Surface ECG f Wave Analysis at Initial Onset of Paroxysmal and Persistent Atrial Fibrillation

The atrial electrocardiogram in patients with persistent atrial fibrillation

BACKGROUND

The electrocardiogram (ECG) in persistent atrial fibrillation (AF) is underused. Signal processing techniques can extract the f-waves from the QRS-T complex to generate the atrial ECG (A-ECG).

OBJECTIVE

This study aimed to explore A-ECG technical and clinical issues.

METHODS

Thirty patients (age, 62.6 ± 9.4 years; 80% male; CHA2DS2-VASc score, 2.2 ± 1.7; ejection fraction, 47.9% ± 10.4%) with persistent AF undergoing ablation were enrolled. ECGs were downloaded for offline analysis. A-ECG was derived by QRS-T subtraction and independent component analysis filtering. A-ECG short-term (DF10s, OI10s) and long-term (DF_Global, OI_Global) spectral features were derived. Optimal recording duration, temporal reproducibility, differences between leads, comparison to intracardiac dominant frequency (DF), acute ablation effect, and A-ECG differences based on ablation outcomes were evaluated.

RESULTS

Time-frequency A-ECG analysis demonstrated dynamic spectral features. Repeated 10-second A-ECG recordings had higher coefficient of variation (7.5%-10.0%) compared with repeated 4- to 5-minute recordings (1.1%-3.4%). Short-term reproducibility of 2 recordings ∼30 minutes apart showed high correlation (r = 0.91-0.99; all P values < .0001). Regional lead analyses showed no significant differences except for the comparison of posterior leads V7-V9 vs V1-V3. A-ECG DF parameters correlated with right atrial DF (r = 0.64-0.72; P < .0001) and not left atrial DF. A-ECG DF declined after ablation (median-DF10s-1min, 5.94 ± 0.63 Hz vs 5.77 ± 0.63 Hz; P = .0003). Patients with successful ablation at 1 year had higher A-ECG DF vs those with recurrences (median-DF10s-5min, 6.04 ± 0.42 Hz vs 5.13 ± 0.93 Hz; P = .003).

CONCLUSION

This study provides the practical basis for further use of the A-ECG as a novel tool for evaluating persistent AF and demonstrates its clinical relevance. Further phenotypic classification of AF based on the A-ECG and assessment of treatment response should be explored.

Pathophysiology and clinical relevance of atrial myopathy

Atrial myopathy is a condition that consists of electrical, structural, contractile, and autonomic remodeling of the atria and is the substrate for development of atrial fibrillation, the most common arrhythmia. Pathophysiologic mechanisms driving atrial myopathy are inflammation, oxidative stress, atrial stretch, and neurohormonal signals, e.g., angiotensin-II and aldosterone. These mechanisms initiate the structural and functional remodeling of the atrial myocardium. Novel therapeutic strategies are being developed that target the pathophysiologic mechanisms of atrial myopathy. In this review, we will discuss the pathophysiology of atrial myopathy, as well as diagnostic and therapeutic strategies.

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

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. In this paper, state-of-the-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. This is the first review paper of its kind that comprehensively presents a discussion on all these aspects related to AF auto-diagnosis in one place. It is observed that there is a dire need for low energy and low cost but accurate auto diagnosis solutions for the proactive management of AF.

Atrial Myopathy Underlying Atrial Fibrillation

While AF most often occurs in the setting of atrial disease, current assessment and treatment of patients with AF does not focus on the extent of the atrial myopathy that serves as the substrate for this arrhythmia. Atrial myopathy, in particular atrial fibrosis, may initiate a vicious cycle in which atrial myopathy leads to AF, which in turn leads to a worsening myopathy. Various techniques, including ECG, plasma biomarkers, electroanatomical voltage mapping, echocardiography, and cardiac MRI, can help to identify and quantify aspects of the atrial myopathy. Current therapies, such as catheter ablation, do not directly address the underlying atrial myopathy. There is emerging research showing that by targeting this myopathy we can help decrease the occurrence and burden of AF.