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.

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.