Detection of occult paroxysmal atrial fibrillation

Abstract

This work introduces a novel approach to the detection of brief episodes of paroxysmal atrial fibrillation (PAF). The proposed detector is based on four parameters which characterize RR interval irregularity, P-wave absence, f-wave presence, and noise level, of which the latter three are determined from a signal produced by an echo state network. The parameters are used for fuzzy logic classification where the decisions involve information on prevailing signal quality; no training is required. The performance is evaluated on a large set of test signals with brief episodes of PAF. The results show that episodes with as few as five beats can be reliably detected with an accuracy of 0.88, compared to 0.82 for a detector based on rhythm information only (the coefficient of sample entropy); this difference in accuracy increases when atrial premature beats are present. The results also show that the performance remains essentially unchanged at noise levels up to [Formula: see text] RMS. It is concluded that the combination of information on ventricular activity, atrial activity, and noise leads to substantial improvement when detecting brief episodes of PAF.

Automated detection of atrial fibrillation using R-R intervals and multivariate-based classification

“…When Moorman and Lake first described the principle of the CoSEn [14] they also demonstrated its improved performance over CV in classifying AF from NSR. Petrenas et al [22] investigated the performance of for AF detection from the R-R interval and found that for small segment window lengths (<30 beats) the sensitivity, specificity and accuracy of AF detection was significantly reduced in recordings with high signal noise, also the authors demonstrated a reduction in the performance of during PACs confirming observations made in [7].…”

Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation

“…Precisely, the most recent works are putting special emphasis on palliating this issue [23,[25][26][27][28][29][30]. In fact, some authors have proposed the use of information from RR intervals series irregularity in combination with features obtained from the atrial activity (AA), that is, the P-orwaves [23,28,29].…”

Arrhythmia Classification via Time and Frequency Domain Analyses of Ventricular and Atrial Contractions

“…; -Combination of independent data from the atrial and ventricular contractions analyses [9,10]. A comparative study of AF detection methods [11] highlights the techniques based on analysis of RR interval irregularity as the most robust against noise, providing the highest sensitivity and specificity, while the combination of RR and atrial activity analysis assures the highest positive predictive value.…”

“…The ECG analyses for AF detection are performed either in the time [3][4][5][6][7][8][9][10] or in the frequency domain, where the dominant AF frequency is usually assessed over a signal with extracted QRS-T complexes [12,13].…”