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ShanghaiTech University Knowledge Management System
| Digital-twin-based Online Parameter Personalization for Implantable Cardiac Defibrillators | |
| 2022-07-15 | |
| 会议录名称 | 2022 44TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)
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| ISSN | 2375-7477 |
| 发表状态 | 已发表 |
| DOI | 10.1109/EMBC48229.2022.9871142 |
| 摘要 | Implantable cardioverter defibrillators (ICDs) are developed to provide timely therapies when adverse patient conditions are detected. Device therapies need to be adjusted for individual patients and evolving patient conditions, which can be achieved by adjusting device parameter settings. However, there are no validated clinical guidelines for parameter personalization, especially for patients with complex and rare conditions. In this paper, we propose a reinforcement learning framework for online parameter personalization of ICDs. Heart states can be inferred from ECG signals from ECG patches, which can be used to create a digital twin of the patient. Reinforcement learning then use the digital twin as environment to explore parameter settings with less misdiagnosis. Experiments were performed on three virtual patients with specific and evolving heart conditions, and the result shows that our proposed approach can identify ICD parameter settings that can achieve better performance compared to default parameter settings. Clinical relevance-Patients with ICD and ECG patch can receive periodic ICD parameter adjustments that are appropriate for their current heart conditions |
| 关键词 | Heart Reinforcement learning Electrocardiography Biology Digital twins Defibrillation Guidelines |
| 会议地点 | Glasgow, Scotland, United Kingdom |
| 会议日期 | 11-15 July 2022 |
| URL | 查看原文 |
| 收录类别 | EI |
| 来源库 | IEEE |
| 引用统计 | 正在获取...
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| 文献类型 | 会议论文 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/240493 |
| 专题 | 信息科学与技术学院_硕士生 信息科学与技术学院_PI研究组_江智浩组 |
| 通讯作者 | Jiang, Zhihao |
| 作者单位 | 1.School of Information Science and Technology, ShanghaiTech University, China; 2.University of Manchester, Department of Mathematics, United Kingdom; 3.Shanghai Engineering Research Center of Intelligent Vision and Imaging, China |
| 第一作者单位 | 信息科学与技术学院 |
| 通讯作者单位 | 信息科学与技术学院 |
| 第一作者的第一单位 | 信息科学与技术学院 |
| 推荐引用方式 GB/T 7714 | Lai, Mincai,Yang, Haochen,Gu, Jicheng,et al. Digital-twin-based Online Parameter Personalization for Implantable Cardiac Defibrillators[C],2022. |
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