Literature DB >> 30669123

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

Elena Peralta1, Jesus Lazaro, Raquel Bailon, Vaidotas Marozas, Eduardo Gil.   

Abstract

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 ([Formula: see text]), 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 [Formula: see text] 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.

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Year:  2019        PMID: 30669123     DOI: 10.1088/1361-6579/ab009b

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  10 in total

1.  Wearable Photoplethysmography for Cardiovascular Monitoring.

Authors:  Peter H Charlton; Panicos A Kyriaco; Jonathan Mant; Vaidotas Marozas; Phil Chowienczyk; Jordi Alastruey
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2022-03-11       Impact factor: 10.961

2.  Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals.

Authors:  Jonathan Martinez; Kaan Sel; Bobak J Mortazavi; Roozbeh Jafari
Journal:  IEEE Open J Eng Med Biol       Date:  2022-05-12

3.  Heart Rate Variability from Wearable Photoplethysmography Systems: Implications in Sleep Studies at High Altitude.

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Journal:  Sensors (Basel)       Date:  2022-04-09       Impact factor: 3.847

4.  Establishing best practices in photoplethysmography signal acquisition and processing.

Authors:  Peter H Charlton; Kristjan Pilt; Panicos A Kyriacou
Journal:  Physiol Meas       Date:  2022-05-25       Impact factor: 2.688

5.  Validation of a Wireless Bluetooth Photoplethysmography Sensor Used on the Earlobe for Monitoring Heart Rate Variability Features during a Stress-Inducing Mental Task in Healthy Individuals.

Authors:  Bruno Correia; Nuno Dias; Patrício Costa; José Miguel Pêgo
Journal:  Sensors (Basel)       Date:  2020-07-13       Impact factor: 3.576

6.  Few-shot pulse wave contour classification based on multi-scale feature extraction.

Authors:  Peng Lu; Chao Liu; Xiaobo Mao; Yvping Zhao; Hanzhang Wang; Hongpo Zhang; Lili Guo
Journal:  Sci Rep       Date:  2021-02-12       Impact factor: 4.379

7.  Detecting beats in the photoplethysmogram: benchmarking open-source algorithms.

Authors:  Peter H Charlton; Kevin Kotzen; Elisa Mejía-Mejía; Philip J Aston; Karthik Budidha; Jonathan Mant; Callum Pettit; Joachim A Behar; Panicos A Kyriacou
Journal:  Physiol Meas       Date:  2022-08-19       Impact factor: 2.688

8.  Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach.

Authors:  Ivan Liu; Shiguang Ni; Kaiping Peng
Journal:  Sensors (Basel)       Date:  2020-03-30       Impact factor: 3.576

9.  Estimation of Heart Rate Variability from Finger Photoplethysmography During Rest, Mild Exercise and Mild Mental Stress.

Authors:  Bjørn-Jostein Singstad; Naomi Azulay; Andreas Bjurstedt; Simen S Bjørndal; Magnus F Drageseth; Peter Engeset; Kari Eriksen; Muluberhan Y Gidey; Espen O Granum; Matias G Greaker; Amund Grorud; Sebastian O Hewes; Jie Hou; Adrián M Llop Recha; Christoffer Matre; Arnoldas Seputis; Simen E Sørensen; Vegard Thøgersen; Vegard Munkeby Joten; Christian Tronstad; Ørjan G Martinsen
Journal:  J Electr Bioimpedance       Date:  2021-12-18

10.  Heart Rate Variability Changes in Patients With Major Depressive Disorder: Related to Confounding Factors, Not to Symptom Severity?

Authors:  Jan Sarlon; Angelica Staniloiu; Andreas Kordon
Journal:  Front Neurosci       Date:  2021-07-05       Impact factor: 4.677

  10 in total

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