{"type":"ScholarlyArticle","title":"Personalized Heart Disease Detection via ECG Digital Twin Generation","authors":[{"name":"Yaojun Hu","slug":"yaojun-hu","given":"Yaojun","family":"Hu","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Jintai Chen","slug":"jintai-chen","given":"Jintai","family":"Chen","affiliation":"Computer Science Department , University of Illinois at Urbana-Champaign"},{"name":"Lianting Hu","slug":"lianting-hu","given":"Lianting","family":"Hu","affiliation":"Medical Big Data Center , Guangdong Provincial People's Hospital , Southern Medical University; Guangdong Cardiovascular Institute , Guangdong Provincial People's Hospital , Guangdong Academy of Medical Sciences; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application , Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences )"},{"name":"Dantong Li","slug":"dantong-li","given":"Dantong","family":"Li","affiliation":"Medical Big Data Center , Guangdong Provincial People's Hospital , Southern Medical University; Guangdong Cardiovascular Institute , Guangdong Provincial People's Hospital , Guangdong Academy of Medical Sciences; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application , Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences )"},{"name":"Jiahuan Yan","slug":"jiahuan-yan","given":"Jiahuan","family":"Yan","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Haochao Ying","slug":"haochao-ying","given":"Haochao","family":"Ying","affiliation":"School of Public Health , Zhejiang University"},{"name":"Huiying Liang","slug":"huiying-liang","given":"Huiying","family":"Liang","affiliation":"Medical Big Data Center , Guangdong Provincial People's Hospital , Southern Medical University; Guangdong Cardiovascular Institute , Guangdong Provincial People's Hospital , Guangdong Academy of Medical Sciences; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application , Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences )"},{"name":"Jian Wu","slug":"jian-wu","given":"Jian","family":"Wu","affiliation":"School of Public Health , Zhejiang University; State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital , Zhejiang University School of Medicine"}],"abstract":"Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention. Most traditional electrocardiogram (ECG) based automated diagnosis methods are trained at population level, neglecting the customization of personalized ECGs to enhance individual healthcare management. A potential solution to address this limitation is to employ digital twins to simulate symptoms of diseases in real patients. In this paper, we present an innovative prospective learning approach for personalized heart disease detection, which generates digital twins of healthy individuals' anomalous ECGs and enhances the model sensitivity to the personalized symptoms. In our approach, a vector quantized feature separator is proposed to locate and isolate the disease symptom and normal segments in ECG signals with ECG report guidance. Thus, the ECG digital twins can simulate specific heart diseases used to train a personalized heart disease detection model. Experiments demonstrate that our approach not only excels in generating high-fidelity ECG signals but also improves personalized heart disease detection. Moreover, our approach ensures robust privacy protection, safeguarding patient data in model development. The code can be found at https://github.com/huyjj/LAVQ-Editor.","doi":"10.24963/ijcai.2024/649","url":"https://ijcai.org/proceedings/2024/649","pdf":"https://ijcai.org/proceedings/2024/0649.pdf","markdown":"https://ijcai.org/proceedings/2024/649.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/649","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":649,"pages":{"first":5872,"last":5881},"keywords":["Machine Learning: ML: Applications","Machine Learning: ML: Generative models","Multidisciplinary Topics and Applications: MTA: Health and medicine"],"videos":[],"references":[{"position":1,"raw":"[Alsentzer et al., 2019] Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jin, Tristan Nau- mann, and Matthew McDermott. 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