一个新的功能级融合方案与多式联通注意力CNN为心脏声音分类
Kalpeshkumar Ranipa1, Wei-Ping Zhu1, M N S Swamy1
1Department of Electrical and Computer Engineering, Concordia University, Canada.
Computer methods and programs in biomedicine
|March 20, 2024
概括
一个新的多模式注意力卷积神经网络 (MACNN) 通过选择性地关注关键特征,提高了心声分类的准确性. 这种方法提高了识别心脏病的诊断能力.
科学领域:
- 心脏病学 心脏病学
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 现有的机器学习心声分类方法的准确性有限.
- 这些方法通常依赖于单域特征,缺乏选择性关注.
- 基于卷积神经网络 (CNN) 的特征没有得到有效的利用或融合.
研究的目的:
- 为改进心脏声音分类提出一种新的多式注意力卷积神经网络 (MACNN).
- 将来自Mel-cepstral和一般频率域的各种特征纳入其中.
- 通过选择性聚焦的注意力机制来增强特征提取.
主要方法:
- 开发了一种MACNN,其中包括Mel-cepstral和频域特征的特征级融合.
- 使用DilationAttenNet创建基于注意力的CNN特征提取器.
- 在MACNN中共同优化特征提取器,采用注意力机制来抑制无关信息.
主要成果:
- 拟议的MACNN方法显著优于最先进的方法.
- 实验表明,在公开的Github和Physionet数据集上,准确度和得分都更高.
- 功能级核聚变被证明比早期的核聚变战略更有效.
结论:
- 在心脏声音分类中,MACNN表现出了高性能.
- 该方法显示了潜在的临床实用性,用于识别心脏病.
- 这种技术可以帮助心脏病学家和研究人员开发先进的分类系统.
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