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Detail-preserving pulse wave extraction from facial videos using consumer-level camera

机译:使用消费者级别的相机从面部视频中保留细节的脉搏波提取

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摘要

With the popularity of smart phones, non-contact video-based vital sign monitoring using a camera has gained increased attention over recent years. Especially, imaging photoplethysmography (IPPG), a technique for extracting pulse waves from videos, conduces to monitor physiological information on a daily basis, including heart rate, respiration rate, blood oxygen saturation, and so on. The main challenge for accurate pulse wave extraction from facial videos is that the facial color intensity change due to cardiovascular activities is subtle and is often badly disturbed by noise, such as illumination variation, facial expression changes, and head movements. Even a tiny interference could bring a big obstacle for pulse wave extraction and reduce the accuracy of the calculated vital signs. In recent years, many novel approaches have been proposed to eliminate noise such as filter banks, adaptive filters, Distance-PPG, and machine learning, but these methods mainly focus on heart rate detection and neglect the retention of useful details of pulse wave. For example, the pulse wave extracted by the filter bank method has no dicrotic wave and approaching sine wave, but dicrotic waves are essential for calculating vital signs like blood viscosity and blood pressure. Therefore, a new framework is proposed to achieve accurate pulse wave extraction that contains mainly two steps: 1) preprocessing procedure to remove baseline offset and high frequency random noise; and 2) a self-adaptive singular spectrum analysis algorithm to obtain cyclical components and remove aperiodic irregular noise. Experimental results show that the proposed method can extract detail-preserved pulse waves from facial videos under realistic situations and outperforms state-of-the-art methods in terms of detail-preserving and real time heart rate estimation. Furthermore, the pulse wave extracted by our approach enabled the non-contact estimation of atrial fibrillation, heart rate variability, blood pressure, as well as other physiological indices that require standard pulse wave.
机译:随着智能电话的普及,近年来使用摄像头的基于非接触视频的生命体征监视越来越受到关注。特别地,成像光电容积描记术(IPPG)是一种从视频中提取脉搏波的技术,它每天都有助于监测生理信息,包括心率,呼吸频率,血氧饱和度等。从面部视频中准确提取脉搏波的主要挑战在于,由于心血管活动而导致的面部颜色强度变化非常微妙,并且通常会受到噪声(例如光照变化,面部表情变化和头部运动)的严重干扰。即使是很小的干扰也可能给脉搏波提取带来很大障碍,并降低所计算生命体征的准确性。近年来,已提出了许多消除噪声的新颖方法,例如滤波器组,自适应滤波器,距离PPG和机器学习,但是这些方法主要集中在心率检测上,而忽略了保留脉搏波的有用细节。例如,通过滤波器组方法提取的脉搏波没有重搏波且接近正弦波,但是重搏波对于计算诸如血液粘度和血压之类的生命体征至关重要。因此,提出了一种新的框架来实现精确的脉搏波提取,主要包括两个步骤:1)去除基线偏移和高频随机噪声的预处​​理程序; 2)自适应奇异频谱分析算法,获取循环分量并消除非周期性的不规则噪声。实验结果表明,该方法可以在真实情况下从面部视频中提取细节保留的脉搏波,在细节保留和实时心率估计方面优于最新方法。此外,通过我们的方法提取的脉搏波可以对房颤,心率变异性,血压以及需要标准脉搏波的其他生理指标进行非接触式估计。

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