使用心电图 (ECG) 对基于人工智能的心律失常检测进行了全面的审查
Ahtisham Ayyub1, Christos Politis1, Muhammad Arslan Usman1
1Faculty of Engineering, Computing and the Environment (ECE), Kingston University London, KT1 2EE, United Kingdom.
Computers in biology and medicine
|July 8, 2025
概括
本综述分析了用于心电图 (ECG) 分析的人工智能 (AI),提供了对技术,结果和趋势的见解. 它包括统计分析,并确定了人工智能驱动的心电图诊断的未来研究方向.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 人工智能 (AI) 在分析心电图 (ECG) 等生理信号方面显示出显著的前景.
- 现有的关于心电图分析人工智能的评论往往缺乏全面的覆盖,统计深度或严格的质量评估.
- 需要对人工智能在心电图分析中的应用进行更新和深入的审查.
研究的目的:
- 提供对用于ECG分析的AI技术的全面审查.
- 分析使用的方法,结果和该领域的新兴趋势.
- 提供统计见解,并确定未来的研究方向.
主要方法:
- 来自高影响力期刊和顶级会议的219篇研究文章的系统审查.
- 深入分析人工智能技术,性能指标 (精度,灵敏度,特异性,F1分数) 和结果.
- 应用Pearson的相关性来检查性能指标之间的相互关系.
主要成果:
- 确定了关键的AI技术及其在ECG分析中的有效性.
- 提供了AI-ECG研究领域的统计概述.
- 演示了性能指标之间的显著相关性,为模型优化提供了洞察力.
结论:
- 本综述是人工智能和心电图分析研究人员的宝贵资源.
- 统计分析提供了对模型性能和优化策略的更深入理解.
- 确定了挑战,并提出了未来的研究途径,以推进ECG诊断中的AI.
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