Video heart rate measurements based on adaptive region of interest

Sen-lu CHEN; Yu-liang LIU; Tuan-wei XU

doi:10.37188/OPE.2020.0699

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Video heart rate measurements based on adaptive region of interest

Information Sciences | 更新时间：2021-08-09

- Video heart rate measurements based on adaptive region of interest
- Optics and Precision Engineering Vol. 29, Issue 7, Pages: 1740-1749(2021)
- 作者机构：
  
  1.中国科学院半导体研究所光电系统实验室，北京 100083
  2.中国科学院大学材料与光电研发中心，北京 100049
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.2020.0699
  CLC：
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Sen-lu CHEN, Yu-liang LIU, Tuan-wei XU. Video heart rate measurements based on adaptive region of interest. [J]. Optics and Precision Engineering 29(7):1740-1749(2021)
DOI：

Sen-lu CHEN, Yu-liang LIU, Tuan-wei XU. Video heart rate measurements based on adaptive region of interest. [J]. Optics and Precision Engineering 29(7):1740-1749(2021) DOI： 10.37188/OPE.2020.0699.

摘要

基于视频的非接触光电容积脉搏波（Photoplethysmography， PPG）可以实现非接触式心率监测。为改善非接触PPG信号质量和提高非接触PPG技术检测心率的准确性，提出一种自适应感兴趣区域（Region of Interest， ROI）的方法。使用独立向量分析对人脸分区域处理，然后使用归一化分割选取信噪比和相关度最高的小区块作为自适应ROI来获取心率，通过对自适应ROI加权平均和频域处理得到非接触PPG信号。相比于预选定ROI的方法，该方法将头部静止状态下心率误差的均值和标准差从（4.72±6.46）次/分降低至（0.52±1.49）次/分，根均方误差（Root Mean Square Error， RMSE）从7.96次/分降低至1.50 次/分，平均误差率从9.45%降低至1.73%。头部运动状态下该方法的误差为（1.02±2.91）次/分，RMSE为2.11 次/分，误差降低50%以上。使用Bland-Altman及相关性分析比较该方法与使用接触式PPG仪器得到的心率，计算得到头部静止时95%置信区间为,-,2.44~3.48 次/分，运动时为,-,2.76~4.79 次/分。最后通过对比与接触式PPG信号的波形，证明该方法得到了细节完整的PPG信号。实验结果表明，该方法显著提升了PPG信号的质量与心率的准确率。

Abstract

Video-based non-contact photoplethysmography （PPG） can achieve non-contact heart rate monitoring. To improve the quality of non-contact PPG signal and the accuracy of non-contact heart rate， a novel method based on adaptive region of interest （ROI） was studied. First， independent vector analysis （IVA） was used to process the face by blocks. Then normalized cuts （Ncuts） was used to select blocks with the highest signal-to-noise ratio and correlation as adaptive ROI to obtain the heart rate. Finally， the non-contact PPG signal was obtained by weighted average of the adaptive ROI and frequency domain processing. Compared with pre-selecting ROI method， the mean and standard deviation of the heart rate error was reduced from （4.72±6.46） beats per minute （bpm） to （0.52±1.49） bpm， the root mean square error （RMSE） was reduced from 7.96bpm to 1.50bpm， and the average error rate was reduced from 9.45% to 1.73% in static situation. The error of this method under head movement was （1.02±2.91） bpm， the RMSE was 2.11 bpm， which was reduced by above 50%. Bland-Altman and correlation analysis was used to compare the heart rate obtained by this method and using a contact PPG instrument， the 95% confidence interval was ,-,2.44~3.48 bpm at static and ,-,2.76~4.79 bpm at move situation. Compared with the waveform of the contact PPG signal， it was showed the method obtains the PPG signal with complete details. Experimental results showed this method has significant advantages in the quality of PPG signal and the accuracy of heart rate.

关键词

光电容积脉搏波非接触心率自适应感兴趣区域独立向量分析归一化分割信噪比

Keywords

photoplethysmographynon-contact heart rateadaptive region of interestindependent vector analysisnormalized cutssignal to noise ratio

references

KAZEMI S， GHORBANI A， AMINDAYAR H， et al. Cyclostationary approach to Doppler radar heart and respiration rates monitoring with body motion cancelation using Radar Doppler System［J］. Biomedical Signal Processing and Control， 2014， 13： 79-88.

ALLEN J. Photoplethysmography and its application in clinical physiological measurement［J］. Physiological Measurement， 2007， 28（3）： R1-R39.

VERKRUYSSE W， SVAASAND L O， STUART NELSON J. Remote plethysmographic imaging using ambient light［J］. Optics Express， 2008， 16（26）： 21434-21445.

POH M Z， MCDUFF D J， PICARD R W. Non-contact， automated cardiac pulse measurements using video imaging and blind source separation［J］. Optics Express， 2010， 18（10）： 10762-10774.

WEI B， HE X， ZHANG C， et al. Non-contact， synchronous dynamic measurement of respiratory rate and heart rate based on dual sensitive regions［J］. BioMedical Engineering OnLine， 2017， 16（1）： 1-21.

WEI J， LUO H， WU S J， et al. Transdermal optical imaging reveal basal stress via heart rate variability analysis： a novel methodology comparable to electrocardiography［J］. Frontiers in Psychology， 2018， 9： 98.

MACWAN R， BENEZETH Y， MANSOURI A. Heart rate estimation using remote photoplethysmography with multi-objective optimization［J］. Biomedical Signal Processing and Control， 2019， 49： 24-33.

QI H， GUO ZH， CHEN X， et al. Video-based human heart rate measurement using joint blind source separation［J］. Biomedical Signal Processing and Control， 2017， 31： 309-320.

ROUAST P V， ADAM M T P， CHIONG R， et al. Remote heart rate measurement using low-cost RGB face video： a technical literature review［J］. Frontiers of Computer Science， 2018， 12（5）： 858-872.

JAIN M， DEB S， SUBRAMANYAM A V. Face Video Based Touchless Blood Pressure and Heart Rate Estimation［C］. 2016 IEEE 18th International Workshop on Multimedia Signal Processing， Montreal， QC， Canada： MMSP， 2016： 1-5.

HASSAN M A， MALIK A S， FOFI D， et al. Heart rate estimation using facial video： A review［J］. Biomedical Signal Processing and Control， 2017， 38： 346-360.

TULYAKOV S， ALAMEDA-PINEDA X， RICCI E， et al. Self-Adaptive Matrix Completion for Heart Rate Estimation from Face Videos under Realistic Conditions［C］. 2016 IEEE Conference on Computer Vision and Pattern Recognition， Las Vegas， USA： CVPR， 2016： 2396-2404.

王蒙军，王楠，王霞，等. 由可见光谱视频中提取心率的对角累积量法［J］. 光学精密工程， 2015， 23（9）： 2698-2707.

WANG M J， WANG N， WANG X， et al. Extracting heart rate from visible spectrum video based on diagonal cumulant algorithm［J］. Opt. Precision Eng.， 2015， 23（9）： 2698-2707.（in Chinese）

李晓媛，武鹏，刘允，等. 基于人脸视频的心率参数提取［J］. 光学精密工程， 2020， 28（3）： 548-557.

LI X Y， WU P， LIU Y， et al. Extraction of heart rate parameters from video of human face［J］. Opt. Precision Eng.， 2020， 28（3）： 548-557.（in Chinese）

ANDERSON M， LI X L， ADALı T. Nonorthogonal independent vector analysis using multivariate Gaussian model［C］. ent Variable Analysis and Signal Separation， St. Malo， France， 2010， 6365： 354-361.

WANG W， BRINKER D B， STUIJK S， et al. Algorithmic Principles of Remote PPG［J］. IEEE Transactions on Biomedical Engineering. 2017， 64（7）：1479-1491

JIANBO S， MALIK J. Normalized cuts and image segmentation［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2000， 22（8）： 888-905.

SUN Y， OGAWA M. Evaluation of Video Compression for Facial ImagePhotoplethysmography in Time Series Space［C］. 2020 IEEE 2nd Global Conference on Life Sciences and Technologies， Kyoto， Japan： LifeTech， 2020： 388-390.

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