根据CNN训练的MelSpectrum和Log-MelSpectrum特征对心脏声音信号进行分类
Wei Chen1,2, Zixuan Zhou2, Junze Bao1
1Medical School, Nantong University, Nantong 226001, China.
Bioengineering (Basel, Switzerland)
|June 28, 2023
概括
记录Mel频率 cepstral 系数 (Log-MelSpectrums) 提高卷积神经网络 (CNN) 的性能,用于心声分类. 这项分析表明,Log-MelSpectrums减少了分类差异,提高了心血管疾病的诊断.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 准确的心声分类有助于快速诊断心血管疾病.
- 卷积神经网络 (CNN) 在使用Mel频率 cepstral 系数 (MelSpectrums) 和log Mel频率 cepstral 系数 (Log-MelSpectrums) 的心声分类方面表现有前途.
- 基于CNN的心声分类的最佳输入表示仍然不清楚.
研究的目的:
- 理论分析和实验验证CNN基于心脏声音分类的最佳输入表示.
- 为了比较MelSpectrums与Log-MelSpectrums在表示心脏声音信号方面的有效性.
- 调查这些特征如何影响CNN模型在分类心脏声音中的表现.
主要方法:
- 利用短时间里叶变换 (STFT) 来从心脏声音信号中推导 MelSpectrums 和 Log-MelSpectrums.
- 结合特征提取与心脏声音获取的数学模型进行理论分析.
- 训练和评估CNN模型,使用MelSpectrum和Log-MelSpectrum在分段心脏声音框架上的功能.
主要成果:
- 理论分析和实验结果表明Log-MelSpectrums的性能优越.
- 据Log-MelSpectrums证明,不同领域的分类差异有所减少.
- 在Log-MelSpectrums上训练的CNN模型在心脏声音分类中获得了更高的准确性.
结论:
- 对于基于CNN的心脏声音分类,Log-MelSpectrum的功能比MelSpectrums更有效.
- 使用Log-MelSpectrums可以提高心血管诊断系统的稳定性和准确性.
- 这项研究提供了优化心脏声音分析的深度学习模型中的特征选择的证据.
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