Zhu Shilin, Tan Ke, Zhang Xinyu, Liu Zhiqiang, Liu Bin
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:2347-50. doi: 10.1109/EMBC.2015.7318864.
The performance of heart rate (HR) monitoring using wrist-type photoplethysmographic (PPG) signals is strongly influenced by motion artifacts (MAs), since the intensive physical exercises are common in real world. Few works focus on this study so far because of unsatisfying quality of corrupted PPG signals. In this paper, we propose an accurate and efficient strategy, named MICROST, which estimates heart rate based on a mixed approach. The MICROST framework is designed as a MIxed algorithm which consists of acceleration Classification (AC), fiRst-frame prOcessing and heuriStic Tracking. Experimental results using recordings from 12 subjects during fast running and intensive movement showed the average absolute error of heart rate estimation was 2.58 beat per minute (BPM), and the Pearson correlation between the estimates and the ground-truth of heart rate was 0.988. We discuss our approach in real time to face the applications of wearable devices such as smart-watches in reality.
由于在现实世界中剧烈的体育锻炼很常见，因此使用腕式光电容积脉搏波描记法（PPG）信号进行心率（HR）监测的性能会受到运动伪影（MA）的强烈影响。到目前为止，由于被损坏的PPG信号质量不尽人意，很少有研究关注这项研究。在本文中，我们提出了一种准确而有效的策略，称为MICROST，它基于混合方法估计心率。MICROST框架被设计为一种混合算法，由加速度分类（AC）、第一帧处理和启发式跟踪组成。使用12名受试者在快速跑步和剧烈运动期间的记录进行的实验结果表明，心率估计的平均绝对误差为每分钟2.58次心跳（BPM），估计值与心率真实值之间的皮尔逊相关性为0.988。我们实时讨论我们的方法，以应对可穿戴设备（如智能手表）在现实中的应用。
