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

A simple model to detect atrial fibrillation via visual imaging

Automatic detection of atrial fibrillation (AF) is a challenging issue. In this study we proposed and validated a model to identify AF by using facial video recordings. We analyzed photoplethysmographic imaging (PPGi) signals, extracted from video of a subject's face. Sixty-eight patients were included: 30 in sinus rhythm (SR), 25 in AF and 13 presenting with atrial flutter or frequent ectopic beats (ARR). Twenty-six indexes were computed. The dataset was divided in three subsets: the training, validation, and test set, containing, respectively, 58, 29, and 13% of the data. Mean of inter-systolic interval series (M), Local Maxima Similarity (LMS), and pulse harmonic strength (PHS) indexes were significantly different among all groups. Variability and irregularity parameters had the lowest values in SR, the highest in AF, with intermediate values in ARR. The PHS was higher in SR than in ARR, and higher in ARR than in AF. The LMS index was the highest in SR, intermediate in ARR and the lowest in AF. Similarity indexes were higher in SR than in AF and ARR. A model with three features, namely M, Similarity1 and LMS was chosen. With this model, the accuracy for the validation set was 0.947\ub10.007 for SR, 0.954\ub10.004 for AF and 0.919\ub10.006 for ARR; for the test set (never-seen data), accuracy was 0.876\ub10.021 for SR, 0.870\ub10.030 for AF and 0.863\ub10.029 for ARR. A contactless video-based monitoring can be used to detect AF, differentiating it from SR and from frequent ectopies

Atrial Impulses at the Atrioventricular Node: Arrival versus Conduction during Atrial Fibrillation

The atrioventricular (AV) node plays a central role during atrial fibrillation (AF).We have recently proposed a statistical AV node model defined by parameters characterizing the arrival rate of atrial impulses, the probability of an impulse choosing the slower of the two AV nodal pathways, the refractory periods of the pathways, and the prolongation of refractory periods. All model parameters are estimated from the RR series using maximum likelihood estimation, except for the mean arrival rate of atrial impulses which is estimated by the AF frequency derived from the f-waves. The aim of this study is to assess the relationship between the probability γ of an atrial impulse to arrive at the slow pathway with the probability α (used in the model) of an impulse to pass through that pathway. A theoretical equation was derived to relate α with γ and viceversa, highlighting the dependence on both refractory periods and their prolongation. Results based on simulations show that the equation was correct, with an average absolute error of 0.0034 ± 0.0008