De Giovanni Elisabetta, Teijeiro Tomas, Millet Gregoire P, Atienza David
IEEE Trans Biomed Eng. 2023 Mar;70(3):941-953. doi: 10.1109/TBME.2022.3205304. Epub 2023 Feb 17.
Continuous monitoring of biosignals via wearable sensors has quickly expanded in the medical and wellness fields. At rest, automatic detection of vital parameters is generally accurate. However, in conditions such as high-intensity exercise, sudden physiological changes occur to the signals, compromising the robustness of standard algorithms.
Our method, called BayeSlope, is based on unsupervised learning, Bayesian filtering, and non-linear normalization to enhance and correctly detect the R peaks according to their expected positions in the ECG. Furthermore, as BayeSlope is computationally heavy and can drain the device battery quickly, we propose an online design that adapts its robustness to sudden physiological changes, and its complexity to the heterogeneous resources of modern embedded platforms. This method combines BayeSlope with a lightweight algorithm, executed in cores with different capabilities, to reduce the energy consumption while preserving the accuracy.
BayeSlope achieves an F1 score of 99.3% in experiments during intense cycling exercise with 20 subjects. Additionally, the online adaptive process achieves an F1 score of 99% across five different exercise intensities, with a total energy consumption of 1.55±0.54 mJ.
We propose a highly accurate and robust method, and a complete energy-efficient implementation in a modern ultra-low-power embedded platform to improve R peak detection in challenging conditions, such as during high-intensity exercise.
The experiments show that BayeSlope outperforms state-of-the-art QRS detectors up to 8.4% in F1 score, while our online adaptive method can reach energy savings up to 38.7% on modern heterogeneous wearable platforms.
通过可穿戴传感器对生物信号进行持续监测在医疗和健康领域迅速发展。在静息状态下，生命体征参数的自动检测通常较为准确。然而，在高强度运动等情况下，信号会发生突然的生理变化，影响标准算法的稳健性。
我们的方法称为BayeSlope，基于无监督学习、贝叶斯滤波和非线性归一化，以根据心电图中R波的预期位置增强并正确检测R波。此外，由于BayeSlope计算量较大且会迅速耗尽设备电池电量，我们提出一种在线设计，使其在适应突然的生理变化时保持稳健性，并使其复杂度与现代嵌入式平台的异构资源相匹配。该方法将BayeSlope与一种轻量级算法相结合，在具有不同能力的内核中执行，以在保持准确性的同时降低能耗。
在对20名受试者进行的高强度自行车运动实验中，BayeSlope的F1分数达到99.3%。此外，在线自适应过程在五种不同运动强度下的F1分数达到99%，总能耗为1.55±0.54毫焦。
我们提出了一种高精度且稳健的方法，以及在现代超低功耗嵌入式平台上的完整节能实现方案，以改善在具有挑战性的条件下（如高强度运动期间）的R波检测。
实验表明，BayeSlope在F1分数方面比现有最先进的QRS检测器高出8.4%，而我们的在线自适应方法在现代异构可穿戴平台上可实现高达38.7%的节能。
