Diagnosis of atrial fibrillation based on AI-detected anomalies of ECG segments.

ABSTRACT: Early detection of atrial fibrillation (AF) is crucial for its effective management and prevention. Various methods for detecting AF using deep learning (DL) based on supervised learning with a large labeled dataset have a remarkable performance. However, supervised learning has several problems, as it is time-consuming for labeling and has a data dependency problem. Moreover, most of the DL methods do not provide any clinical evidence to physicians regarding the analysis of electrocardiography (ECG) for classification or detection of AF. To address these limitations, in this study, we proposed a novel AF diagnosis system using unsupervised learning for anomaly detection with three segments, PreQ, QRS, and PostS, based on the normal ECG. Two independent datasets, PTB-XL and China, were use

SUBMITTER: Choi S 

PROVIDER: S-EPMC10770559 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature