Early differentiation of long-standing persistent atrial fibrillation using the characteristics of fibrillatory waves in surface ECG multi-leads

Park, Junbeom; Lee, Chung-Keun; Leshem, Eran; Blau, Ira; Kim, Sung‐Soo; Lee, Jung Myung; Hwang, Jaehoon; Choi, Byung Il; Lee, Moon Hyoung; Hwang, Hye Jin

doi:10.1038/s41598-019-38928-6

Cited by 15 publications

(10 citation statements)

References 44 publications

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“…Low f-wave frequency can predict successful outcome in patient with persistent AF undergoing cardioversion (Bollmann et al, 2008 ) and high f-wave frequency predicts early AF recurrence (Bollmann et al, 2003 ). The f-wave frequency can increase with the progression of AF, and patients with persistent AF often have a higher f-wave frequency than patients with paroxysmal AF (Alcaraz et al, 2011 ; Park et al, 2019 ). Further, it has been shown that a low f-wave frequency is associated with spontaneous conversion of recent-onset AF (Choudhary et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Respiratory Induced Modulation in f-Wave Characteristics During Atrial Fibrillation

et al. 2021

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The autonomic nervous system (ANS) is an important factor in cardiac arrhythmia, and information about ANS activity during atrial fibrillation (AF) may contribute to personalized treatment. In this study we aim to quantify respiratory modulation in the f-wave frequency trend from resting ECG. First, an f-wave signal is extracted from the ECG by QRST cancelation. Second, an f-wave model is fitted to the f-wave signal to obtain a high resolution f-wave frequency trend and an index for signal quality control (S). Third, respiratory modulation in the f-wave frequency trend is extracted by applying a narrow band-pass filter. The center frequency of the band-pass filter is determined by the respiration rate. Respiration rate is estimated from a surrogate respiration signal, obtained from the ECG using homomorphic filtering. Peak conditioned spectral averaging, where spectra of sufficient quality from different leads are averaged, is employed to obtain a robust estimate of the respiration rate. The envelope of the filtered f-wave frequency trend is used to quantify the magnitude of respiratory induced f-wave frequency modulation. The proposed methodology is evaluated using simulated f-wave signals obtained using a sinusoidal harmonic model. Results from simulated signals show that the magnitude of the respiratory modulation is accurately estimated, quantified by an error below 0.01 Hz, if the signal quality is sufficient (S>0.5). The proposed method was applied to analyze ECG data from eight pacemaker patients with permanent AF recorded at baseline, during controlled respiration, and during controlled respiration after injection of atropine, respectively. The magnitude of the respiratory induce f-wave frequency modulation was 0.15 ± 0.01, 0.18 ± 0.02, and 0.17 ± 0.03 Hz during baseline, controlled respiration, and post-atropine, respectively. Our results suggest that parasympathetic regulation affects the magnitude of respiratory induced f-wave frequency modulation.

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Section: Introductionmentioning

confidence: 99%

Respiratory Induced Modulation in f-Wave Characteristics During Atrial Fibrillation

et al. 2021

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“…This finding is not completely surprising due to two reasons. On the one hand, lead II can capture information from both right and left atria, because it is aligned with the interatrial septum [14]. In this way, f -waves in this lead could reflect more globally atrial activity and its organization.…”

Section: Discussionmentioning

confidence: 99%

Limb Versus Precordial ECG Leads as Improved Predictors of Electrical Cardioversion Outcome in Persistent Atrial Fibrillation

Cirugeda

Calero

Quesada

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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Electrical cardioversion (ECV) is an effective and lowcost rhythm control strategy for persistent atrial fibrillation (AF). Because of its limited mid-and long-term success rates, prediction of early failure could avoid patients with reduced chance to maintain sinus rhythm (SR). To this end and due to its proximity to the right atrium, several indices characterizing atrial activity have been proposed based on lead V1. However, information from other leads has been discarded to date. Hence, this work studies how effective some common indices computed over the whole set of 12 standard ECG leads are in predicting ECV outcome. Precisely, amplitude, dominant frequency, and sample entropy were computed from the fibrillatory (f-) waves extracted for each one of 12 standard leads acquired before ECV from 58 patients in persistent AF. The classification between the patients who relapsed to AF and maintained sinus rhythm after a follow-up of 4 weeks achieved by these parameters was better from limb lead II than from V1, thus reporting improvements about 6 and 12%. As a consequence, characterization of f-waves from the more accessible limb lead II has proven to be the best choice to improve AF ECV outcome prediction from the ECG.

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“…Therefore, to extract features from atrial activity either an isolated f-wave or ECG excerpt excluding QRS complex (e.g TQ interval) are used. To get an isolated f-wave or ECG free from QRS complex, QRS cancellation is a common preliminary step [88]. Popular methods of QRS cancellation include direct suppression of QRS, average beat subtraction (ABS) [89], [90], use of filters (e.g, bandpass filter, adaptive filter) [91], spatiotemporal QRS cancellation, blind source separation, independent component analysis, and principal component analysis.…”

Section: Features Extractionmentioning

confidence: 99%

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning

Rizwan

Zoha

Mabrouk

et al. 2021

IEEE Rev. Biomed. Eng.

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Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the main causes of morbidity and mortality worldwide. The timely diagnosis of AF is an equally important and challenging task because of its asymptomatic and episodic nature. n this paper, state-ofthe-art ECG data-based machine learning models and signal processing techniques applied for auto diagnosis of AF are reviewed. Moreover, key biomarkers of AF on ECG and the common methods and equipment used for the collection of ECG data are discussed. Besides that, the modern wearable and implantable ECG sensing technologies used for gathering AF data are presented briefly. In the end, key challenges associated with the development of auto diagnosis solutions of AF are also highlighted. It is the first review paper of its kind that comprehensively presents a discussion on all these aspects related to AF auto-diagnosis at one place. It is observed that there is dire need of low energy, low cost but accurate auto diagnosis solutions for the proactive management of AF.

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Early differentiation of long-standing persistent atrial fibrillation using the characteristics of fibrillatory waves in surface ECG multi-leads

Cited by 15 publications

References 44 publications

Respiratory Induced Modulation in f-Wave Characteristics During Atrial Fibrillation

Respiratory Induced Modulation in f-Wave Characteristics During Atrial Fibrillation

Limb Versus Precordial ECG Leads as Improved Predictors of Electrical Cardioversion Outcome in Persistent Atrial Fibrillation

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning

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