通过使用深度卷积神经网络进行增强的过早心室收缩脉冲检测和分类
Remya Raj1, Ushus S Kumar2, Vivek Maik3
1Department of Biomedical Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India. remyar1@srmist.edu.in.
这项研究引入了一个深度卷积神经网络 (CNN) 用于检测胎儿心律不整,使用光透视图 (PPG) 和动脉血压 (ABP) 信号. 这种新方法在分类早发性心室收缩 (PMVCs) 方面取得了高准确性.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 人工智能在医学中的应用
背景情况:
- 准确的监测系统对于预防心脏疾病至关重要.
- 胎儿心律不整,特别是早发性心室收缩 (PMVCs),需要精确的检测方法.
研究的目的:
- 开发和评估一个深度卷积神经网络 (CNN) 用于分类胎儿心律不整 (PMVCs).
- 为了提高检测准确度,利用光聚缩学 (PPG) 和动脉血压 (ABP) 信号.
主要方法:
- 使用了一个深度卷积神经网络 (CNN) 架构.
- 使用转移学习,使用来自Icentia 11k心电图数据集的权重.
- 通过使用PPG和ABP数据,CNN进行了微调,以改进PMVC的分类.
主要成果:
- 拟议的CNN方法在检测和分类PMVC方面表现出高准确度.
- 对于PMVC的正常,P1和P2类型的分类准确度分别达到99.9%,99.8%和99.5%.
结论:
- 开发的CNN框架有效地检测和分类胎儿心律不整 (PMVCs).
- 将PPG和ABP信号与CNN集成为非侵入性胎儿心脏监测提供了一个有希望的方法.
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