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相关实验视频

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先进的耐噪电心电图分类使用混合波段-中位数排斥和卷积神经网络.

Aditya Pal1, Hari Mohan Rai2, Saurabh Agarwal3

  • 1Department of Information Technology, Dronacharya Group of Institutions, Greater Noida 201306, India.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
概括

这项研究引入了对心电图 (ECG) 信号的混合消噪方法,显著提高了心血管诊断的准确性. 增强的信号处理提高了关键心脏护理应用的分类性能.

关键词:
电脑心电图信号分类 电脑心电图信号分类生物医学信号处理监测心脏健康 监测心脏健康无雾化技术的使用修改的轻量级MLCNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN减少ECG中的噪声.

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科学领域:

  • 生物医学工程 生物医学工程
  • 信号处理 信号处理
  • 心脏病学 心脏病学

背景情况:

  • 电心电图 (ECG) 信号分类对于诊断心脏疾病至关重要.
  • 信号采集可以引入噪音,影响诊断准确度.
  • 有效的消噪对于可靠的心电图分析至关重要.

研究的目的:

  • 通过使用和评估各种无声化技术,提高心电图信号分类的准确性.
  • 通过先进的信号处理,提高心血管诊断的可靠性.
  • 为ECG信号引入一种新的混合无声化方法.

主要方法:

  • 在心电图数据中模拟现实的噪声 (高斯式,盐和胡,斑点,均,指数式).
  • 应用的无色化方法:波波变换,中间波器,高斯波器和混合波波-中间波器.
  • 使用修改的轻量级卷积神经网络 (CNN) 或MLCNN进行信号分类.

主要成果:

  • 混合波形-中介波器表现出优于单个方法的性能,其低平均平方误差 (MSE) 为0.0012和平均绝对误差 (MAE) 为0.025.02,证明了这一点.
  • 高R平方 (0.98) 和皮尔森相关系数 (0.99) 证实了混合方法在保存心电图特征方面的有效性.
  • 使用无声数据进行分类,与无声数据 (0.80,0.78,0.82,0.80,分别) 相比,获得了显著更高的准确性 (0.92),精度 (0.91),回忆 (0.90) 和F1得分 (0.91).

结论:

  • 拟议的混合消噪技术有效地消除噪声,同时保持基本的心电图信号特征.
  • 通过无声化改善心电图信号质量,直接提高心脏诊断的准确性.
  • 这项研究强调了信号预处理对于临床环境中可靠的实时心电图分析的重要性.