Blind Source Separation of Retinal Pulsatile Patterns in Optic Nerve Head Video-Recordings

Dynamic optical imaging of retinal hemodynamics is a rapidly evolving technique in vision and eye-disease research. Video-recording, which may be readily accessible and affordable, captures several distinct functional phenomena such as the spontaneous venous pulsations (SVP) of central vein or local arterial blood supply etc. These phenomena display specific dynamic patterns that have been detected using manual or semi-automated methods. We propose a pioneering concept in retina video-imaging using blind source separation (BSS) serving as an automated localizer of distinct areas with temporally synchronized hemodynamics. The feasibility of BSS techniques (such as spatial principal component analysis and spatial independent component analysis) and K-means based post-processing method were successfully tested on the monocular and binocular video-ophthalmoscopic (VO) recordings of optic nerve head (ONH) in healthy subjects. BSSs automatically detected three spatially distinct reproducible areas, i.e. SVP, optic cup pulsations (OCP) that included areas of larger vessels in the nasal part of ONH, and other pulsations (OP). The K-means post-processing reduced a spike noise from the patterns' dynamics while high linear dependence between the non-filtered and post-processed signals was preserved. Although the dynamics of all patterns were heart rate related, the morphology analysis demonstrated significant phase shifts between SVP and OCP, and between SVP and OP. In addition, we detected low frequency oscillations that may represent respiratory-induced effects in time-courses of the VO recordings.

Automated summary

Dynamic optical imaging of retinal hemodynamics is an evolving technique in vision and eye-disease research, with video-recording capturing distinct functional phenomena. The use of blind source separation as an automated localizer of temporally synchronized hemodynamics in retina video-imaging has shown promising results, with the detection of reproducible areas and reduction of noise in patterns' dynamics through post-processing methods. The study also revealed differences in phase shifts between different dynamic patterns and detected low frequency oscillations possibly related to respiratory effects in the time-courses of the recordings.