REWARD: Design, Optimization, and Evaluation of a Real-Time Relative-Energy Wearable R-Peak Detection Algorithm.
Annu Int Conf IEEE Eng Med Biol Soc
; 2019: 3341-3347, 2019 Jul.
Article
en En
| MEDLINE
| ID: mdl-31946597
Wearable 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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Señales Asistido por Computador
/
Electrocardiografía
/
Dispositivos Electrónicos Vestibles
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2019
Tipo del documento:
Article
Pais de publicación:
Estados Unidos