集成PCG光谱图纹理和深度特征用于诊断心力衰竭与保存的喷射分数使用异质堆叠合体学习
Yineng Zheng1,2, Jian Qin3, Fajin Lv1,2
1Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Frontiers in physiology
|February 2, 2026
概括
一个新的集体学习模型有效地检测心力衰竭与保存的喷射分数 (HFpEF) 使用声心图 (PCG) 分析. 这种方法结合了纹理和深度特征,以改善计算机辅助诊断.
科学领域:
- 心脏病学和人工智能的人工智能
- 生物医学信号处理
- 医疗保健中的机器学习
背景情况:
- 保存喷射分数 (HFpEF) 的心力衰竭是一个关键的临床挑战.
- 精确检测HFpEF对于有效的慢性心力衰竭管理至关重要.
- 心电图 (PCG) 信号提供了有价值的诊断信息.
研究的目的:
- 为HFpEF检测开发一种新的异质堆叠集体学习模型.
- 为了融合心电图 (PCG) 谱图纹理和深度特征,提高诊断准确度.
- 评估模型的性能与基线和现有的深度学习方法相比.
主要方法:
- 使用Gammatone过器将PCG信号转换为2D光谱图.
- 开发了四种基本模型:纹理分析 (SVM与RFE) 和转移学习 (ResNet50,InceptionResNetV2,DenseNet121与RF-PCA).
- 一个异质堆叠组合模型与一个MLP元学习者集成的基础模型输出用于分类.
主要成果:
- 拟议的整体模型实现了高性能指标:AUC为0.933,准确度为0.902,灵敏度为0.958,特异性为0.843,精度为0.968,F1得分为0.923.
- 对HFpEF检测的基线和常见深度学习模型进行了持续的改进.
- 验证了基于PCG的HFpEF分析整体策略的有效性.
结论:
- 新型异质堆叠集体学习模型显示了计算机辅助诊断HFpEF的巨大潜力.
- 通过集体学习,PCG纹理和深度特征的融合增强了诊断能力.
- 这种方法为改善慢性心力衰竭的临床评估提供了一个有希望的途径.
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