{"type":"ScholarlyArticle","title":"DiffECG: Diffusion Model-Powered Label-Efficient and Personalized Arrhythmia Diagnosis","authors":[{"name":"Tianren Zhou","slug":"tianren-zhou","given":"Tianren","family":"Zhou","affiliation":""},{"name":"Zhenge Jia","slug":"zhenge-jia","given":"Zhenge","family":"Jia","affiliation":""},{"name":"Dongxiao Yu","slug":"dongxiao-yu","given":"Dongxiao","family":"Yu","affiliation":""},{"name":"Zhaoyan Shen","slug":"zhaoyan-shen","given":"Zhaoyan","family":"Shen","affiliation":""}],"abstract":"Arrhythmia diagnosis using electrocardiogram (ECG) is critical for preventing cardiovascular risks. However, existing deep learning-based methods struggle with label scarcity and contrastive learning-based methods suffer from false-negative samples, which lead to poor model generalization. Besides, due to inter-subject variability, pre-trained models cannot achieve evenly performance across individuals. Conducting model fine-tuning for each individual is computationally expensive and does not guarantee improvement. We propose DiffECG, a diffusion-based self-supervised learning framework for label-efficient and personalized arrhythmia detection. Our method utilizes a diffusion model to extract robust ECG representations, coupled with a novel feature extractor and a multi-modal feature fusion strategy to obtain a well-generalized model. Moreover, we propose an efficient model personalization mechanism based on zeroth-order optimization. It personalizes the model by tuning the noise-adding step t in the diffusion process, significantly reducing computational costs compared to model fine-tuning. Experimental results show that our proposed method outperforms the SOTA method by 37.9% and 23.9% in generalization and personalization performance, respectively. The source code is available at: https://github.com/Auguuust/DiffEC","doi":"10.24963/ijcai.2025/890","url":"https://ijcai.org/proceedings/2025/890","pdf":"https://ijcai.org/proceedings/2025/0890.pdf","markdown":"https://ijcai.org/proceedings/2025/890.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/890","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":890,"pages":{"first":8003,"last":8011},"keywords":["Machine Learning: ML: Applications","Machine Learning: ML: Classification","AI Ethics, Trust, Fairness: ETF: Fairness and diversity","Multidisciplinary Topics and Applications: MTA: Health and medicine"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}