A Novel Method for Detection of the Transition Between Atrial Fibrillation and Sinus Rhythm

Abstract

Automatic detection of atrial fibrillation (AF) for AF diagnosis, especially for AF monitoring, is necessarily desirable for clinical therapy. In this study, we proposed a novel method for detection of the transition between AF and sinus rhythm based on RR intervals. First, we obtained the delta RR interval distribution difference curve from the density histogram of delta RR intervals, and then detected its peaks, which represented the AF events. Once an AF event was detected, four successive steps were used to classify its type, and thus, determine the boundary of AF: 1) histogram analysis; 2) standard deviation analysis; 3) numbering aberrant rhythms recognition; and 4) Kolmogorov-Smirnov (K-S) test. A dataset of 24-h Holter ECG recordings (n = 433) and two MIT-BIH databases (MIT-BIH AF database and MIT-BIH normal sinus rhythm (NSR) database) were used for development and evaluation. Using the receiver operating characteristic curves for determining the threshold of the K-S test, we have achieved the highest performance of sensitivity and specificity (SP) (96.1% and 98.1%, respectively) for the MIT-BIH AF database, compared with other previously published algorithms. The SP was 97.9% for the MIT-BIH NSR database.

Automated Detection of Paroxysmal Atrial Fibrillation Using an Information-Based Similarity Approach

“…Let F denote the weight of the word, where F is computed using Shannon's entropy and normalized with the normalization factor Z. The degree of dissimilarity between two symbolic sequences can be defined as [16][17][18]:…”

“…Most of the detectors in Table 3 employ a window length of 127/128 beats, i.e., [15,16,18,19,21,22], however, detectors with a 128-beat window tend to miss brief clinical episodes. It is important to also consider the ability to detect brief AF episodes when evaluating detector performance.…”

“…Lian, et al [15] developed an AF detector with its basis centered on the Map of RR intervals versus change of RR intervals. Huang, et al [16] utilized a histogram of ∆RR n and standard deviation analysis. Parvaresh, et al [17] evaluated three classifiers for AF screening by using autoregressive modeling.…”

Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks

“…For the past years, a series of sophisticated methods have been developed to tackle the challenges of AF detection (Kikillus et al, 2007; Couceiro et al, 2008; Babaeizadeh et al, 2009; Yaghouby et al, 2010; Larburu et al, 2011; Parvaresh and Ayatollahi, 2011). Two classes of AF detection methods, the atrial activity analysis-based (Artis et al, 1991; Slocum et al, 1992; Lake and Moorman, 2011; Zhou et al, 2014; Ladavich and Ghoraani, 2015) and the ventricular response analysis-based (Moody and Mark, 1983; Tateno and Glass, 2001; Dash et al, 2009; Park et al, 2009; Huang et al, 2011; Lian et al, 2011; Yaghouby et al, 2012; Lee et al, 2014) method, attract the interest of the most of studies. The first category methods utilize the absence of P waves or the presence of f-waves for diagnosis.…”

“…The second category methods are based on the variability of RR intervals. Although these kinds of methods have a robust noise resistance, its diagnosis accuracy is unsatisfactory when a wide variety of rhythms need to be dealt with due to the limitation of the information conveyed by RR intervals (Petrutiu et al, 2006; Huang et al, 2011; Lian et al, 2011; Lee et al, 2014). …”