基于心电图的自动深度学习,用于早期识别和分类心血管疾病
Anand Pandey1, Ajeet Singh2, Prasanthi Boyapati3
1Department of Computer Science and Application, SSET, Sharda University, Greater Noida, India.
概括
这项研究引入了一种自动化的深度学习系统,用于使用心电图 (ECG) 检测心血管疾病 (CVD). 该FFNN-CQNGT模型实现了高精度,为早期心血管疾病识别和患者护理提供了一个有前途的工具.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 心脏病是全球主要的死亡原因,需要及时诊断.
- 电心电图 (ECG) 对于识别心血管问题至关重要.
- 深度学习 (DL) 显示出快速ECG异常检测的潜力,但自动CVD检测仍然具有挑战性.
研究的目的:
- 通过整合基于症状的检测和心电图分析来加强心血管疾病诊断.
- 开发一种新的自动预测方法,以提高心电图诊断的准确性.
- 为了利用DL功能与ECG属性进行精确的CVD识别.
主要方法:
- 一个Feed Forward神经网络 (FFNN) 模型被开发用于自动ECG诊断.
- 混沌理论和破坏分析被用来结合DL特征和心电图特征.
- 恒定-Q非静止的加博变换 (CQNGT) 将1D心电图数据转换为2D图像,用于FFNN处理.
主要成果:
- 与最先进的方法相比,FFNN-CQNGT系统表现出卓越的性能.
- 获得了94.89%的精度,95.55%的准确性,93.77%的特异性和93.99%的灵敏性.
- 显示了计算效率,处理时间为2.114 ms,MSE为40.32%.
结论:
- 开发的基于心电图的自动化DL系统 (FFNN-CQNGT) 有助于早期识别心血管疾病.
- 该系统具有显著的潜力,可以改善患者护理和公共卫生结果.
- 该研究通过使用先进的DL技术为自动化CVD分类提供了一种新方法.
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