Non-Contact Atrial Fibrillation Detection Based on Video Pulse Features

doi:10.3778/j.issn.1002-8331.2201-0385

Abstract

Abstract: Early detection of atrial fibrillation is very important for the prevention of cardiovascular and cerebrovascular diseases. This paper proposes a facial video atrial fibrillation detection method. The method extracts the pulse signals from the facial video by face tracking and improved complete ensemble empirical modes decomposition（ICEEMD）, and extracts atrial fibrillation discriminative features from video pulse signals according to the pulse characteristics during atrial fibrillation episodes. An improved recursive feature elimination feature selection method is designed to screen out the more important features for atrial fibrillation detection. Based on the above features, machine learning methods are used to achieve atrial fibrillation detection. Experiments are conducted on 122 cases of atrial fibrillation patients and 139 cases of normal sinus rhythm facial videos. The optimal features are the percentage of heartbeats with adjacent RR interval greater than 50 ms （PNN50）, the maximum value of RR intervals （maxRR）, and the horizontal radius of poincare diagram （SD2） etc. Based on the above optimal feature set, the accuracy of atrial fibrillation detection is 92.31%, the specificity is 90.24%, the sensitivity is 94.59%, and the AUC is 0.920 5.

Key words: machine learning, atrial fibrillation detection, facial video, feature selection

摘要： 房颤的早发现对心脑血管疾病的预防至关重要。提出一种面部视频房颤检测方法。该方法通过人脸跟踪和集成经验模态分解从面部视频中提取脉搏信号，根据房颤发作时的脉搏特性，从视频脉搏信号中提取房颤判别特征。设计一种改进的递归特征消除特征选择方法，筛选出对房颤检测较重要的特征，基于以上特征采用机器学习方法实现房颤检测。在采集的122例房颤病人和139例正常窦性心律面部视频上实验，最优特征为相邻RR间期大于50?ms的心搏百分比（PNN50）、RR间期的最大值（maxRR）、庞加莱图水平半径（SD2）等。基于以上最优特征集，房颤检测的准确率为92.31%，特异度是90.24%，灵敏度是94.59%，AUC是0.920 5。

关键词: 机器学习, 房颤检测, 面部视频, 特征选择

ZHANG Xu, YANG Xuezhi, LIU Xuenan, FANG Shuai. Non-Contact Atrial Fibrillation Detection Based on Video Pulse Features[J]. Computer Engineering and Applications, 2023, 59(8): 331-340.

张姁, 杨学志, 刘雪南, 方帅. 视频脉搏特征的非接触房颤检测[J]. 计算机工程与应用, 2023, 59(8): 331-340.

References

[1] FUSTER V，RYDéN L E，ASINGER R W，et al.ACC/AHA/ESC guidelines for the management of patients with atrial fibrillation：executive summary：a report of the Ameri-
can College of Cardiology/American Heart Association Task Force on Practice Guidelines and the European Society of Cardiology Committee for Practice Guidelines and Policy Conferences（Committee to Develop Guidelines for the Management of Patients With Atrial Fibrillation） Developed in collaboration with the North American Society of Pacing and Electrophysiology[J].Journal of the American College of Cardiology，2001，38（4）：1231-1265.
[2] COLILLA S，CROW A，PETKUN W，et al.Estimates of current and future incidence and prevalence of atrial fibrillation in the U.S. adult population[J].The American Journal of Cardiology，2013，112（8）：1142-1147.
[3] KRIJTHE B P，KUNST A，BENJAMIN E J，et al.Projections on the number of individuals with atrial fibrillation in the European Union，from 2000 to 2060[J].European Heart Journal，2013，34（35）：2746-2751.
[4] JONES N R，TAYLOR C J，HOBBS F D R，et al.Screening for atrial fibrillation：a call for evidence[J].European Heart Journal，2020，41（10）：1075-1085.
[5] 黄从新，张澍，黄德嘉，等.心房颤动：目前的认识和治疗建议-2015[J].中国心脏起搏与心电生理杂志，2015，29（5）：377-434.
HUANG C X，ZHANG S，HUANG D J，et al.Atrial fibrillation：current knowledge and treatment recommendations-2015[J].Chinese Journal of Cardiac Pacing and Electrophysiology，2015，29（5）：377-434.
[6] SANNA T，DIENER H C，PASSMAN R S，et al.Cryptogenic stroke and underlying atrial fibrillation[J].The New England Journal of Medicine，2014，370（26）：2478-2486.
[7] HINDRICKS G，POTPARA T，DAGRES N，et al.2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery（EACTS） The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology（ESC） Developed with the special contribution of the European Heart Rhythm Association（EHRA） of the ESC[J].European Heart Journal，2021，42（5）：373-498.
[8] TURAKHIA M P，DESAI M，HEDLIN H，et al.Rationale and design of a large-scale，app-based study to identify cardiac arrhythmias using a smartwatch：the apple heart study[J].American Heart Journal，2019，207：66-75.
[9] GUO Y，WANG H，ZHANG H，et al.Mobile photoplethysmographic technology to detect atrial fibrillation[J].Journal of the American College of Cardiology，2019，74（19）：2365-2375.
[10] FAVILLA R，ZUCCALA V C，COPPINI G.Heart rate and heart rate variability from single-channel video and ICA integration of multiple signals[J].IEEE Journal of Biomedical and Health Informatics，2018，23（6）：2398-2408.
[11] WANG W，STUIJK S，DE HAAN G.Exploiting spatial redundancy of image sensor for motion robust rPPG[J].IEEE Transactions on Biomedical Engineering，2014，62（2）：415-425.
[12] 杨昭.运动干扰下人脸视频心率检测方法研究[D].合肥：合肥工业大学，2019：19-28.
YANG Z.Research on human face video heart rate detection method under motion interference[D].Hefei：Hefei University of Technology，2019：19-28.
[13] POH M Z，MCDUFF D J，PICARD R W.Advancements in noncontact，multiparameter physiological measurements using a webcam[J].IEEE Transactions on Biomedical Engineering，2010，58（1）：7-11.
[14] 杨刚，薛挺，高伟.一种抗运动干扰的实时心率提取方法[J].计算机工程与应用，2019，55（5）：244-250.
YANG G，XUE T，GAO W.Real-time heart rate extraction method of anti-motion interference[J].Computer Engineering and Applications，2019，55（5）：244-250.
[15] GHODRATIGOHAR M，GHANADIAN H，AL OSMAN H.A remote respiration rate measurement method for non-stationary subjects using CEEMDAN and machine learning[J].IEEE Sensors Journal，2019，20（3）：1400-1410.
[16] DE HAAN G，JEANNE V.Robust pulse rate from chrominance-based rPPG[J].IEEE Transactions on Biomedical Engineering，2013，60（10）：2878-2886.
[17] WANG W，STUIJK S，DE HAAN G.A novel algorithm for remote photoplethysmography：spatial subspace rotation[J].IEEE Transactions on Biomedical Engineering，2015，63（9）：1974-1984.
[18] WANG W，DEN BRINKER A C，STUIJK S，et al.Algorithmic principles of remote PPG[J].IEEE Transactions on Biomedical Engineering，2016，64（7）：1479-1491.
[19] CHEN W，MCDUFF D.Deepphys：video-based physiological measurement using convolutional attention networks[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：349-365.
[20] YU Z，PENG W，LI X，et al.Remote heart rate measurement from highly compressed facial videos：an end-to-end deep learning solution with video enhancement[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：151-160.
[21] NIU X，SHAN S，HAN H，et al.Rhythmnet：end-to-end heart rate estimation from face via spatial-temporal representation[J].IEEE Transactions on Image Processing，2019，29：2409-2423.
[22] LEE E，CHEN E，LEE C Y，Meta-rppg：remote heart rate estimation using a transductive meta-learner[C]//European Conference on Computer Vision.Cham：Springer，2020，392-409.
[23] KYAL S，MESTHA L K，XU B，et al.A method to detect cardiac arrhythmias with a webcam[C]//2013 IEEE Signal Processing in Medicine and Biology Symposium（SPMB），2013：1-5.
[24] COUDERC J P，KYAL S，MESTHA L K，et al.Pulse harmonic strength of facial video signal for the detection of atrial fibrillation[J].Computing in Cardiology，2014：661-664.
[25] YAN B P，LAI W H S，CHAN C K Y，et al.Contact‐free screening of atrial fibrillation by a smartphone using facial pulsatile photoplethysmographic signals[J].Journal of the American Heart Association，2018，7（8）：e008585.
[26] SHI J，ALIKHANI I，LI X，et al.Atrial fibrillation detection from face videos by fusing subtle variations[J].IEEE Transactions on Circuits and Systems for Video Technology，2020，30（8）：2781-2795.
[27] YAN B P，LAI W H S，CHAN C K Y，et al，High-throughput contact-free detection of atrial fibrillation from video with deep learning[J].JAMA Cardiology，2020，5（1）：105-107.
[28] SUN Z，JUNTTILA J，TULPPO M，et al.Non-contact atrial fibrillation detection from face videos by learning systolic peaks[J].arXiv：2110.07610，2021.
[29] DAUTOV C P，DAUTOV R，COUDERC J P，et al.Machine learning approach to detection of atrial fibrillation using high quality facial videos[C]//IEEE EMBS International Conference on Biomedical and Health Informatics（BHI），2021：1-4.
[30] KWON S，KIM J，LEE D，et al.ROI analysis for remote photoplethysmography on facial video[C]//2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society（EMBC），2015：4938-4941.
[31] COLOMINAS M A，SCHLOTTHAUER G，TORRES M E.Improved complete ensemble EMD：a suitable tool for biomedical signal processing[J].Biomedical Signal Processing and Control，2014，14：19-29.
[32] HUANG W，CAI N，XIE W，et al.ECG baseline wander correction based on ensemble empirical mode decomposition with complementary adaptive noise[J].Journal of Medical Imaging and Health Informatics，2015，5（8）：1796-1799.
[33] SELVARAJ N，MENDELSON Y，SHELLEY K H，et al.Statistical approach for the detection of motion/noise artifacts in photoplethysmogram[C]//2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society，2011：4972-4975.
[34] ELGENDI M.Optimal signal quality index for photoplethysmogram signals[J].Bioengineering，2016：3（4）：21.
[35] 母远慧.心律失常ECG信号的多尺度时频特征提取与分析研究[D].北京：北京理工大学，2015：10-11.
MU Y H.Multi-scale time-frequency feature extraction and analysis of arrhythmia ECG signals[D].Beijing：Beijing Institute of Technology，2015：10-11.
[36] 聂春燕.混沌系统与弱信号检测[M].北京：清华大学出版社，2009：1-55.
NIE C Y.Chaos system and weak signal detection[M].Beijing：Tsinghua University Press，2009：1-55.
[37] ZHANG H F，SHU Y T，YANG O.Estimation of Hurst parameter by variance-time plots[C]//1997 IEEE Pacific Rim Conference on Communications，Computers and Signal Processing，PACRIM.10 Years Networking the Pacific Rim，1987-1997，1997：883-886.
[38] LITTLE M，MCSHARRY P，ROBERTS S，et al.Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection[J].Nature Precedings，2007：23.
[39] PENG C K，HAVLIN S，STANLEY H E，et al.Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series[J].Chaos，1995，5（1）：82-87.
[40] KAREGAR F P，FALLAH A，RASHIDI S.Using recurrence quantification analysis and generalized Hurst exponents of ECG for human authentication[C]//2017 2nd Conference on Swarm Intelligence and Evolutionary Computation（CSIEC），2017：66-71.
[41] MOHEBBI M，GHASSEMIAN H，ASL B M.Structures of the recurrence plot of heart rate variability signal as a tool for predicting the onset of paroxysmal atrial fibrillation[J].Journal of Medical Signals and Sensors，2011，1（2）：113.
[42] 李郅琴，杜建强，聂斌，等.特征选择方法综述[J].计算机工程与应用，2019，55（24）：10-19.
LI Z Q，DU J Q，NIE B，et al.Summary of feature selection methods[J].Computer Engineering and Applications，2019，55（24）：10-19.
[43] GUYON I，WESTON J，BARNHILL S，et al.Gene selection for cancer classification using support vector machines[J].Machine Learning，2002，46（1）：389-422.
[44] YE Y，HE W，CHENG Y，et al.A robust random forest-based approach for heart rate monitoring using photoplethysmography signal contaminated by intense motion artifacts[J].Sensors，2017，17（2）：385.
[45] GRANITTO P M，FURLANELLO C，BIASIOLI F，et al.Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products[J].Chemometrics and Intelligent Laboratory Systems，2006，83（2）：83-90.
[46] BREIMAN L.Random forests[J].Machine Learning，2001，45（1）：5-32.
[47] LOUPPE G.Understanding random forests：from theory to practice[J].arXiv：1407.7502，2014.
[48] HOOKER G，MENTCH L.Please stop permuting features：an explanation and alternatives[J].arXiv：1905.03151，2019.
[49] SHAPLEY L S，ROTH A E.The Shapley value：essays in honor of Lloyd S.Shapley[M].Cambridge：Cambridge University Press，1988：307-317.
[50] EBRAHIMZADEH E，KALANTARI M，JOULANI M，et al.Prediction of paroxysmal atrial fibrillation：a machine learning based approach using combined feature vector and mixture of expert classification on HRV signal[J].Computer Methods and Programs in Biomedicine，2018，165：53-67.
[51] 董阳，黄织春.低强度耳屏迷走神经刺激治疗心房颤动的研究进展[J].中国心血管杂志，2021，26（1）：82-85.
DONG Y，HUANG Z C.Research progress of low-intensity tragus vagus nerve stimulation in the treatment of atrial fibrillation[J].China Cardiovascular Journal，2021，26（1）：82-85.
[52] ACHARD S，PHAM D T，JUTTEN C.Criteria based on mutual information minimization for blind source separation in post nonlinear mixtures[J].Signal Processing，2005，85（5）：965-974.
[53] ALTMANN A，TOLO?I L，SANDER O，et al.Permutation importance：a corrected feature importance measure[J].Bioinformatics，2010，26（10）：1340-1347.