Literature DB >> 25183767

Photoplethysmographic measurement of various retinal vascular pulsation parameters and measurement of the venous phase delay.

William H Morgan1, Martin L Hazelton2, Brigid D Betz-Stablein2, Dao-Yi Yu1, Christopher R P Lind3, Vignesh Ravichandran4, Philip H House1.   

Abstract

PURPOSE: Retinal vein pulsation properties are altered by glaucoma, intracranial pressure (ICP) changes, and retinal venous occlusion, but measurements are limited to threshold measures or manual observation from video frames. We developed an objective retinal vessel pulsation measurement technique, assessed its repeatability, and used it to determine the phase relations between retinal arteries and veins.
METHODS: Twenty-three eyes of 20 glaucoma patients had video photograph recordings from their optic nerve and peripapillary retina. A modified photoplethysmographic system using video recordings taken through an ophthalmodynamometer and timed to the cardiac cycle was used. Aligned video frames of vessel segments were analyzed for blood column light absorbance, and waveform analysis was applied. Coefficient of variation (COV) was calculated from data series using recordings taken within ±1 unit ophthalmodynamometric force of each other. The time in cardiac cycles and seconds of the peak (dilation) and trough (constriction) points of the retinal arterial and vein pulse waveforms were measured.
RESULTS: Mean vein peak time COV was 3.4%, and arterial peak time COV was 4.4%. Lower vein peak occurred at 0.044 cardiac cycles (0.040 seconds) after the arterial peak (P = 0.0001), with upper vein peak an insignificant 0.019 cardiac cycles later. No difference in COV for any parameter was found between upper or lower hemiveins. Mean vein amplitude COV was 12.6%, and mean downslope COV was 17.7%.
CONCLUSIONS: This technique demonstrates a small retinal venous phase lag behind arterial pulse. It is objective and applicable to any eye with clear ocular media and has moderate to high reproducibility. ( http://www.anzctr.org.au number, ACTRN12608000274370.). Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

Entities:  

Keywords:  glaucoma; retina; retinal blood flow

Mesh:

Year:  2014        PMID: 25183767     DOI: 10.1167/iovs.14-15104

Source DB:  PubMed          Journal:  Invest Ophthalmol Vis Sci        ISSN: 0146-0404            Impact factor:   4.799


  9 in total

1.  Time-resolved quantitative inter-eye comparison of cardiac cycle-induced blood volume changes in the human retina.

Authors:  Ralf-Peter Tornow; Jan Odstrcilik; Radim Kolar
Journal:  Biomed Opt Express       Date:  2018-11-14       Impact factor: 3.732

2.  Assessing blood vessel perfusion and vital signs through retinal imaging photoplethysmography.

Authors:  Harnani Hassan; Sheila Jaidka; Vincent M Dwyer; Sijung Hu
Journal:  Biomed Opt Express       Date:  2018-04-26       Impact factor: 3.732

3.  Waveform analysis of human retinal and choroidal blood flow with laser Doppler holography.

Authors:  Léo Puyo; Michel Paques; Mathias Fink; José-Alain Sahel; Michael Atlan
Journal:  Biomed Opt Express       Date:  2019-09-05       Impact factor: 3.732

4.  Heart rate and age modulate retinal pulsatile patterns.

Authors:  Ivana Labounková; René Labounek; Radim Kolář; Ralf P Tornow; Charles F Babbs; Collin M McClelland; Benjamin R Miller; Igor Nestrašil
Journal:  Commun Biol       Date:  2022-06-14

5.  Quantitative Analysis of Fundus-Image Sequences Reveals Phase of Spontaneous Venous Pulsations.

Authors:  Fabrice Moret; Charlotte M Reiff; Wolf A Lagrèze; Michael Bach
Journal:  Transl Vis Sci Technol       Date:  2015-09-16       Impact factor: 3.283

6.  Objective detection of retinal vessel pulsation.

Authors:  William H Morgan; Anmar Abdul-Rahman; Dao-Yi Yu; Martin L Hazelton; Brigid Betz-Stablein; Christopher R P Lind
Journal:  PLoS One       Date:  2015-02-02       Impact factor: 3.240

7.  A combined convolutional and recurrent neural network for enhanced glaucoma detection.

Authors:  Soheila Gheisari; Sahar Shariflou; Jack Phu; Paul J Kennedy; Ashish Agar; Michael Kalloniatis; S Mojtaba Golzan
Journal:  Sci Rep       Date:  2021-01-21       Impact factor: 4.379

8.  A machine learning approach in the non-invasive prediction of intracranial pressure using Modified Photoplethysmography.

Authors:  Anmar Abdul-Rahman; William Morgan; Dao-Yi Yu
Journal:  PLoS One       Date:  2022-09-29       Impact factor: 3.752

9.  Zero retinal vein pulsation amplitude extrapolated model in non-invasive intracranial pressure estimation.

Authors:  W H Morgan; A Vukmirovic; A Abdul-Rahman; Y J Khoo; A G Kermode; C R Lind; J Dunuwille; D Y Yu
Journal:  Sci Rep       Date:  2022-03-25       Impact factor: 4.379

  9 in total

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