2 research outputs found
Identification of atrial fibrillation episodes using a camera as contactless sensor
Identification of paroxysmal atrial fibrillation (AF) can
be difficult and undiagnosed AF patients are at high risk
of cardioembolic stroke or other complications associated
with AF. The aim of this study is to analyze the video photoplethysmografic
(vPPG) signal obtained from a videocamera
to explore the possibility of discriminating AF from
normal sinus rhythm (NSR) and other arrhythmias (ARR).
We acquired 24 3-min long face-videos (8 for each rhythm)
using an industrial camera. After preprocessing, vPPG
signal was extracted using zero-phase component analysis.
Diastolic minima were detected and inter-diastolic series
obtained. The signals were characterized by time domain
indexes, the sample entropy (SampEn); and the shape similarity
index (ShapeSim). The time domain indexes and
ShapeSim are significantly different when comparing the
group of patients with AF or ARR to subjects in NSR. SampEn
is significantly higher in AF than in NSR and ARR.
From the shape analysis, it can be noted that waves in
NSR are more similar than in AF. These preliminary results
show the capability of different indexes to capture differences
among AF, ARR and NSR. Further studies will help
in assessing the performance of the vPPG signal to screen
general population