对常量Q转换,色谱和Mel光谱技术进行比较分析,用于人工智能辅助的心脏诊断
Mohammed Saddek Mekahlia1, Mohamed Fezari1, Ahcen Aliouat2
1Laboratory of Automatic and Signals of Annaba (LASA), Badji Mokhtar - Annaba University, P.o.Box 12, Annaba, 23000, Algeria.
Medical engineering & physics
|March 8, 2025
概括
本研究引入了一种先进的深度学习模型,用于使用心电图 (PCG) 分析进行自动心脏声音分类. 该模型在检测心脏异常方面达到很高的准确性,为早期诊断提供了具有成本效益的工具.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 心血管疾病 (CVD) 是全球死亡的主要原因,需要及早检测心脏异常.
- 异常的心声是心脏问题的关键指标,由于它们的低频特征,很难识别.
研究的目的:
- 通过深度学习开发和评估用于心脏声音分类的自动化系统.
- 通过心电图 (PCG) 分析,增强早期和准确的心脏异常检测.
主要方法:
- 采用数字信号处理和先进的深度学习技术进行自动心脏声音分类.
- 使用的时间频率表示:光谱图,Mel光谱图和常量Q变换 (CQT).
- 设计和评估了一个带有定向环形图 (DAG-CNN) 架构的卷积神经网络.
主要成果:
- 实现了高分类准确度: 100%的光谱, 99.7%的Mel光谱和 99.5%的CQT.
- 与预先训练的卷积神经网络 (CNN) 模型相比,证明了更高的性能.
- 拟议的DAG-CNN模型有效地使用各种时间频率表示来分类心脏声音.
结论:
- 开发的自动心声分类系统显示了早期心脏异常检测的重大前景.
- 这种进步提供了一个具有成本效益的解决方案,特别有利于资源有限的环境.
- 这项研究有助于弥合诊断差距,改善心脏护理的可获得性.
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