[2511.22935] EnECG: Efficient Ensemble Learning for Electrocardiogram Multi-task Foundation Model

Computer Science > Machine Learning arXiv:2511.22935 (cs) [Submitted on 28 Nov 2025 (v1), last revised 3 Mar 2026 (this version, v2)] Title:EnECG: Efficient Ensemble Learning for Electrocardiogram Multi-task Foundation Model Authors:Yuhao Xu, Xiaoda Wang, Jiaying Lu, Sirui Ding, Defu Cao, Huaxiu Yao, Yan Liu, Xiao Hu, Carl Yang View a PDF of the paper titled EnECG: Efficient Ensemble Learning for Electrocardiogram Multi-task Foundation Model, by Yuhao Xu and Xiaoda Wang and Jiaying Lu and Sirui Ding and Defu Cao and Huaxiu Yao and Yan Liu and Xiao Hu and Carl Yang View PDF HTML (experimental) Abstract:Electrocardiogram (ECG) analysis plays a vital role in the early detection, monitoring, and management of various cardiovascular conditions. While existing models have achieved notable success in ECG interpretation, they fail to leverage the interrelated nature of various cardiac abnormalities. Conversely, developing a specific model capable of extracting all relevant features for multiple ECG tasks remains a significant challenge. Large-scale foundation models, though powerful, are not typically pretrained on ECG data, making full re-training or fine-tuning computationally expensive. To address these challenges, we propose EnECG(Mixture of Experts-based Ensemble Learning for ECG Multi-tasks), an ensemble-based framework that integrates multiple specialized foundation models, each excelling in different aspects of ECG interpretation. Instead of relying on a single model or si...