Amelard Robert, Clausi David A, Wong Alexander
University of Waterloo, Department of Systems Design Engineering, 200 University Avenue West, Waterloo N2L 3G1, CanadabSchlegel-University of Waterloo Research Institute for Aging, 250 Laurelwood Drive, Waterloo N2J 0E2, Canada.
University of Waterloo, Department of Systems Design Engineering, 200 University Avenue West, Waterloo N2L 3G1, Canada.
J Biomed Opt. 2016 Nov 1;21(11):116010. doi: 10.1117/1.JBO.21.11.116010.
Photoplethysmographic imaging (PPGI) is a widefield noncontact biophotonic technology able to remotely monitor cardiovascular function over anatomical areas. Although spatial context can provide insight into physiologically relevant sampling locations, existing PPGI systems rely on coarse spatial averaging with no anatomical priors for assessing arterial pulsatility. Here, we developed a continuous probabilistic pulsatility model for importance-weighted blood pulse waveform extraction. Using a data-driven approach, the model was constructed using a 23 participant sample with a large demographic variability (11/12 female/male, age 11 to 60 years, BMI 16.4 to 35.1??kg·m?2). Using time-synchronized ground-truth blood pulse waveforms, spatial correlation priors were computed and projected into a coaligned importance-weighted Cartesian space. A modified Parzen–Rosenblatt kernel density estimation method was used to compute the continuous resolution-agnostic probabilistic pulsatility model. The model identified locations that consistently exhibited pulsatility across the sample. Blood pulse waveform signals extracted with the model exhibited significantly stronger temporal correlation (W=35,p<0.01) and spectral SNR (W=31,p<0.01) compared to uniform spatial averaging. Heart rate estimation was in strong agreement with true heart rate [r2=0.9619, error (?,?)=(0.52,1.69) bpm].
光电容积脉搏波成像（PPGI）是一种宽视野非接触式生物光子技术，能够对解剖区域的心血管功能进行远程监测。尽管空间背景可以为生理相关的采样位置提供见解，但现有的PPGI系统依赖于粗略的空间平均，且在评估动脉搏动性时没有解剖学先验知识。在此，我们开发了一种用于重要性加权血脉搏波形提取的连续概率搏动性模型。采用数据驱动方法，使用具有较大人口统计学变异性的23名参与者样本（11名女性/12名男性，年龄11至60岁，体重指数16.4至35.1 kg·m²）构建该模型。利用时间同步的地面真值血脉搏波形，计算空间相关先验并投影到对齐的重要性加权笛卡尔空间中。使用改进的Parzen–Rosenblatt核密度估计方法来计算连续的分辨率无关概率搏动性模型。该模型识别出样本中持续表现出搏动性的位置。与均匀空间平均相比，用该模型提取的血脉搏波形信号表现出显著更强的时间相关性（W = 35，p < 0.01）和频谱信噪比（W = 31，p < 0.01）。心率估计与真实心率高度一致[r² = 0.9619，误差（均值，标准差）=（0.52，1.69）bpm]。
