[基于心肌梗塞心电图的智能辅助诊断方法的审查]
Chuang Han1, Wenge Que2, Zhizhong Wang3
1School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, P. R. China.
概括
本研究回顾了使用心电图 (ECG) 的心肌梗塞 (MI) 智能诊断方法. 它强调了机器学习和深度学习方法,以更快,更准确地检测心脏病发作,改善临床结果.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 心肌梗塞 (MI) 由于高死亡率,突然发作和诊断困难,包括延迟,误诊和错误诊断等,带来了重大的临床挑战.
- 电心电图 (ECG) 是心肌梗塞的关键,快速诊断工具,强调了智能辅助诊断系统的重要性.
研究的目的:
- 综合审查和总结基于心电图分析的MI的智能辅助诊断方法.
- 探索机器学习和深度学习在基于心电图的MI诊断中的应用.
- 确定当前的挑战和该领域的未来研究方向.
主要方法:
- 对心脏病发作和特征性心电图变化的病理生理机制的审查.
- 关于心电图特征点提取和形态识别技术的总结.
- 分析机器学习和深度学习模型用于智能MI辅助诊断.
主要成果:
- 基于模型,数据集,心电图参数,输入模式和评估指标的各种智能诊断方法的比较.
- 确定MI检测中不同方法的优点和弱点.
- 对基于心电图的MI诊断的当前研究环境的综合分析.
结论:
- 智能辅助诊断系统在改善心脏病发作检测的准确性和及时性方面具有显著的前景.
- 未来的研究应该专注于数据增强,高级特征提取 (包括动态特征),模型概括和临床解释性.
- 这些进步预计将为研究人员和临床医生在MI智能辅助诊断领域提供有价值的参考资料.
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