Dataset Information

Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography.

ABSTRACT: Background: We developed a convolutional neural network (CNN) model to detect atrial fibrillation (AF) using the sinus rhythm ECG (SR-ECG). However, the diagnostic performance of the CNN model based on different ECG leads remains unclear. Methods and Results: In this retrospective analysis of a single-center, prospective cohort study, we identified 616 AF cases and 3,412 SR cases for the modeling dataset among new patients (n=19,170). The modeling dataset included SR-ECGs obtained within 31 days from AF-ECGs in AF cases and SR cases with follow-up ≥1,095 days. We evaluated the CNN model's performance for AF detection using 8-lead (I, II, and V1-6), single-lead, and double-lead ECGs through 5-fold cross-validation. The CNN model achieved an area under the curve (AUC) of 0.872 (95% confidence interval (CI): 0.856-0.888) and an odds ratio of 15.24 (95% CI: 12.42-18.72) for AF detection using the eight-lead ECG. Among the single-lead and double-lead ECGs, the double-lead ECG using leads I and V1 yielded an AUC of 0.871 (95% CI: 0.856-0.886) with an odds ratio of 14.34 (95% CI: 11.64-17.67). Conclusions: We assessed the performance of a CNN model for detecting AF using eight-lead, single-lead, and double-lead SR-ECGs. The model's performance with a double-lead (I, V1) ECG was comparable to that of the 8-lead ECG, suggesting its potential as an alternative for AF screening using SR-ECG.

SUBMITTER: Suzuki S

PROVIDER: S-EPMC10920024 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography.

Suzuki Shinya S Motogi Jun J Umemoto Takuya T Hirota Naomi N Nakai Hiroshi H Matsuzawa Wataru W Takayanagi Tsuneo T Hyodo Akira A Satoh Keiichi K Arita Takuto T Yagi Naoharu N Kishi Mikio M Semba Hiroaki H Kano Hiroto H Matsuno Shunsuke S Kato Yuko Y Otsuka Takayuki T Hori Takayuki T Matsuhama Minoru M Iida Mitsuru M Uejima Tokuhisa T Oikawa Yuji Y Yajima Junji J Yamashita Takeshi T

Circulation reports 20240227 3

Background: We developed a convolutional neural network (CNN) model to detect atrial fibrillation (AF) using the sinus rhythm ECG (SR-ECG). However, the diagnostic performance of the CNN model based on different ECG leads remains unclear. Methods and Results: In this retrospective analysis of a single-center, prospective cohort study, we identified 616 AF cases and 3,412 SR cases for the modeling dataset among new patients (n=19,170). The modeling dataset included SR- ...[more]

PMID: 38464990

Similar Datasets

Project description:GSE2240 contains two different experimental subsets: 1) Comparison of atrial and ventricular gene expression (atrial tissue of patients with sinus rhythm vs. human left ventricular non-failing myocardium) The purpose of our investigation was to identify the transcriptional basis for ultrastructural and functional specialization of human atria and ventricles. Using exploratory microarray analysis (Affymetrix U133A+B), we detected 11,740 transcripts expressed in human heart, representing the most comprehensive report of the human myocardial transcriptome to date. Variation in gene expression between atria and ventricles accounted for the largest differences in this data set, as 3.300 and 2.974 transcripts showed higher expression in atria and ventricles, respectively. Functional classification based on Gene Ontology identified chamber-specific patterns of gene expression and provided molecular insights into the regional specialization of cardiomyocytes, correlating important functional pathways to transcriptional activity: Ventricular myocytes preferentially express genes satisfying contractile and energetic requirements, while atrial myocytes exhibit specific transcriptional activities related to neurohumoral function. In addition, several pro-fibrotic and apoptotic pathways were concentrated in atrial myocardium, substantiating the higher susceptibility of atria to programmed cell death and extracellular matrix remodelling observed in human and experimental animal models of heart failure. Differences in transcriptional profiles of atrial and ventricular myocardium thus provide molecular insights into myocardial cell diversity and distinct region-specific adaptations to physiological and pathophysiological conditions (Barth AS et al., Eur J Physiol, 2005). 2) Comparison of atrial gene expression in patients with permanent atrial fibrillation and sinus rhythm. Atrial fibrillation is associated with increased expression of ventricular myosin isoforms in atrial myocardium, regarded as part of a dedifferentiation process. Whether re-expression of ventricular isoforms in atrial fibrillation is restricted to transcripts encoding for contractile proteins is unknown. Therefore, this study compares atrial mRNA expression in patients with permanent atrial fibrillation to atrial mRNA expression of patients with sinus rhythm as well as to ventricular gene expression using Affymetrix U133 arrays. In atrial myocardium, we identified 1.434 genes deregulated in atrial fibrillation, the majority of which, including key elements of calcium-dependent signaling pathways, displayed down-regulation. Functional classification based on Gene Ontology provided the specific gene sets of the interdependent processes of structural, contractile and electrophysiological remodeling. In addition, we demonstrate for the first time a prominent up-regulation of transcripts involved in metabolic activities, suggesting an adaptive response to an increased metabolic demand in fibrillating atrial myocardium. Ventricular-predominant genes were five times more likely to be up-regulated in atrial fibrillation (174 genes up-regulated, 35 genes down-regulated), while atrial-specific transcripts were predominantly down-regulated (56 genes up-regulated, 564 genes down-regulated). Overall, in atrial myocardium, functional classes of genes characteristic of ventricular myocardium were found to be up-regulated (e.g. metabolic processes) while functional classes predominantly expressed in atrial myocardium were down-regulated in atrial fibrillation (e.g. signal transduction and cell communication). Therefore, dedifferentiation with adoption of a ventricular-like signature is a general feature of the fibrillating atrium, uncovering the transcriptional response pattern in pmAF (Barth AS et al., Circ Res, 2005). Keywords = human myocardium Keywords = atrial fibrillation Keywords = sinus rhythm Keywords = left ventricular gene expression Keywords: other

Dataset Information

Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography.

Publications

Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets