深度学习融合框架用于使用原始心脏声音信号自动检测冠状动脉疾病
YunFei Dai1, PengFei Liu2, WenQing Hou3
1College of Information Science and Technology, Shihezi University, Shihezi, Xinjiang, 832000, China.
Heliyon
|September 12, 2024
概括
早期发现冠状动脉疾病 (CAD) 是至关重要的. 这项研究引入了一种新的深度学习融合框架,使用原始心脏声音进行非侵入性CAD检测,实现高精度.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
背景情况:
- 冠状动脉疾病 (CAD) 是一种普遍的心血管疾病,需要早期诊断.
- 目前使用心声进行CAD的计算机辅助检测方法通常依赖于对图形表示的主观专家分析.
- 需要客观,准确的非侵入性方法来早期检测CAD.
研究的目的:
- 开发和验证一种新的融合框架,用于使用原始心脏声音信号进行非侵入性冠状动脉疾病检测.
- 克服当前CAD检测方法中基于专家的主观分析的局限性.
- 通过深度学习整合多领域和医疗多领域功能,以改善CAD诊断.
主要方法:
- 构建了一个融合框架,结合了两个CAD检测模型:一个多域特征模型和一个医疗多域特征融合模型.
- 收集了400名参与者的心脏声音信号.
- 该框架提取了206个多域特征和126个医疗多域特征,将它们与一维深度学习特征合并.
主要成果:
- 多域特征模型实现曲线下的面积 (AUC) 为94.7%.
- 医学多领域特征融合模型的AUC达到92.7%.
- 融合框架在通过深度学习整合各种心脏声音特征方面表现出有效性.
结论:
- 拟议的融合框架为早期检测冠状动脉疾病提供了一种有效的非侵入性解决方案.
- 深度学习算法可以成功地整合一维和跨域心声特征,以提高诊断准确度.
- 这种方法减少了依赖专家的主观性在心脏声音的CAD诊断.
相关概念视频
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Transthoracic Echocardiography (TTE)
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