在使用率特征和RR间隔与CNN架构的患者间心电图信号中检测心律失常
Nadia Berrahou1, Abdelmajid El Alami1, Abderrahim Mesbah2
1Faculty of sciences dhar el mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
概括
这项研究引入了一个1D卷积神经网络 (CNN),用于使用心电图 (ECG) 信号检测心律失常,通过结合形态和时间特征来实现高准确度的患者间分类.
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:
- 为了检测心律失常,患者间心电图 (ECG) 分类面临性能挑战,尽管深度学习取得了进展.
- 现有的方法往往难以有效地捕捉心脏信号的形状和时间,以进行准确的分类.
研究的目的:
- 开发一种创新的1D卷积神经网络 (CNN) 模型,用于在心电图数据中增强心律失常检测.
- 利用心脏周期的形态和时间特征来改善患者间心电图分类.
主要方法:
- 使用1D CNN自动从R峰周围的ECG波形中提取形态特征.
- 嵌入RR间隔特征和率用于时间上下文和高级特征提取.
- 在最终心律失常分类的分类层中结合学习和时间特征.
主要成果:
- 在MIT-BIH数据集上,在五个类别中实现了高的患者内分类准确率 (高达99.17%).
- 证明了强大的患者间分类性能,平均准确率为98.73% (3类) 和97.91% (5类).
- 在INCART数据集上验证了概括,在三个类中实现了98.20%的准确性,超过了最先进的模型.
结论:
- 拟议的1D CNN模型有效地整合了形态和时间心电图特征,用于高级心律失常检测.
- 该模型展示了增强的概括能力,为现实世界,不平衡心律失常数据集提供了强大的解决方案.
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