Heart Rate Monitoring During Physical Exercise From Photoplethysmography Using Neural Network
Heart Rate Monitoring During Physical Exercise From Photoplethysmography Using Neural Network
复制标题
DOI：
10.1109/lsens.2018.2878207
复制
发表时间：
2019
影响因子：
2.8
通讯作者：
Lianning Zhu;Chen Kan;Yuncheng Du;D. Du
中科院分区：
文献类型：
--
作者：
Lianning Zhu;Chen Kan;Yuncheng Du;D. Du
光电体积描记（PPG）信号已经广泛用于心率（HR）监测。与心电图相比，PPG信号可以用智能手表等可穿戴设备轻松采集，成本更低。然而，PPG信号经常被运动伪影（MA）和噪声污染，这极大地恶化了信号质量，并对HR监测提出了重大挑战。在本文中，一种新的算法，使用谱减法和神经网络（NN），在MA和噪声的存在下，精确的HR跟踪开发。具体地，从加速度（ACC）信号估计MA的谱分量，然后从PPG的谱中去除。此外，神经网络模型开发的基础上提取的ACC信号的新功能，以确定ACC和HR的变化之间的关系，在连续的时间窗口。这些信息被进一步用作选择对应于实际HR的谱峰的参考。后处理算法被用于校正误识别的HR并提高准确性。基于NN的算法使用2015 IEEE信号处理杯数据集进行验证。我们的算法实现了1.03每分钟（BPM）的平均绝对误差（标准差：1.82 BPM），这优于文献中以前报道的作品。
Photoplethysmography (PPG) signals have been widely used for heart rate (HR) monitoring. Compared to the electrocardiogram, PPG signals can be easily collected with wearable devices such as smart watches at a lower cost. However, the PPG signals are often contaminated by the motion artifact (MA) and noises, which greatly deteriorate the signal quality and pose significant challenges on HR monitoring. In this article, a new algorithm, using the spectral subtraction and the neural network (NN), is developed for accurate HR tracking in the presence of MA and noises. Specifically, the spectral component of MA is estimated from the acceleration (ACC) signals and then removed from the spectra of PPG. In addition, an NN model is developed based on new features extracted from ACC signals to identify the relationship between the ACC and HR variations in consecutive time windows. Such information is further used as a reference to select the spectral peak corresponding to the actual HR. A postprocessing algorithm is used to correct misidentified HR and to improve the accuracy. The NN-based algorithm is validated using the 2015 IEEE Signal Processing Cup Dataset. Our algorithm achieves an average absolute error of 1.03 beats per minutes (BPM) (standard deviation: 1.82 BPM), which outperforms previously reported works in the literature.