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使用可穿戴式心电图和加速仪数据,基于深度学习对姿势正静心动综合征患者的分类

Hyunjun Choi¹, Nicholas Matsumoto¹, Xi Li²

¹Department of Computational Biomedicine, Center for Artificial Intelligence Research and Education, Cedars Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G-541H, West Hollywood, USA www.cedars-sinai.org, AISupport@csmc.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|February 27, 2026

概括

可穿戴式传感器和人工智能可以帮助诊断姿势正静心动综合征 (POTS),这是一种慢性自主性疾病. 这项研究表明,使用心电图和加速度计数据的深度学习模型可以在日常活动中区分POTS患者和健康人群.

科学领域:

心脏病学心脏病学
自主神经科学自主神经科学
生物医学工程生物医学工程

背景情况:

姿势正静心动综合征 (POTS) 是一种复杂的自主性疾病,导致正静心不耐受.
目前的诊断方法,如倾斜表测试有局限性,包括不适和无法捕捉现实世界的症状变化.
需要对POTS提供客观,非侵入性和可扩展的诊断工具.

研究的目的:

研究可穿戴设备在POTS患者中持续收集生理数据的实用性.
开发和评估一种深度学习模型,使用可穿戴传感器数据将POTS患者与健康对照患者区分开来.
探索AI驱动的POTS诊断在日常日常活动中的潜力.

主要方法:

在日常生活中使用可穿戴设备持续收集心电图 (ECG) 和加速度计 (ACC) 数据.
识别和处理与姿势变化相关的生理特征.
在从POTS患者和健康对照组收集的生理数据上培训和测试一个深度学习模型.

主要成果:

深度学习模型在区分POTS患者和健康对照患者方面表现出有希望的准确性.
该模型有效地利用了ECG和ACC数据来识别与POTS相关的生理模式.
使用可穿戴技术和人工智能用于POTS诊断的可行性已在试点队列中确定.

结论:

可穿戴传感器与深度学习相结合,为POTS诊断提供了潜在的非侵入性方法.
这项技术可以补充传统的诊断方法,并促进早期检测.
建议对更大,更多样化的队列进行进一步验证,以提高临床决策支持的模型稳定性.

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