Qi Yiqiu, Li Guangyuan, Yang Jinzhu, Li Honghe, Yu Qi, Qu Mingjun, Ning Hongxia, Wang Yonghuai
Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang, China.
Department of Cardiovascular Ultrasound, The First Hospital of China Medical University, China; Clinical Medical Research Center of Imaging in Liaoning Province, Shenyang, China.
Artif Intell Med. 2025 Feb;160:103065. doi: 10.1016/j.artmed.2024.103065. Epub 2025 Jan 3.
Left ventricular systolic dysfunction (LVSD) and its severity are correlated with the prognosis of cardiovascular diseases. Early detection and monitoring of LVSD are of utmost importance. Left ventricular ejection fraction (LVEF) is an essential indicator for evaluating left ventricular function in clinical practice, the current echocardiography-based evaluation method is not avaliable in primary care and difficult to achieve real-time monitoring capabilities for cardiac dysfunction. We propose a two-branch deep learning model (ECGEFNet) for calculating LVEF using electrocardiogram (ECG), which holds the potential to serve as a primary medical screening tool and facilitate long-term dynamic monitoring of cardiac functional impairments. It integrates original numerical signal and waveform plots derived from the signals in an innovative manner, enabling joint calculation of LVEF by incorporating diverse information encompassing temporal, spatial and phase aspects. To address the inadequate information interaction between the two branches and the lack of efficiency in feature fusion, we propose the fusion attention mechanism (FAT) and the two-branch feature fusion module (BFF) to guide the learning, alignment and fusion of features from both branches. We assemble a large internal dataset and perform experimental validation on it. The accuracy of cardiac dysfunction screening is 92.3%, the mean absolute error (MAE) in LVEF calculation is 4.57%. The proposed model performs well and outperforms existing basic models, and is of great significance for real-time monitoring of the degree of cardiac dysfunction.
左心室收缩功能障碍（LVSD）及其严重程度与心血管疾病的预后相关。早期检测和监测LVSD至关重要。左心室射血分数（LVEF）是临床实践中评估左心室功能的重要指标，目前基于超声心动图的评估方法在基层医疗中不可用，且难以实现对心脏功能障碍的实时监测能力。我们提出了一种用于使用心电图（ECG）计算LVEF的双分支深度学习模型（ECGEFNet），它有潜力作为一种初级医疗筛查工具，并有助于对心脏功能损害进行长期动态监测。它以创新的方式整合了原始数值信号和从信号中导出的波形图，通过纳入包括时间、空间和相位方面的各种信息来实现LVEF的联合计算。为了解决两个分支之间信息交互不足以及特征融合效率低下的问题，我们提出了融合注意力机制（FAT）和双分支特征融合模块（BFF）来指导两个分支的特征学习、对齐和融合。我们组装了一个大型内部数据集并在其上进行实验验证。心脏功能障碍筛查的准确率为92.3%，LVEF计算中的平均绝对误差（MAE）为4.57%。所提出的模型表现良好，优于现有的基础模型，对于实时监测心脏功能障碍程度具有重要意义。
