在远程医疗服务中,水平交叉处理和深度卷积神经网络用于心律失常的分类
Syed Fawad Hussain1,2, Saeed Mian Qaisar3,4, Muhammad Sherjeel5
1MDS Lab, Faculty of Computer Science and Engineering, G.I.K Institute, Topi, 23640, Pakistan. s.f.hussain@bham.ac.uk.
Physical and engineering sciences in medicine
|November 3, 2025
概括
一种新的远程医疗方法使用交叉级别模拟数字转换器 (LCADCs) 和深度学习自动诊断心律失常. 这种方法显著减少了数据大小,并提高了实时医疗应用的计算效率.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 信号处理 信号处理
背景情况:
- 远程医疗依赖于无线生物医学设备,在数据压缩,传输,安全和处理方面面临挑战.
- 对心律失常的有效和准确的自动诊断对于有效的患者监测和预后至关重要.
研究的目的:
- 提出一种新,高效和有效的方法,用于在远程医疗保健中自动诊断心律失常.
- 为了实现实时数据大小缩小,计算效率高的信号预定和低延迟准确的分类.
主要方法:
- 拟议的技术整合了交叉水平模拟数字转换器 (LCADC),增强活动选择算法 (EASA),自适应速率过 (ARF) 和一维深卷积神经网络 (CNN).
- 采用电图信号采样水平交叉概念,然后是基于QRS的细分和ARF.
- 被拒绝的部分由1D CNN直接分类,没有手工制作的特征提取.
主要成果:
- 与传统的固定利率方法相比,获得的样本平均减少了4.2倍.
- 由于数据尺寸的缩小,在退化后的阶段证明了超过7.2倍的计算效率.
- 从MIT-BIH数据集中分类五个临床重要心律失常类的准确率达到99%,分类延迟时间显著减少.
结论:
- 拟议的方法为远程医疗保健中的自动心律失常诊断提供了一种高效和有效的解决方案.
- 集成LCADCs,EASA,ARF和1-D CNN显著改善了数据压缩,处理效率和分类准确性.
- 这种方法有望在云连接的医疗环境中实现实时,低延迟和准确的心脏监测.
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