机器和深度学习在ECG节拍分类方面的进展:系统性审查
Allam Jaya Prakash1, Abdelkader Nasreddine Belkacem2, Ibrahim M Elfadel3
1Electrical and Communication Engineering Department, College of Engineering, United Arab Emirates University, Abu Dhabi, United Arab Emirates.
Frontiers in digital health
|December 15, 2025
概括
本综述探讨了用于心电图 (ECG) 节拍分类的人工智能 (AI),强调了自动化分析的进步,并确定了未来心脏诊断研究的数据不平衡等挑战.
科学领域:
- 心脏病学和医疗信息学
- 医疗保健中的人工智能
- 信号处理 信号处理
背景情况:
- 电心电图 (ECG) 是一个重要的,非侵入性的心脏评估工具.
- 人工智能 (AI) 越来越多地用于自动化心电图分析.
- 电脑心电图节拍分类对于准确的心脏诊断至关重要.
研究的目的:
- 从2014年到2024年,系统地审查人工智能驱动的ECG节拍分类的进展.
- 分析从传统到深度学习方法的演变.
- 确定当前的挑战,并提出未来的研究方向.
主要方法:
- 按照PRISMA标准进行系统的文献审查.
- 分析了2014年至2024年间发表的106篇文章.
- 专注于预处理,功能工程和AI模型架构.
主要成果:
- 从手动特征工程转向使用CNN,RNN和混合模型的自动特征提取.
- 确定的挑战:数据不平衡,患者间的变化,缺乏标准化指标.
- 新兴趋势包括注意力机制和深度学习架构.
结论:
- 人工智能显著提高了ECG节拍分类的准确性和效率.
- 标准化数据集,交叉模式融合和可解释的AI是临床翻译的关键.
- 未来的工作应该集中在解决现实世界部署的当前局限性.
相关概念视频
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An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
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Definition
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Parts of an ECG
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