螺旋自编码器-CNN混合模型用于心律失常的分类.
Merve Akkuş1, Murat Karabatak2, Ramazan Tekin1
1Department of Computer Engineering, Batman University, 72100, Batman, Turkey.
Computers in biology and medicine
|June 21, 2025
概括
一个新的深度学习框架结合了修改的螺旋自编码器 (MSCAE) 和卷积神经网络 (CNN) 准确地检测心电图的心律失常. 这种先进的系统达到98.78%的准确性,提高了心律障碍的诊断效率.
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:
- 心律不整是影响血液循环的不规则心律,通常通过心电图 (ECG) 进行诊断.
- 由于其可靠性和成本效益,心电图分析对于诊断心律障碍至关重要.
- 自动心律失常检测系统对于提高临床实践中的诊断效率至关重要.
研究的目的:
- 引入一种新的深度学习框架,用于使用心电图信号对心律失常进行分类.
- 整合一个修改的螺旋自编码器 (MSCAE) 用于特征提取与卷积神经网络 (CNN) 用于分类.
- 在标准心律失常数据库上评估拟议的MSCAE-CNN模型的性能.
主要方法:
- 来自MIT-BIH心律失常数据库的心电图信号被预处理并细分为个别的心跳.
- 修改的螺旋自编码器 (MSCAE) 用于从心电图段中提取深度特征表示.
- 使用卷积神经网络 (CNN) 来捕获抽取的特征中的空间关系,用于心律失常的分类.
主要成果:
- 集成的MSCAE-CNN模型实现了98.78%的高分类准确度.
- 拟议的深度学习框架与现有的心律失常分类方法相比,表现优越.
- 使用MSCAE的特征提取有效地捕获了来自ECG信号的复杂表示,以改进分类.
结论:
- 该MSCAE-CNN框架提供了一个有前途的方法,用于快速和准确的基于心电图的心律失常检测.
- 这种模型具有显著的临床潜力,可以帮助医疗决策诊断心律障碍.
- 深度学习的整合提高了自动心律失常诊断的可靠性和效率.
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