Optimal fiducial points for pulse rate variability analysis from forehead and finger photoplethysmographic signals

被引:28
|
作者
Peralta, Elena [1 ,2 ]
Lazaro, Jesus [1 ,2 ,5 ]
Bailon, Raquel [1 ,2 ]
Marozas, Vaidotas [3 ,4 ]
Gil, Eduardo [1 ,2 ]
机构
[1] Univ Zaragoza, IIS Aragon, Aragon Inst Engn Res I3A, Biomed Signal Interpretat & Computat Simulat BSIC, Zaragoza, Spain
[2] CIBER BBN, Zaragoza, Spain
[3] Kaunas Univ Technol, Biomed Engn Inst, Kaunas, Lithuania
[4] Kaunas Univ Technol, Elect Engn Dept, Kaunas, Lithuania
[5] Univ Connecticut, Dept Biomed Engn, Storrs, CT USA
基金
欧盟地平线“2020”;
关键词
photoplethysmography (PPG); ECG; heart rate variability; pulse rate variability; autonomic nervous system; fiducial point selection; transmission and reflection modes; HEART-RATE-VARIABILITY; SHORT-TERM ANALYSIS; RELIABILITY; SURROGATE;
D O I
10.1088/1361-6579/ab009b
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
Objective: The aim of this work is to evaluate and compare five fiducial points for the temporal location of each pulse wave from forehead and finger photoplethysmographic (PPG) pulse wave signals to perform pulse rate variability (PRV) analysis as a surrogate for heart rate variability (HRV) analysis. Approach: Forehead and finger PPG signals were recorded during a tilt-table test simultaneously with the electrocardiogram (ECG). Artefacts were detected and removed and five fiducial points were computed: apex, middle-amplitude and foot points of the PPG signal, apex point of the first derivative signal and the intersection point of the tangent to the PPG waveform at the apex of the derivative PPG signal and the tangent to the foot of the PPG pulse, defined as the intersecting tangents method. Pulse period (PP) time interval series were obtained from both PPG signals and compared with the RR intervals obtained from the ECG. HRV and PRV signals were estimated and classical time and frequency domain indices were computed. Main results: The middle-amplitude point of the PPG signal (n(M)), the apex point of the first derivative (n(A)*), and the tangent intersection point (n(T)) are the most suitable fiducial points for PRV analysis, resulting in the lowest relative errors estimated between PRV and HRV indices and higher correlation coefficients and reliability indices. Statistically significant differences according to the Wilcoxon test between PRV and HRV signals were found for the apex and foot fiducial points of the PPG, as well as the lowest agreement between RR and PP series according to Bland-Altman analysis. Hence, these signals have been considered less accurate for variability analysis. In addition, the relative errors are significantly lower for n(M) and n(A)(*) using Friedman statistics with a Bonferroni multiple-comparison test, and we propose that n(M) is the most accurate fiducial point. Based on our results, forehead PPG seems to provide more reliable information for a PRV assessment than finger PPG. Significance: The accuracy of the pulse wave detection depends on the morphology of the PPG. There is therefore a need to widely define the most accurate fiducial point for performing a PRV analysis under non-stationary conditions based on different PPG sensor locations and signal acquisition techniques.
引用
下载
收藏
页数:15
相关论文
共 50 条
  • [41] Comparison of Valley-to-Valley and Peak-to-Peak Intervals from Photoplethysmographic Signals to Obtain Heart Rate Variability in the Sitting Position
    Chen, Xiang
    Chen, Tianjun
    Luo, Feifei
    Li, Jin
    PROCEEDINGS OF THE 2013 6TH INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND INFORMATICS (BMEI 2013), VOLS 1 AND 2, 2013, : 214 - 218
  • [42] Fusing Partial Camera Signals for Noncontact Pulse Rate Variability Measurement
    McDuff, Daniel J.
    Blackford, Ethan B.
    Estepp, Justin R.
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2018, 65 (08) : 1725 - 1739
  • [43] Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification
    Millan, Cesar A.
    Giron, Nathalia A.
    Lopez, Diego M.
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2020, 17 (02)
  • [44] Arterial Pulse Rate Variability Analysis for Diagnoses
    Joshi, Aniruddha J.
    Chandran, Sharat
    Jayaraman, V. K.
    Kulkarni, B. D.
    19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6, 2008, : 2234 - 2237
  • [45] A novel algorithm to separate motion artifacts from photoplethysmographic signals obtained with a reflectance pulse oximeter
    Yao, JC
    Warren, S
    PROCEEDINGS OF THE 26TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7, 2004, 26 : 2153 - 2156
  • [46] Stress Measurement from Wearable Photoplethysmographic Sensor using Heart Rate Variability Data
    Mohan, P. Madhan
    Nagarajan, V.
    Das, Sounak Ranjan
    2016 INTERNATIONAL CONFERENCE ON COMMUNICATION AND SIGNAL PROCESSING (ICCSP), VOL. 1, 2016, : 1141 - 1144
  • [47] Feasibility Analysis for Pulse Rate Variability to Replace Heart Rate Variability of the Healthy Subjects
    Yu, Enze
    He, Dianning
    Su, Yingfei
    Zheng, Li
    Yin, Zhong
    Xu, Lisheng
    2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO), 2013, : 1065 - 1070
  • [48] Assessment of Subtle Changes in Diabetes-Associated Arteriosclerosis using Photoplethysmographic Pulse Wave from Index Finger
    Po-Chun Hsu
    Hsien-Tsai Wu
    Cheuk-Kwan Sun
    Journal of Medical Systems, 2018, 42
  • [49] Assessment of Subtle Changes in Diabetes-Associated Arteriosclerosis using Photoplethysmographic Pulse Wave from Index Finger
    Hsu, Po-Chun
    Wu, Hsien-Tsai
    Sun, Cheuk-Kwan
    JOURNAL OF MEDICAL SYSTEMS, 2018, 42 (03)
  • [50] OPTIMAL DETECTION OF PULSE AND RESPIRATION SIGNALS FROM THE WRIST
    FARAB, AA
    TACKER, WC
    FOSTER, K
    GEDDES, LA
    BOURLAND, JD
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 1985, 32 (10) : 870 - 870