FPGA based two-channel ECG sensor node for wearable applications

Mihel, J.; Magjarević, Ratko

doi:10.1007/978-3-540-89208-3_289

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…In addition, traditional medical applications such as EMG measurements, ECG analysis, and glucose monitoring were implemented using BSNs [14,22,23]. With these developments, unique aspects of BSNs such as physiological signal processing, need for wearable form factor and interfacing with medical sensors, were identified, which differentiated them from generic WSNs.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of body sensor network platforms

Nabar

Banerjee

Gupta

et al. 2010

Wireless Health 2010

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Body Sensor Networks (BSNs) consist of sensor nodes deployed on the human body for health monitoring. Each sensor node is implemented by interfacing a physiological sensor with a sensor platform consisting of components such as microcontroller, radio and memory. Diverse needs of BSN applications require customized platform development for optimizing performance. In this paper, we propose a two-phase framework to evaluate the performance of sensor platforms to match a BSN's computation, communication and sensing requirements: 1) Design Space Determination, wherein we investigate salient features of BSN platforms and quantify them as design coordinates through evaluation metrics such as SPSW (Samples Processed per Second per Watt) and EPC (Expected Power Consumption). To measure these metrics for a platform under typical BSN application workloads, we propose BSN-Bench, a benchmarking suite composed of basic tasks that occur in diverse BSN applications. BSNBench enables an accurate profiling of platforms based on the design coordinates ; 2) Design Space Exploration, wherein we explore the design space to find the most suitable platform for a given application. We demonstrate the usage of our framework through a case study, where we consider two practical BSN applications and choose suitable platforms for them.

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Section: Related Workmentioning

confidence: 99%

Evaluation of body sensor network platforms

Nabar

Banerjee

Gupta

et al. 2010

Wireless Health 2010

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“…A statistical approach for determination of time plane features from digitized ECG has been addressed in [8]. [9] presents an FPGA based two channel ECG sensor node design for wearable applications. The node was designed and implemented using two instrumentation amplifiers, two fourth order Sallen-Key filters, a two channel A/D converter, an Actel's Flash based FPGA and additional communication circuitry.…”

Section: Introductionmentioning

confidence: 99%

Real time electrocardiogram wave peak detection algorithm and its implementation on FPGA

Chatterjee¹,

Gupta

Mitra

2014

Proceedings of the 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC)

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A real time Electrocardiogram wave peak detection algorithm with low latency has been developed. It can detect different component wave peaks, viz., P, Q, R, S and T from digitized samples. The algorithm has been implemented using Xilinx Spartan-III xc3s400tq144-5 FPGA module. For the purpose of detection of ECG wave peaks, during a training period of 6 second, a slope and polarity based signature is learned from the incoming ECG signal, which is sampled at 1 kHz frequency. These detected signatures of individual wave peaks of the concerned are then used for detection of subsequent individual component wave peaks. R, T and P waves are detected with 98.8%, 97.98% and 97.75% respectively using ptbdb and mitdb data from Physionet. The design consumed 35% resources of FPGA.

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FPGA based two-channel ECG sensor node for wearable applications

Cited by 2 publications

References 6 publications

Evaluation of body sensor network platforms

Evaluation of body sensor network platforms

Real time electrocardiogram wave peak detection algorithm and its implementation on FPGA

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