Zhang Zhilin, Pi Zhouyue, Liu Benyuan
IEEE Trans Biomed Eng. 2015 Feb;62(2):522-31. doi: 10.1109/TBME.2014.2359372. Epub 2014 Sep 19.
Heart rate monitoring using wrist-type photoplethysmographic signals during subjects' intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects' hand movements. So far few works have studied this problem. In this study, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/h showed that the average absolute error of heart rate estimation was 2.34 beat per minute, and the Pearson correlation between the estimates and the ground truth of heart rate was 0.992. This framework is of great values to wearable devices such as smartwatches which use PPG signals to monitor heart rate for fitness.
在受试者进行高强度运动期间，利用腕部光电容积脉搏波信号监测心率是一个难题，因为这些信号会被受试者手部运动所产生的极强运动伪迹干扰。到目前为止，很少有研究涉及这个问题。在本研究中，我们提出了一个通用框架，称为TROIKA，它由用于去噪的信号分解、用于高分辨率频谱估计的稀疏信号重构以及带验证的频谱峰值跟踪组成。TROIKA框架具有较高的估计精度，并且对强运动伪迹具有鲁棒性。从这个框架可以直接衍生出许多变体。对12名受试者在以15公里/小时的峰值速度快速跑步期间记录的数据集进行的实验结果表明，心率估计的平均绝对误差为每分钟2.34次心跳，估计值与心率真实值之间的皮尔逊相关系数为0.992。该框架对于诸如使用PPG信号监测心率以用于健身的智能手表等可穿戴设备具有重要价值。
