使用基于深度学习和优化的方法论进行心律失常的分类
Suvita Rani Sharma1, Birmohan Singh1, Manpreet Kaur2
1Department of Computer Science and Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, India.
Journal of medical engineering & technology
|February 14, 2025
概括
这项研究引入了一种先进的电心电图 (ECG) 信号分类方法,通过深度学习和一种新的特征选择技术,在识别五种不同心律时达到99.31%的准确性.
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:
- 电心电图 (ECG) 信号分析对于诊断心脏病状况至关重要.
- 传统方法面临着噪音和准确分类的挑战.
- 深度学习为改进心电图解释提供了潜力.
研究的目的:
- 开发一种强大的方法来分类五种类型的心电图信号.
- 通过消除噪音和基线流动来提高心电图信号质量.
- 为了实现高精度的自动化心电图节拍分类.
主要方法:
- 使用移动平均线过器和离散波纹转换进行心电图信号预处理.
- 从预处理的信号中形成图像 (灰度和刻度图).
- 通过EfficientNet-B0深度学习模型提取功能.
- 使用混合方法的特征选择,结合过方法和自适应搜索 (SABES) 优化.
- 用于特征缩放的Z分数规范化.
主要成果:
- 通过R峰检测成功对ECG信号进行细分.
- 有效降低噪音,包括基线流浪和电源线干扰.
- 五个ECG信号类别的高精度分类,达到99.31%的精度.
- 证明了EfficientNet-B0模型和SABES优化的有效性.
结论:
- 拟议的方法提供了对ECG信号分类的有效和准确方法.
- 深度学习和高级功能选择的整合大大提高了诊断能力.
- 这项工作为自动心律分析提供了有价值的工具.
相关概念视频
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