Introduction Atrial fibrillation (AF) is the most common age-related, progressive tachyarrhythmia in the USA and in European countries. AF is associated with an increased risk of stroke, heart failure, impaired cognitive function, and increased mortality. An obstacle for optimal diagnosis and treatment is the relatively unknown (electro-)pathophysiology of AF. In combination with intra-operative cardiac mapping, accurate analysis of the AF burden using post-operative continuous rhythm registrations might provide great insight into the underlying mechanisms of AF development. However, manual analysis of these continuous rhythm registrations is both time-consuming and subject to interpretation. Therefore, the aim of this study is to develop an automated AF detection algorithm for use in the research setting. Methods Using 6,400 manually annotated 30-seconds electrograms (ECGs) derived from the post-operative continuous rhythm registrations in the Erasmus Medical Center (Rotterdam), and 192 annotated records from standard MIT-BIH ECG databases, a classifier was developed with three output classes: AF, No AF, and Unusable (due to noise/artefacts). QRS-complexes were detected using a method based on the Pan-Tompkins algorithm. Subsequently, P- and T-waves were detected and features were extracted, which can be grouped into eight groups: RR-interval characteristics, peak-interval characteristics, amplitude characteristics, P-wave characteristics, T-wave characteristics, QRS-morphology characteristics, autocorrelation characteristics, and noise. Multiple classifiers were trained using a training set containing 4,800 post-operative ECGs and a hidden test set containing the remaining 1,600 post-operative ECGs. The optimal classifier in terms of accuracy was further optimized. Results Optimal classification was achieved using boosted decision trees. For the hidden test set, this resulted in an accuracy of 96.44% (95% CI: 95.41% - 97.24%) for detection of AF with a false negative rate of 2.8% (95% CI: 1.5% - 4.9%) and a false positive rate of 3.8% (95% CI: 2.9% - 5.1%). Of all 74 misclassifications, 36 (49%) were made in the group with irregular rhythms without AF. Classification was mainly based on the RR-interval characteristics. Conclusion An automated AF classifier based on post-operative continuous rhythm registrations for use in the research setting was proposed. Careful use of the classifier in combination with manual validation of detected AF segments makes the classifier suitable for supervised research purposes.

Background: Unipolar electrograms (U-EGMs) contain additional information about interatrial activation and conduction in their morphology, which may aid towards improved diagnosis and staging of atrial fibrillation (AF).Objective: The primary objective is to investigate regional differences in electrogram area (EA) during SR and AF and to design a patient-specific EA fingerprint, to characterize the arrhythmogenic substrate in patients with mitral valve disease (MVD).Methods: Patients (N = 42) either with (‘AF group’, N = 23) or without a history of AF (‘No AF group’, N = 19), undergoing elective open heart surgery underwent high-resolution mapping of the right atrium (RA), left atrium (LA) and pulmonary veins (PV) including Bachmann’s bundle (BB). Spatial distributions of mean EA, variance and total EA were determined in SR and AF. Absolute EA values were correlated with amplitude, an established metric in substrate mapping.Results: A total of 3104460 EAs were analysed and compared between rhythms, regions and groups (Table 3). EA was larger in AF [SR: 54.97 (42.87), AF: 57.03 (51.02), p < 0.01], but smaller per region except the RA. In patients with AF, EA was significantly smaller across all atrial regions. During AF, amplitude showed moderate correlation with EA at best [no AF: r = 0.54 vs. AF: = 0.51].Conclusion: The EA feature, entailing the U-EGM amplitude, duration and overall morphology, is suitable in signal fingerprinting to characterize the arrhythmogenic substrate and contains additional information compared with amplitude alone. Further studies are required to fine-tune the EA and implement EA-based classification.

For the heart to pump blood throughout the body, electrical impulses that trigger the cellular contraction must be generated and spread through the myocardial tissue. These signals propagate faster along the longitudinal cardiac fiber direction than the transverse direction, conferring the heart with anisotropic conduction properties. Therefore, the arrangement of the fibers within the tissue governs the impulse propagation. Given the variability of the fiber direction across the heart and between patients, incorporating it into electrophysiological models would enhance our understanding of the mechanisms and progression of different heart conditions, such as atrial fibrillation (AF). The study of this common cardiac arrhythmia relies on analyzing electrical recordings of the heart, known as electrograms (EGMs), which, if integrated with the patient’s fiber architecture into cardiac models, can enable effective personalized treatment. Over the years, researchers have proposed different approaches to estimate the fiber direction from EGMs. However, these methods have been evaluated in different, usually simplistic, cardiac tissue models, making their comparison, and therefore selection of the most accurate approach for clinical and research applications, challenging. The current study aims to identify the best fiber direction estimation method under consistent and realistic conditions. To achieve this goal, synthetic EGMs and local activation time (LAT) maps were generated from 2D and 3D monodomain models that mimicked the muscle bundle, atrial bilayer, and ventricular transmural fiber rotation structures. A comparison analysis of existing fiber direction estimation methods, first as described by their authors and then standardized to have the same spatial resolution, showed the superior performance of the techniques based on fitting an ellipse to local conduction velocity or conduction slowness vectors from a whole LAT map. The estimation accuracy of these methods can be further improved by increasing the number of vectors to which the ellipse is fitted. Nonetheless, given the influence of underlying layers in the epicardial recordings, the estimation error increases in the tissue models where fibers in the epicardial and endocardial layers run perpendicularly. The effect on the estimate of such architecture, characteristic of the inferior side of the right atria and the ventricles, can be accounted for by combining epicardial electrical recordings obtained after pacing either in the endocardium or the epicardium. Although a preliminary assessment of the estimation methods was carried out with human EGMs, future studies should focus on validating the methods in a controlled experimental framework and refining them for more localized fiber direction estimation. All in all, the automation of the techniques and their integration into electrophysiological models brings us a step closer to creating valuable clinical tools for diagnosing and treating electropathologies.