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基于GAN的新型特征选择方法与混合深度学习用于从心电图信号进行心跳分类

S Haseena Beegum¹, R Manju²

¹Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thuckalay, Kanyakumari distrct, Tamil Nadu, India.

Computational biology and chemistry

|October 5, 2025

概括

这项研究引入了一种先进的深度学习模型,用于从心电图 (ECG) 数据中分类心律失常. 新的SExpHGS-DBN-VGG方法在检测异常心跳时实现了高精度,改善了心血管诊断.

科学领域:

心脏病学心脏病学
生物医学工程生物医学工程
人工智能的人工智能

背景情况:

心律不整是严重的心血管疾病,严重程度各不相同.
准确的心律失常检测依赖于心电图 (ECG) 分析.
机器学习为自动ECG解释提供了先进的工具.

研究的目的:

开发一个最佳的深度学习技术来分类心跳.
使用心电图数据提高心律失常检测的准确性和效率.
引入一种新的SExpHGS-DBN-VGG模型用于心跳分类.

主要方法:

使用介质波器和基于波段的技术进行心电图数据预处理.
提取各种特征,包括DWT,自行回归,FrFT和形态特征.
使用肯德尔,包装方法和用GAN使用Kraskov的特征融合,然后用SExpHGS-DBN-VGG进行分类.

主要成果:

拟议的SExpHGS-DBN-VGG模型与传统方法相比显示出更高的性能.
获得了95.7%的精度,97.2%的灵敏度和94.9%的特异性.
验证了集成深度学习方法对心跳分类的有效性.

结论:

开发的深度学习技术为心跳分类提供了高度准确的方法.
这种方法显著推进了用于诊断心律失常的自动化心电图分析.
该SExpHGS-DBN-VGG模型显示出在心血管健康领域临床应用的前景.

关键词:

深度学习是一种深度学习.疾病分类疾病分类.功能融合的特点是:心跳的分类心跳的分类优化优化优化优化

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