通过连续波波变换和多分支变压器来增强ECG分类
Chenyang Qiu1, Hao Li1, Chaoqun Qi1
1School of Information Technology, Yunnan University, Kunming, China.
Heliyon
|March 4, 2024
概括
这项研究引入了一种新的心电图 (ECG) 分类方法,使用连续波波变换器和多分支变压器,实现心脏病诊断的高精度. 这种方法简化了预处理,并增强了特征提取,以改善心律失常检测.
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
- 信号处理 信号处理
背景情况:
- 准确的心电图 (ECG) 信号分类对于诊断心脏病至关重要.
- 现有的方法通常涉及复杂的预处理,并与高级时间序列特征提取作斗争.
- 卷积神经网络 (CNN) 在捕获复杂的心电图信号模式方面存在局限性.
研究的目的:
- 开发一种先进的ECG分类方法.
- 为了克服传统的基于CNN的方法的局限性.
- 提高自动心脏病诊断的准确性和效率.
主要方法:
- 利用连续波波变换 (CWT) 将ECG信号转换为时间序列特征图,减少预处理需求.
- 引入了多分支变压器架构,以增强功能提取.
- 专注于保留重要的功能,同时删除多余的信息.
主要成果:
- 在CPSC 2018数据集 (6877个案例) 上实现了98.53%的准确性和97.57%的F1得分.
- 在MIT-BIH数据集 (47个案例) 上获得了99.38%的准确性和98.65%的F1评分.
- 在性能方面表现优于大多数现有的ECG分类方法.
结论:
- 拟议的方法准确地对ECG时间序列特征图进行分类.
- 显示出诊断心律不整的显著潜力.
- 为自动ECG诊断系统提供了宝贵的进步.
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