000265401 001__ 265401
000265401 005__ 20190617200542.0
000265401 037__ $$aPOST_TALK
000265401 245__ $$aREWARD: Design, Optimization, and Evaluation of a Real-Time Relative-Energy Wearable R-Peak Detection Algorithm
000265401 260__ $$c2019-05-01
000265401 269__ $$a2019-05-01
000265401 300__ $$a7 p.
000265401 336__ $$aTalks
000265401 520__ $$aWearable devices are an unobtrusive, cost-effective means of continuous ambulatory monitoring of chronic cardiovascular diseases. However, on these resource-constrained systems, electrocardiogram (ECG) processing algorithms must consume minimal power and memory, yet robustly provide accurate physiological information. This work presents REWARD, the Relative-Energy-based WeArable R-Peak Detection algorithm, which is a novel ECG R-peak detection mechanism based on a nonlinear filtering method called Relative-Energy (Rel-En). REWARD is designed and optimized for real-time execution on wearable systems. Then, this novel algorithm is compared against three state-of-the-art real-time R-peak detection algorithms in terms of accuracy, memory footprint, and energy consumption. The Physionet QT and NST Databases were employed to evaluate the algorithms’ accuracy and robustness to noise, respectively. Then, a 32-bit ARM Cortex-M3-based microcontroller was used to measure the energy usage, computational burden, and memory footprint of the four algorithms. REWARD consumed at least 63% less energy and 32% less RAM than the other algorithms while obtaining comparable accuracy results. Therefore, REWARD would be a suitable choice of R-peak detection mechanism for wearable devices that perform more complex ECG analysis, whose algorithms require additional energy and memory resources.
000265401 6531_ $$aWearable devices
000265401 6531_ $$aResource-constrained embedded systems
000265401 6531_ $$aECG
000265401 6531_ $$areal-time R-peak detection
000265401 6531_ $$aUltra-low power devices
000265401 700__ $$aOrlandic, Lara
000265401 700__ $$0250072$$aDe Giovanni, Elisabetta$$g264566
000265401 700__ $$0251510$$aArza Valdes, Adriana$$g261121
000265401 700__ $$0247673$$aYazdani, Sasan$$g233621
000265401 700__ $$0240458$$aVesin, Jean-Marc$$g106643
000265401 700__ $$0240268$$aAtienza Alonso, David$$g169199
000265401 7112_ $$dJuly 23-26, 2019$$cBerlin, Germany$$a41st International Engineering in Medicine and Biology Conference - EMBC 2019
000265401 8560_ $$falessandra.bianchi@epfl.ch
000265401 85641 $$yConference Programme$$uhttps://embc.embs.org/2019/
000265401 8564_ $$uhttps://infoscience.epfl.ch/record/265401/files/REWARD_paper.pdf$$zPREPRINT$$s506251
000265401 909C0 $$pESL$$mhomeira.salimi@epfl.ch$$mdavid.atienza@epfl.ch$$0252050$$zMarselli, Béatrice$$xU11977
000265401 909CO $$qGLOBAL_SET$$pSTI$$ppresentation$$ooai:infoscience.epfl.ch:265401
000265401 960__ $$aadriana.arza@epfl.ch
000265401 961__ $$aalessandra.bianchi@epfl.ch
000265401 973__ $$aEPFL$$rREVIEWED
000265401 980__ $$aPOST_TALK
000265401 981__ $$aoverwrite