通过自动检测呼吸循环对常见的肺声音进行基于深度学习的分类
Mustafa Alptekin Engin1, Rukiye Uzun Arslan2, İrem Senyer Yapici3
1Department of Electrical and Electronics Engineering, Bayburt University, 69000 Bayburt, Türkiye.
Bioengineering (Basel, Switzerland)
|February 27, 2026
概括
人工智能 (AI) 可以准确地分类肺部声音 (LSs) 来诊断呼吸系统疾病. 将光谱图与卷积神经网络 (CNN) 结合起来,获得了97.3%的准确性,超过了人类专家.
科学领域:
- 医疗技术 医疗技术 医学技术
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 慢性呼吸道疾病是全球死亡的主要原因之一.
- 通过肺声音听觉进行早期诊断至关重要,但需要专业的听觉技能.
- 人工智能 (AI) 在高精度的肺声 (LSs) 分类方面表现有前途.
研究的目的:
- 为了比较各种基于人工智能的方法来分类肺部声音.
- 为了评估LS分析的不同时间频率表示.
- 确定最佳的AI架构和特征表示,以准确地进行LS分类.
主要方法:
- 自动检测和标记从肺部声音的呼吸循环.
- 使用的时间频率表示:光谱图,光谱图,Mel光谱图和光谱图.
- 使用预训练的卷积神经网络 (CNN) 进行转移学习,并比较CNN,CNN-LSTM混合和支持矢量机器模型.
主要成果:
- 分析了高信度的低频谱结构和抗噪声的玛图图谱.
- 将光谱图与CNN架构相结合,获得了最高的分类准确率97.3%±1.9%.
- 这种人工智能方法与其他评估方法相比,显示出更高的性能.
结论:
- 人工智能,特别是CNN与玛图谱相结合,为肺声音分类提供了高度准确和可靠的方法.
- 这项技术可以帮助早期和非侵入性诊断慢性呼吸道疾病.
- 这项研究强调了先进的信号处理和深度学习在呼吸系统医学中的潜力.
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