推进心脏诊断:使用EGOLF网络模型进行高精度心律失常分类
Deepika Tenepalli1, T M Navamani1
1School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India.
Frontiers in physiology
|July 14, 2025
概括
一个新的模型,EGOLFNet,使用心电图 (ECG) 数据准确检测心律不整. 这种带有LSTM融合网络的增强灰狼优化实现了99.61%的准确性,改善了心脏诊断系统.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 信号处理 信号处理
背景情况:
- 检测心律失常对于管理心律障碍至关重要.
- 现有的方法需要提高准确性和稳定性.
研究的目的:
- 开发和验证一个新的深度学习模型,EGOLFNet,用于准确的心律失常分类.
- 通过使用先进的AI技术,提高心脏病诊断能力.
主要方法:
- 使用MIT-BIH心律失常数据库进行培训和验证.
- 员工增强的灰狼优化,以实现最佳的特征选择.
- 集成的LSTM层,以捕捉心电图信号中的时间依赖.
- 预处理的心电图数据,包括正常化和噪声过.
主要成果:
- 在心律失常分类中达到99.61%的高精度.
- 证明了EGOLFNet在识别心律失常方面的有效性.
- 展示了该模型的可靠性和强度.
结论:
- EGOLFNet在自动检测心律失常方面取得了重大进展.
- 该模型显示了集成到临床心脏病诊断系统的潜力.
- 这种方法为及时的医疗干预提供了一个高度可靠的工具.
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