神经心脏感应 (NCS):一个时间感知模糊的决策框架,用于多轴心心电图分类和心律失常检测
Cheng-Hai He1, Xiao-Li Wang1, Ying Feng1
1School of Electronics and Information, Guangdong Polytechnic Normal University, 293 West Zhongshan Avenue, Tianhe District, Guangzhou, Guangzhou, 510665, China.
Biomedical physics & engineering express
|March 5, 2026
概括
神经心脏感应 (NCS) 框架改进了心电图 (ECG) 的分类,用于检测心律失常. 它的新型深度学习模型NCSN和NCSNF通过更好地分析波形细节和节奏动态来提高准确性.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 准确的心电图 (ECG) 分类对于自动检测心律失常和临床决策支持至关重要.
- 现有的深度学习方法在共同分析ECG信号中的形态模式,多相互作用和时间依赖性方面面临挑战.
- 这种限制阻碍了波形细节,节奏动态和类边界分离的全面表示.
研究的目的:
- 引入NeuroCardioSense (NCS) 框架,这是一种用于增强心电图信号分类的新型深度学习方法.
- 解决现有方法在捕获复杂的心电图信号特征方面的局限性.
- 提高自动心律失常检测系统的准确性和稳定性.
主要方法:
- 开发了NeuroCardioSenseNet (NCSN),使用卷积神经网络 (CNN) 骨干与时间感知门卷积 (TAG-Conv) 和时间感知门机制 (TAGM).
- 引入了NeuroCardioSenseNet-Fusion (NCSNF),这是一个增强的变体,包含一个时间模糊集成模块 (TFIM) 以改善类边界模糊性缓解.
- 在MIT-BIH心律失常数据库上验证了框架.
主要成果:
- NCSN实现了98.77%的患者内和87.82%的患者间准确性.
- NCSNF进一步提高了性能,达到99.16%的患者内和90.85%的患者间准确度.
- 与现有的基线方法相比,NCS框架显示出更高的性能.
结论:
- 该NCS框架有效地解决了当前深度学习模型在ECG分析中的局限性.
- NCSN和NCSNF显著增强了ECG信号中形态模式,多相互作用和时间依赖性的联合表征.
- 提出的方法为自动检测心律失常和临床决策支持提供了有希望的进步.
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