In-vivo Sino-Atrial Node Mapping in Children and Adults With Congenital Heart Disease

BACKGROUND: Sinus node dysfunction (SND) and atrial tachyarrhythmias frequently co-exist in the aging patient with congenital heart disease (CHD), even after surgical correction early in life. We examined differences in electrophysiological properties of the sino-atrial node (SAN) area between pediatric and adult patients with CHD. METHODS: Epicardial mapping of the SAN was performed during sinus rhythm in 12 pediatric (0.6 [0.4–2.4] years) and 15 adult (47 [40–55] years) patients. Unipolar potentials were classified as single-, short or long double- and fractionated potentials. Unipolar voltage, relative R-to-S-amplitude ratio and duration of all potentials was calculated. Conduction velocity (CV) and the amount of conduction block (CB) was calculated. RESULTS: SAN activity in pediatric patients was solely observed near the junction of the superior caval vein and the right atrium, while in adults SAN activity was observed even up to the middle part of the right atrium. Compared to pediatric patients, the SAN region of adults was characterized by lower CV, lower voltages, more CB and a higher degree of fractionation. At the earliest site of activation, single potentials from pediatrics consisted of broad monophasic S-waves with high amplitudes, while adults had smaller rS-potentials with longer duration which were more often fractionated. CONCLUSIONS: Compared to pediatric patients, adults with uncorrected CHD have more inhomogeneous conduction and variations in preferential SAN exit site, which are presumable caused by aging related remodeling. Long-term follow-up of these patients is essential to demonstrate whether these changes are related to development of SND and also atrial tachyarrhythmias early in life

Digital biomarkers and algorithms for detection of atrial fibrillation using surface electrocardiograms: A systematic review: Digital Biomarkers for AF in Surface ECGs

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

An accurate and efficient method to train classifiers for atrial fibrillation detection in ECGs: Learning by asking better questions

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

Endo-Epicardial Mapping of In Vivo Human Sinoatrial Node Activity

Objectives : The aim of the current study was to examine electrophysiological characteristics of sinoatrial node (SAN) activity from an endo-epicardial perspective. Background : Electrophysiological properties of the in vivo human SAN and its exit pathways remain poorly understood. Methods : Twenty patients (75% male; median age 66 years [59 to 73 years]) with structural heart disease underwent simultaneous endo-epicardial mapping (256 unipolar electrodes, interelectrode distance 2 mm). Conduction times, endo-epicardial delays (EEDs), and R/S ratio were examined in the surrounding 10 mm of SAN activation. Areas of conduction block were defined as conduction delays ≥12 ms and endo-epicardial asynchrony as EED ≥15 m. Results : Three distinct activation patterns were observed in a total of 28 SAN–focal activation patterns (SAN-FAPs) (4 patients exhibited >1 different exit site), including SAN activation patterns with: 1) solely an endocardial exit site (n = 10 [36%]); 2) solely an epicardial exit site (n = 13 [46%]); and 3) simultaneously activated endo-epicardial exit sites (n = 5 [18%]). Median (interquartile range) EED at the origin of the SAN-FAP was 10 ms (6 to 14 ms) and the prevalence of endo-epicardial asynchrony in the surroundings of the SAN-FAP was 5% (2% to 18%). Electrograms at the origin of the SAN-FAPs exhibited significantly larger R-peaks in the mid right atrium (RA) compared with the superior RA (mid R/S ratio 0.15 [0.067 to 0.34] vs. superior R/S ratio 0.045 [0.026 to 0.062]; p = 0.004). Conduction velocity within a distance of 10 mm from the SAN-FAP was 125 cm/s (80 to 250 cm/s). All 6 SAN-FAPs at the mid RA were observed in patients with a history of atrial fibrillation. Conclusions : Variations in activation patterns of the SAN observed in this study highlight the complex 3-dimensional SAN geometry and indicate the presence of interindividual differences in SAN exit pathways. Solely in patients with a history of atrial fibrillation, SAN activity occurred more caudally, which indicates changes in preferential SAN exit pathways