基于转移学习的心脏声检测在声心图信号使用光谱图
Pratibha Dohare1, Unmesh Shukla2, Diptadeep Bhattacharjee3
1Cluster Innovation Centre, University of Delhi, Delhi, India.
Computer methods in biomechanics and biomedical engineering
|December 2, 2025
概括
这项研究通过使用转移学习在心电图 (PCG) 信号中增强了心脏声检测. 连续波形变换 (CWT) 谱图和VGG19模型的最佳组合实现了89.44%的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 心脏是异常的心脏声音,需要精确的检测.
- 心电图 (PCG) 信号提供了有价值的诊断信息.
- 自动检测心脏声可以提高诊断效率.
研究的目的:
- 调查转移学习架构在PCG信号中检测心脏声的有效性.
- 为了提高准确性,比较不同的特征提取技术和深度学习模型.
- 为了优化信号预处理以提高声识别.
主要方法:
- 使用第四阶段的Butterworth带通波器和Savitzky-Golay波器,对PCG信号进行排斥.
- 使用短时间里叶变换 (STFT),Mel频 Cepstral 系数 (MFCC) 和连续波纹变换 (CWT) 来生成光谱图.
- 培训VGG16,VGG19,ResNet50和InceptionV3模型在生成的光谱图上进行二进制分类.
主要成果:
- 结合CWT光谱图和VGG19模型,实现了最高准确度的89.44%.
- 优化的信号意味着显著改善了分类性能.
- 各种光谱图和转移学习模型组合显示出卓越的精度,回忆,F1得分和ROC-AUC.
结论:
- 转移学习架构显示出来自PCG信号的自动心脏声检测的显著前景.
- 与VGG19等深度学习模型相结合的CWT光谱图提供了一个强大的方法.
- 进一步的研究可以完善这些方法用于临床应用.
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