患者间心电图心跳分类用于心律失常分类:多层感知子与体重囊和序列对序列组合的新方法
Chenchen Zhou1,2, Xiangkui Li1,3, Fan Feng2
1Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China.
Frontiers in physiology
|October 16, 2023
概括
这项研究引入了一种新的方法,将多层感知器 (MLP) 和重量囊 (WCapsule) 网络与序列对序列 (Seq2Seq) 模型相结合,以有效地分类心律失常,即使有不平衡的数据,在没有数据增强的情况下实现高精度.
科学领域:
- 心脏病学 心脏病学
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 心律不整的分类对于诊断心脏疾病至关重要.
- 电心电图 (ECG) 数据中的样本不平衡对准确的分类模型构成重大挑战.
- 现有的方法通常依赖于数据增强,这可能会引入偏差.
研究的目的:
- 开发一种新的心律失常分类方法,有效处理不平衡的数据集.
- 在不使用数据增强技术的情况下提高分类性能.
- 在MIT-BIH心律失常数据库上评估拟议方法的有效性.
主要方法:
- 一个新的多层感知器 (MLP) 块被开发并集成到重量囊 (WCapsule) 网络中.
- 将WCapsule-MLP架构与一个序列对序列 (Seq2Seq) 模型相结合,用于心律失常的分类.
- 该模型使用MIT-BIH心律失常数据库进行训练和评估,并遵守AAMI标准.
主要成果:
- 拟议的模型在不平衡的心电图数据上实现了99.88%的整体准确性 (ACC).
- 在不同的心律失常类 (N,S,V,F) 中报告了高灵敏度 (SEN),正预测值 (PPV) 和特异性 (SPEC).
- 即使训练数据减少 (样本的一半),该模型也表现出与传统算法相比,对N类和V类的灵敏度更高.
结论:
- 结合的MLP,WCapsule和Seq2Seq网络有效地解决了在心律失常分类中的样本失衡问题.
- 拟议的方法在分类心律不整时表现出强大的性能和高准确性.
- 这种方法显示出在有限的培训样本中可靠分类的巨大潜力.
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