使用卷积神经网络与富里埃转换模块的声心图细分的增强
Changhyun Park1,2, Keewon Shin3, Jinew Seo2
1Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Biomedical engineering letters
|March 3, 2025
概括
这项研究引入了新的卷积里埃 (CF) 模块,用于在心电图 (PCG) 信号中精确识别心脏声音 (S1和S2),改善心脏诊断. 深度学习方法准确地细分心脏声音,即使有声和噪音.
科学领域:
- * 生物医学工程 * 生物医学工程
- * * 信号处理 信号处理
- * 医学中的人工智能
背景情况:
- *在心电图 (PCG) 信号中准确识别心脏声音 (S1和S2) 对于诊断心脏膜疾病至关重要.
- *传统方法难以区分心脏声音和背景噪音,限制了诊断的准确性.
- * 深度学习为强大的PCG信号分析提供了潜力.
研究的目的:
- * 开发和评估基于深度学习的细分方法,用于在PCG信号中准确识别S1和S2心声.
- * 在心脏声和背景噪音的情况下,增强心脏声音细分的稳定性.
- * 将拟议方法的性能与现有的最先进模型进行比较.
主要方法:
- * 提出了一种新的深度学习架构,利用连接顺序的卷积里埃 (CF) 模块进行PCG信号分割.
- *通过内部数据集和PhysioNet 2016数据集进行CF模块的培训和验证.
- *对外部数据集的模型有效性进行了评估,包括PhysioNet 2022和阿桑医疗中心 (AMC) 数据集.
- *与后勤回归隐藏半马尔科夫模型 (LR-HSMM) 的性能比较.
主要成果:
- *CF模块在S1和S2细分中获得了97.64%的F1平均得分.
- * 与LR-HSMM模型相比,在细分心脏声音方面显著提高了准确性和稳定性.
- * 在心脏声和背景噪音中成功地保持了性能.
结论:
- * 拟议的CF模块为PCG信号中的S1和S2心声细分提供了高度准确和强大的方法.
- *这种深度学习方法代表了自动心脏诊断的重大进步.
- *该方法有望改善心脏门疾病的早期检测和管理.
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