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使用囊神经网络进行心脏声分类

Yu-Ting Tsai^1,2, Yu-Hsuan Liu¹, Zi-Wei Zheng^2,3

¹Master's Program in Electro-Acoustics, Feng Chia University, Taichung 40724, Taiwan.

Bioengineering (Basel, Switzerland)

|November 25, 2023

概括

一个新的囊神经网络 (CapsNet) 通过更好地从心脏声音数据中提取特征,提高了心脏声分类的准确性. 这种人工智能方法提高了心血管医疗保健中的诊断能力.

科学领域:

心脏病学心脏病学
人工智能的人工智能
生物医学信号处理

背景情况:

像人工智能驱动的诊断系统这样的智能检测系统已经有了先进的心脏病诊断.
对心脏声音的自动细分和分类对于精确的AI驱动分析至关重要.
目前的方法通常依赖于心电图 (ECG) 标记的心电图 (PCG) 或Mel尺度频率头系数 (MFCC) 特征提取,这可能是卷积神经网络 (CNN) 的限制.

研究的目的:

引入一种新的囊神经网络 (CapsNet),以改进心脏声音的分类.
解决传统的CNN在从MFCC心声频谱中提取相关特征方面的局限性.
使用先进的人工智能技术提高心脏声分类的预测准确度.

主要方法:

提出了一个囊神经网络 (CapsNet),利用代动态路由来提取特征.
采用CapsNet来利用MFCC频谱特征的翻译等价值,以便更好地进行分类.
使用2016年PhysioNet心声数据库和定制的临床数据集,对CapsNet与CNN进行了培训和验证.

主要成果:

与传统的CNN相比,CapsNet在分类心脏声音方面表现优越.
在测试数据集上实现了90.29%和91.67%的验证准确性.
成功微调超参数,并在现实世界的临床听觉数据集上测试结果.

结论:

卡普斯网为准确的心脏声分类提供了一种可行和有效的方法.
拟议的方法克服了CNN在心声分析中所面临的特征提取挑战.
这种人工智能进步有可能通过增强的心声分析来改善心血管诊断.

关键词:

听术 (Auscultation) 是一种听觉方式.囊神经网络的神经网络心脏失声的诊断是指心脏失声的诊断.医疗保健中的深度学习心脏的声心脏的声心脏声音诊断诊断心脏声音诊断

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