IEEE J Biomed Health Inform. 2019 Nov;23(6):2398-2408. doi: 10.1109/JBHI.2018.2880097. Epub 2018 Nov 7.
Unobtrusive monitoring of vital signs is relevant for both medical (patient monitoring) and non-medical applications (e.g., stress and fatigue monitoring). In this paper, we focus on the use of imaging photoplethysmography (iPPG). High frame rate videos were acquired by using a monochrome camera and an optical band-pass filter ([Formula: see text] nm). To enhance iPPG signal, we investigated the use of independent component analysis (ICA) pre-processing applied to iPPG signal from different regions of the face. Methodology was tested on [Formula: see text] healthy volunteers. Heart rate (HR) and standard time and frequency domain descriptors of heart rate variability (HRV), simultaneously extracted from videos and ECG data, were compared. A mean absolute error (MAE) about 3.812 ms was observed for normal-to-normal intervals with or without ICA pre-processing. Smaller MAE values of frequency domain descriptors were observed when ICA pre-processing was used. The impact of both video frame rate and video signal interval were also analyzed. All the results support the conclusion that proposed ICA pre-processing can effectively improve the HR and HRV assessment from iPPG.
非侵入式生命体征监测在医疗（患者监测）和非医疗应用（例如，压力和疲劳监测）中都很重要。在本文中，我们专注于使用成像光体积描记法（iPPG）。通过使用单色相机和光学带通滤波器（[Formula: see text]nm）来获取高帧率视频。为了增强 iPPG 信号，我们研究了将独立成分分析（ICA）预处理应用于面部不同区域的 iPPG 信号。该方法在[Formula: see text]名健康志愿者身上进行了测试。从视频和 ECG 数据中同时提取心率（HR）和心率变异性（HRV）的标准时间和频域描述符，并进行了比较。在使用或不使用 ICA 预处理的情况下，正常到正常间隔的平均绝对误差（MAE）约为 3.812ms。当使用 ICA 预处理时，频域描述符的 MAE 值更小。还分析了视频帧率和视频信号间隔的影响。所有结果都支持这样的结论，即所提出的 ICA 预处理可以有效地提高从 iPPG 评估心率和心率变异性的效果。
