使用离散波波变换和注意力增强的CNN-BiGRU模型进行基于ECG的心律失常分类.
Xiaosong He1, Chuanli Hu2, Kai Ma3
1School of Big Data and Artificial Intelligence, Chongqing Institute of Engineering, Chongqing, 400056, China. 04705@cqie.edu.cn.
Physical and engineering sciences in medicine
|September 15, 2025
概括
这项研究引入了一种先进的方法,用于使用心电图 (ECG) 信号对心律不整进行分类. 这种新的方法通过将信号拒绝与深度学习模型相结合,显著提高了检测心血管疾病的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 电心电图 (ECG) 信号噪声阻碍了准确的心律失常分类.
- 通过心电图分析早期发现心血管疾病至关重要.
研究的目的:
- 开发一个强大的基于心电图的心律失常分类系统.
- 通过解决信号噪声和类不平衡来提高分类性能.
主要方法:
- 离散波纹转换 (DWT) 用于ECG信号的消噪.
- 边界-SMOTE用于解决阶级不平衡.
- 注意力增强的卷积神经网络-双向门循环单元 (CNN-BiGRU) 进行分类.
主要成果:
- 在MIT-BIH数据库上对五种心律失常类别的分类中获得了99.22%的准确性.
- 提出的方法优于现有的心律失常检测技术.
- 在消音过程中成功保留了ECG的基本形态特征.
结论:
- DWT和注意力增强CNN-BiGRU模型为自动心律失常检测提供了有效的解决方案.
- 这种方法有望改善心血管疾病的临床诊断.
- 该方法在识别心律失常时具有很高的辨别能力.
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