Aims: Areas of conduction inhomogeneity (CI) during sinus rhythm may facilitate the initiation and perpetuation of atrial fibrillation (AF). Currently, no tool is available to quantify the severity of CI. Our aim is to develop and validate a novel tool using unipolar electrograms (EGMs) only to quantify the severity of CI in the atria. Methods and results: Epicardial mapping of the right atrium (RA) and left atrium, including Bachmann's bundle, was performed in 235 patients undergoing coronary artery bypass grafting surgery. Conduction inhomogeneity was defined as the amount of conduction block. Electrograms were classified as single, short, long double (LDP), and fractionated potentials (FPs), and the fractionation duration of non-single potentials was measured. The proportion of low-voltage areas (LVAs, <1mV) was calculated. Increased CI was associated with decreased potential voltages and increased LVAs, LDPs, and FPs. The Electrical Fingerprint Score consisting of RA EGM features, including LVAs and LDPs, was most accurate in predicting CI severity. The RA Electrical Fingerprint Score demonstrated the highest correlation with the amount of CI in both atria (r = 0.70, P < 0.001). Conclusion: The Electrical Fingerprint Score is a novel tool to quantify the severity of CI using only unipolar EGM characteristics recorded. This tool can be used to stage the degree of conduction abnormalities without constructing spatial activation patterns, potentially enabling early identification of patients at high risk of post-operative AF or selection of the appropriate ablation approach in addition to pulmonary vein isolation at the electrophysiology laboratory.@en

Background: An increasing number of wearables are capable of measuring electrocardiograms (ECGs), which may help in early detection of atrial fibrillation (AF). Therefore, many studies focus on automated detection of AF in ECGs. A major obstacle is the required amount of manually labelled data. This study aimed to provide an efficient and reliable method to train a classifier for AF detection using large datasets of real-life ECGs. Method: Human-controlled semi-supervised learning was applied, consisting of two phases: the pre-training phase and the semi-automated training phase. During pre-training, an initial classifier was trained, which was used to predict the classes of new ECG segments in the semi-automated training phase. Based on the degree of certainty, segments were added to the training dataset automatically or after human validation. Thereafter, the classifier was retrained and this procedure was repeated. To test the model performance, a real-life telemetry dataset containing 3,846,564 30-s ECG segments of hospitalized patients (n = 476) and the CinC Challenge 2017 database were used. Results: After pre-training, the average F1-score on a hidden testing dataset was 89.0%. Furthermore, after the pre-training phase 68.0% of all segments in the hidden test set could be classified with an estimated probability of successful classification of 99%, providing an F1-score of 97.9% for these segments. During the semi-automated training phase, this F1-score showed little variation (97.3%–97.9% in the hidden test set), whilst the number of segments which could be automatically classified increased from 68.0% to 75.8% due to the enhanced training dataset. At the same time, the overall F1-score increased from 89.0% to 91.4%. Conclusions: Human-validated semi-supervised learning makes training a classifier more time efficient without compromising on accuracy, hence this method might be valuable in the automated detection of AF in real-life ECGs.@en

Aims: Automated detection of atrial fibrillation (AF) in continuous rhythm registrations is essential in order to prevent complications and optimize treatment of AF. Many algorithms have been developed to detect AF in surface electrocardiograms (ECGs) during the past few years. The aim of this systematic review is to gain more insight into these available classification methods by discussing previously used digital biomarkers and algorithms and make recommendations for future research. Methods: On the 14th of September 2020, the PubMed database was searched for articles focusing on algorithms for AF detection in ECGs using the MeSH terms Atrial Fibrillation, Electrocardiography and Algorithms. Articles which solely focused on differentiation of types of rhythm disorders or prediction of AF termination were excluded. Results: The search resulted in 451 articles, of which 130 remained after full-text screening. Not only did the amount of research on methods for AF detection increase over the past years, but a trend towards more complex classification methods is observed. Furthermore, three different types of features can be distinguished: atrial features, ventricular features, and signal features. Although AF is an atrial disease, only 22% of the described methods use atrial features. Conclusion: More and more studies focus on improving accuracy of classification methods for AF in ECGs. As a result, algorithms become increasingly complex and less well interpretable. Only a few studies focus on detecting atrial activity in the ECG. Developing innovative methods focusing on detection of atrial activity might provide accurate classifiers without compromising on transparency.@en

BACKGROUND AND AIMS: Atrial extrasystoles (AES) provoke conduction disorders and may trigger episodes of atrial fibrillation (AF). However, the direction- and rate-dependency of electrophysiological tissue properties on epicardial unipolar electrogram (EGM) morphology is unknown. Therefore, this study examined the impact of spontaneous AES on potential amplitude, -fractionation, -duration, and low-voltage areas (LVAs), and correlated these differences with various degrees of prematurity and aberrancy. METHODS AND RESULTS: Intra-operative high-resolution epicardial mapping of the right and left atrium, Bachmann's Bundle, and pulmonary vein area was performed during sinus rhythm (SR) in 287 patients (60 with AF). AES were categorized according to their prematurity index (>25% shortening) and degree of aberrancy (none, mild/opposite, moderate and severe). In total, 837 unique AES (457 premature; 58 mild/opposite, 355 moderate, and 154 severe aberrant) were included. The average prematurity index was 28% [12-45]. Comparing SR and AES, average voltage decreased (-1.1 [-1.2, -0.9] mV, P < 0.001) at all atrial regions, whereas the amount of LVAs and fractionation increased (respectively, +3.4 [2.7, 4.1] % and +3.2 [2.6, 3.7] %, P < 0.001). Only weak or moderate correlations were found between EGM morphology parameters and prematurity indices (R2 < 0.299, P < 0.001). All parameters were, however, most severely affected by either mild/opposite or severely aberrant AES, in which the effect was more pronounced in AF patients. Also, there were considerable regional differences in effects provoked by AES. CONCLUSION: Unipolar EGM characteristics during spontaneous AES are mainly directional-dependent and not rate-dependent. AF patients have more direction-dependent conduction disorders, indicating enhanced non-uniform anisotropy that is uncovered by spontaneous AES.@en

(1) Background: Structural remodeling plays an important role in the pathophysiology of atrial fibrillation (AF). It is likely that structural remodeling occurs transmurally, giving rise to electrical endo-epicardial asynchrony (EEA). Recent studies have suggested that areas of EEA may be suitable targets for ablation therapy of AF. We hypothesized that the degree of EEA is more pronounced in areas of transmural conduction block (T-CB) than single-sided CB (SS-CB). This study examined the degree to which SS-CB and T-CB enhance EEA and which specific unipolar potential morphology parameters are predictive for SS-CB or T-CB. (2) Methods: Simultaneous endo-epicardial mapping in the human right atrium was performed in 86 patients. Potential morphology parameters included unipolar potential voltages, low-voltage areas, potential complexity (long double and fractionated potentials: LDPs and FPs), and the duration of fractionation. (3) Results: EEA was mostly affected by the presence of T-CB areas. Lower potential voltages and more LDPs and FPs were observed in T-CB areas compared to SS-CB areas. (4) Conclusion: Areas of T-CB could be most accurately predicted by combining epicardial unipolar potential morphology parameters, including voltages, fractionation, and fractionation duration (AUC = 0.91). If transmural areas of CB indeed play a pivotal role in the pathophysiology of AF, they could theoretically be used as target sites for ablation.@en

(1) Background: Structural remodeling plays an important role in the pathophysiology of atrial fibrillation (AF). It is likely that structural remodeling occurs transmurally, giving rise to electrical endo-epicardial asynchrony (EEA). Recent studies have suggested that areas of EEA may be suitable targets for ablation therapy of AF. We hypothesized that the degree of EEA is more pronounced in areas of transmural conduction block (T-CB) than single-sided CB (SS-CB). This study examined the degree to which SS-CB and T-CB enhance EEA and which specific unipolar potential morphology parameters are predictive for SS-CB or T-CB. (2) Methods: Simultaneous endo-epicardial mapping in the human right atrium was performed in 86 patients. Potential morphology parameters included unipolar potential voltages, low-voltage areas, potential complexity (long double and fractionated potentials: LDPs and FPs), and the duration of fractionation. (3) Results: EEA was mostly affected by the presence of T-CB areas. Lower potential voltages and more LDPs and FPs were observed in T-CB areas compared to SS-CB areas. (4) Conclusion: Areas of T-CB could be most accurately predicted by combining epicardial unipolar potential morphology parameters, including voltages, fractionation, and fractionation duration (AUC = 0.91). If transmural areas of CB indeed play a pivotal role in the pathophysiology of AF, they could theoretically be used as target sites for ablation.@en

Background: Quantified features of local conduction heterogeneity due to pathological alterations of myocardial tissue could serve as a marker for the degree of electrical remodeling and hence be used to determine the stage of atrial fibrillation (AF). Objectives: In this study, the authors investigated whether local directional heterogeneity (LDH) and anisotropy ratio, derived from estimated local conduction velocities (CVs) during AF, are suitable electrical parameters to stage AF. Methods: Epicardial mapping (244-electrode array, interelectrode distance 2.25 mm) of the right atrium was performed during acute atrial fibrillation (AAF) (n = 25, 32 ± 11 years of age) and during long-standing persistent atrial fibrillation (LSPAF) (n = 23, 64 ± 9 years of age). Episodes of 9 ± 4 seconds of AF were analyzed. Local CV vectors were constructed to assess the degree of anisotropy. Directions and magnitudes of individual vectors were compared with surrounding vectors to identify LDH. Results: Compared with the entire AAF group, LSPAF was characterized by slower conduction (71.5 ± 6.8 cm/s vs 67.6 ± 5.6 cm/s; P = 0.037) with a larger dispersion (1.59 ± 0.21 vs 1.95 ± 0.17; P < 0.001) and temporal variability (32.0 ± 4.7 cm/s vs 38.5 ± 3.3 cm/s; P < 0.001). Also, LSPAF was characterized by more LDH (19.6% ± 4.4% vs 26.0% ± 3.4%; P < 0.001) and a higher degree of anisotropy (1.38 ± 0.07 vs 1.51 ± 0.14; P < 0.001). Compared with the most complex type of AAF (type III), LSPAF was still associated with a larger CV dispersion, higher temporal variability of CV, and larger amount of LDH. Conclusions: Increasing AF complexity was associated with increased spatiotemporal variability of local CV vectors, local conduction heterogeneity, and anisotropy ratio. By using these novel parameters, LSPAF could potentially be discriminated from the most complex type of AAF. These observations may indicate pathological alterations of myocardial tissue underlying progression of AF.@en