School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.
Sensors (Basel). 2017 Oct 25;17(11):2450. doi: 10.3390/s17112450.
Dynamic accurate heart-rate (HR) estimation using a photoplethysmogram (PPG) during intense physical activities is always challenging due to corruption by motion artifacts (MAs). It is difficult to reconstruct a clean signal and extract HR from contaminated PPG. This paper proposes a robust HR-estimation algorithm framework that uses one-channel PPG and tri-axis acceleration data to reconstruct the PPG and calculate the HR based on features of the PPG and spectral analysis. Firstly, the signal is judged by the presence of MAs. Then, the spectral peaks corresponding to acceleration data are filtered from the periodogram of the PPG when MAs exist. Different signal-processing methods are applied based on the amount of remaining PPG spectral peaks. The main MA-removal algorithm (NFEEMD) includes the repeated single-notch filter and ensemble empirical mode decomposition. Finally, HR calibration is designed to ensure the accuracy of HR tracking. The NFEEMD algorithm was performed on the 23 datasets from the 2015 IEEE Signal Processing Cup Database. The average estimation errors were 1.12 BPM (12 training datasets), 2.63 BPM (10 testing datasets) and 1.87 BPM (all 23 datasets), respectively. The Pearson correlation was 0.992. The experiment results illustrate that the proposed algorithm is not only suitable for HR estimation during continuous activities, like slow running (13 training datasets), but also for intense physical activities with acceleration, like arm exercise (10 testing datasets).
利用光电容积脉搏波（PPG）在剧烈运动期间进行动态精确心率（HR）估计一直具有挑战性，因为它容易受到运动伪影（MA）的干扰。从污染的 PPG 中重建干净的信号并提取 HR 非常困难。本文提出了一种稳健的 HR 估计算法框架，该框架使用单通道 PPG 和三轴加速度数据来重建 PPG，并根据 PPG 的特征和频谱分析来计算 HR。首先，通过存在 MA 来判断信号。然后，当存在 MA 时，从 PPG 的周期图中过滤与加速度数据对应的频谱峰值。根据剩余 PPG 频谱峰值的数量，应用不同的信号处理方法。主要的 MA 去除算法（NFEEMD）包括重复的单陷波滤波器和集合经验模态分解。最后，设计 HR 校准以确保 HR 跟踪的准确性。在 2015 年 IEEE 信号处理杯数据库的 23 个数据集上执行了 NFEEMD 算法。平均估计误差分别为 1.12 BPM（12 个训练数据集）、2.63 BPM（10 个测试数据集）和 1.87 BPM（所有 23 个数据集）。Pearson 相关系数为 0.992。实验结果表明，所提出的算法不仅适用于连续活动（如慢跑（13 个训练数据集））期间的 HR 估计，也适用于具有加速度的剧烈体育活动（如手臂运动（10 个测试数据集））。
