Zanon Mattia, Kriara Lito, Lipsmeier Florian, Nobbs David, Chatham Christopher, Hipp Joerg, Lindemann Michael
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:706-709. doi: 10.1109/EMBC44109.2020.9175671.
Heart rate variability (HRV) measures the regularity between consecutive heartbeats driven by the balance between the sympathetic and parasympathetic branches of the autonomous nervous system. Wearable devices embedding photoplethysmogram (PPG) technology can be used to derive HRV, creating many opportunities for remote monitoring of this physiological parameter. However, uncontrolled conditions met in daily life pose several challenges related to disturbances that can deteriorate the PPG signal, making the calculation of HRV metrics untrustworthy and not reliable. In this work, we propose a HRV quality metric that is directly related to the HRV accuracy and can be used to distinguish between accurate and inaccurate HRV values. A parametric supervised approach estimates HRV accuracy using a model whose inputs are features extracted from the PPG signal and the output is the HRV error between HRV metrics obtained from the PPG and the ECG collected during an experimental protocol involving several activities. The estimated HRV accuracy of the model is used as an indication of the HRV quality.
心率变异性（HRV）测量的是由自主神经系统交感神经和副交感神经分支之间的平衡所驱动的连续心跳之间的规律性。嵌入光电容积脉搏波描记法（PPG）技术的可穿戴设备可用于获取HRV，为远程监测这一生理参数创造了诸多机会。然而，日常生活中遇到的不受控制的状况带来了一些与干扰相关的挑战，这些干扰会使PPG信号恶化，导致HRV指标的计算不可信且不可靠。在这项工作中，我们提出了一种与HRV准确性直接相关的HRV质量指标，可用于区分准确和不准确的HRV值。一种参数化监督方法使用一个模型来估计HRV准确性，该模型的输入是从PPG信号中提取的特征，输出是在涉及多项活动的实验方案期间从PPG获得的HRV指标与收集的心电图之间的HRV误差。该模型估计的HRV准确性用作HRV质量的指标。
