一个基于注意力聚合和适应性的多层次特征融合的神经网络模型,用于自动检测心律失常
Yushuai Wang1,2, Hao Dong1,2, Haitao Wu2,3
1School of Computer Science, Zhongyuan University of Technology, Henan, China.
Computer methods in biomechanics and biomedical engineering
|March 20, 2025
概括
这项研究引入了一种用于检测心律失常的新型神经网络,提高了具有挑战性的患者间模式的准确性. 该模型有效地识别异常心律,增强心血管疾病诊断.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 心血管疾病的发病率正在上升,因此及时检测心律失常至关重要.
- 电心电图 (ECG) 对于诊断和监测心脏健康至关重要.
- 目前的患者间自动化心律失常检测面临着手动功能和检测异常的挑战.
研究的目的:
- 提出一种神经网络模型,以增强在患者间模式中对异常心律失常类别的自动检测.
- 通过关注关键的ECG特征和解决信号尺度差异来提高性能.
主要方法:
- 开发了一个神经网络模型,结合了注意力聚合 (AP) 和自适应多层特征融合 (AMFF).
- AP专注于关键的道和空间特征,减少冗余的信息.
- 适应性地融合多层ECG特征以增强模型表达能力.
主要成果:
- 在患者内部模式中达到99.32%的准确性,在患者间模式中达到93.35%的准确性 (AAMI标准,MIT-BIH数据库).
- 在患者间模式内在N类分类和异常检测 (S,V,F) 中表现强.
- 该模型在不同的心律失常类别中表现出平衡的性能.
结论:
- 拟议的AP和AMFF神经网络模型显著提高了患者间心律失常检测性能.
- 该模型有效地解决了检测异常类别和心电图信号尺度变化的挑战.
- 这种方法为自动心律失常诊断提供了更强大,更平衡的解决方案.
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