IEEE Trans Biomed Eng. 2018 Jan;65(1):189-198. doi: 10.1109/TBME.2017.2697911. Epub 2017 Apr 25.
The photoplethysmographic (PPG) signal is an important source of information for estimating heart rate (HR). However, the PPG signal could be strongly contaminated by the motion artifact of the subjects, making HR estimation a particularly difficult problem. In this paper, we propose PARHELIA, a PARticle filter-based algorithm for HEart rate estimation using photopLethysmographIc signAls. The proposed method employs a particle filter, and utilizes the simultaneously recorded acceleration signals from a wrist-type sensor, to keep track of multiple HR candidates. This achieves quick recovery from incorrect HR estimations under the strong influence of the MA. Experimental results for a dataset of 12 subjects recorded during fast running showed that the average absolute estimation error was 1.17 beats per minute (BPM) whereas that of the best-known conventional method, JOSS, is 1.28 BPM. Furthermore, the estimation time of PARHELIA is 20 times shorter than JOSS.
光电容积脉搏波（PPG）信号是估计心率（HR）的重要信息源。然而，PPG 信号可能会受到受试者运动伪影的强烈干扰，使得 HR 估计成为一个特别困难的问题。在本文中，我们提出了 PARHELIA，这是一种基于粒子滤波器的算法，用于使用光电容积脉搏波信号进行心率估计。该方法采用粒子滤波器，并利用腕式传感器同时记录的加速度信号，跟踪多个 HR 候选值。这在 MA 的强烈影响下，实现了从错误的 HR 估计中快速恢复。对 12 名受试者在快速跑步期间记录的数据集进行的实验结果表明，平均绝对估计误差为 1.17 次/分钟（BPM），而最著名的传统方法 JOSS 的误差为 1.28 BPM。此外，PARHELIA 的估计时间比 JOSS 短 20 倍。
