Orlandic Lara, Giovanni Elisabetta de, Arza Adriana, Yazdani Sasan, Vesin Jean-Marc, Atienza David
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:3341-3347. doi: 10.1109/EMBC.2019.8857226.
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.
可穿戴设备是对慢性心血管疾病进行连续动态监测的一种不引人注意且经济高效的手段。然而，在这些资源受限的系统上，心电图（ECG）处理算法必须消耗最少的功率和内存，同时还要稳健地提供准确的生理信息。这项工作提出了REWARD，即基于相对能量的可穿戴R波检测算法，它是一种基于名为相对能量（Rel-En）的非线性滤波方法的新型ECG R波检测机制。REWARD专为在可穿戴系统上实时执行而设计和优化。然后，将这种新颖的算法与三种最先进的实时R波检测算法在准确性、内存占用和能耗方面进行比较。分别使用Physionet QT和NST数据库来评估算法的准确性和对噪声的鲁棒性。然后，使用基于32位ARM Cortex-M3的微控制器来测量这四种算法的能量使用情况、计算负担和内存占用。与其他算法相比，REWARD消耗的能量至少少63%，RAM少32%，同时获得了相当的准确性结果。因此，对于执行更复杂ECG分析的可穿戴设备而言，REWARD将是一种合适的R波检测机制选择，因为其算法需要额外的能量和内存资源。
