Work

Detecting Left Ventricular Systolic Dysfunction in Left Bundle Branch Block Patients Using Electrocardiogram: A Deep Learning Approach with Limited Data

Year: 2025

Type: article

Abstract: Left ventricular systolic dysfunction (LVSD) is associated with increased mortality and is sometimes reversible when found early. Artificial intelligence (AI)-enabled electrocardiogram (ECG) has emerged as an efficient screening tool for LVSD, but has not been validated in left bundle branch block (LBBB) patients. The clinical significance of developing an AI prediction model for LBBB patients lies in the fact that LBBB can be a cause, consequence, or both of LVSD. This pilot study was designed to develop an AI model for LVSD detection in the LBBB population using a limited dataset. ECG data from 508 patients with sinus rhythm and LBBB were labeled based on an LVSD threshold of 35%. To enhance the performance of a model derived from such a small and skewed dataset, we combined an autoencoder-based anomaly detection model with a convolutional neural network (CNN). We used a lead-wise ensemble technique for the final classification. Experimental results showed an accuracy of 0.81, precision of 0.87, recall of 0.56, and an area under the receiver operating characteristic curve of 0.75 in LVSD prediction among LBBB patients. Despite the limited dataset size, our study findings suggest the potential of deep learning techniques in detecting LVSD in patients with LBBB.

Source: Applied Sciences

DOI: 10.3390/app15158384

Authors: Chanjin Kwon, Hye Bin Gwag, Jongwon Seok

Institutions: Changwon National University, Samsung Changwon Hospital, Sungkyunkwan University

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,896