], PhysioMem [10] or LifeMonitor [11].…”

Section: Related Workmentioning

confidence: 99%

An ECG monitoring system for prediction of cardiac anomalies using WBAN

Hadjem

Salem

Naït‐Abdesselam

2014

2014 IEEE 16th International Conference on E-Health Networking, Applications and Services (Healthcom)

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Abstract-Cardiovascular diseases (CVD) are known to be the most widespread causes to death. Therefore, detecting earlier signs of cardiac anomalies is of prominent importance to ease the treatment of any cardiac complication or take appropriate actions. Electrocardiogram (ECG) is used by doctors as an important diagnosis tool and in most cases, it's recorded and analyzed at hospital after the appearance of first symptoms or recorded by patients using a device named holter ECG and analyzed afterward by doctors. In fact, there is a lack of systems able to capture ECG and analyze it remotely before the onset of severe symptoms. With the development of wearable sensor devices having wireless transmission capabilities, there is a need to develop real time systems able to accurately analyze ECG and detect cardiac abnormalities. In this paper, we propose a new CVD detection system using Wireless Body Area Networks (WBAN) technology. This system processes the captured ECG using filtering and Undecimated Wavelet Transform (UWT) techniques to remove noises and extract nine main ECG diagnosis parameters, then the system uses a Bayesian Network Classifier model to classify ECG based on its parameters into four different classes: Normal, Premature Atrial Contraction (PAC), Premature Ventricular Contraction (PVC) and Myocardial Infarction (MI). The experimental results on ECGs from real patients databases show that the average detection rate (TPR) is 96.1% for an average false alarm rate (FPR) of 1.3%.

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Section: Related Workmentioning

confidence: 99%

An ECG monitoring system for prediction of cardiac anomalies using WBAN

Hadjem

Salem

Naït‐Abdesselam

2014

2014 IEEE 16th International Conference on E-Health Networking, Applications and Services (Healthcom)

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“…On-body signal processing is performed to estimate HR from textile-ECG and continuously classify ambulatory activity (resting, lying, walking, running, and going up/down stairs) based on a 1-axis accelerometer within the on-body electronics. Bluetooth wireless communication is also established between the on-body electronics and a cell phone, which is then used to forward the processed signals to a remote monitoring station (Luprano et al, 2006). SmartVest, a wearable physiological monitoring system, consists of a vest, data acquisition and processing hardware, and a remote monitoring station.…”

Section: Fig 2 Vtamn Garment With the Beltmentioning

confidence: 99%

Health Care with Wellness Wear

Kim¹,

Meng²,

Chung³

2011

Health Management - Different Approaches and Solutions

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“…A sensor network that senses health parameters becomes a body sensor network (BSN) [2][3][4][5]. Nodes of BSNs are directly attached to human body, so special care must be exercised.…”

Section: Introductionmentioning

confidence: 99%

Mobile Solution for Three-Tier Biofeedback Data Acquisition and Processing

Pereira

Neves

Rodrigues

2008

IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference

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Abstract-Wireless sensor networks span from military applications into everyday life. Body sensor networks greatly benefit from wireless sensor networks to answer the biofeedback challenges in healthcare applications. In such applications, data is of fundamental importance, it must be reliable and within easy reach. However, most solutions rely on a personal computer to process and display sensor data. In this paper we propose a mobile solution that draws on three-tier body sensor networks to dramatically improve data accessibility, through the use of a Java and Bluetooth-enabled mobile phone. The mobile tool features data monitoring and presentation. This approach allows data visualization by the patient or medical staff without a portable computer or specific monitoring hardware. We hope to contribute to the adoption of biofeedback for early detection of health abnormalities and lower the budget that governments spend each year in healthcare.

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Combination of Body Sensor Networks and On-Body Signal Processing Algorithms: the practical case of MyHeart project

Cited by 66 publications

References 8 publications

An ECG monitoring system for prediction of cardiac anomalies using WBAN

An ECG monitoring system for prediction of cardiac anomalies using WBAN

Health Care with Wellness Wear

Mobile Solution for Three-Tier Biofeedback Data Acquisition and Processing

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