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基于咳声音的功能融合方法用于肺结核病患者的检测
Wenlong Xu1, Xiaofan Bao1, Xiaomin Lou2
1College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, China.
这项研究引入了一种咳声音分析模型用于结核病 (TB) 查,实现了高精度. 该方法有效地将结核病患者与健康个体区分开来,使用融合的声学特征,提供了一个有前途的社区诊断工具.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 医疗保健中的人工智能
- 呼吸道疾病研究 呼吸道疾病研究
背景情况:
- 咳声音分析越来越多地用于COVID-19后的呼吸道疾病查.
- 结核病 (TB) 仍然是一个全球性卫生挑战,特别是在欠发达地区,目前的查方法,如PPD测试在敏感性和可访问性方面存在局限性.
- 像IGRA和Xpert MTB/RIF这样的先进诊断工具是有效的,但成本高昂,阻碍了广泛的社区使用.
研究的目的:
- 开发和评估一种新的功能融合模型,用于使用咳声音进行初级结核病查.
- 评估将传统的声学特征与语音谱图的深度特征相结合的有效性,以改善结核病检测.
- 提供一种可访问且准确的社区结核查方法.
主要方法:
- 从70名结核病患者和74名使用智能手机的健康受试者收集了咳声音数据.
- 使用Bi-LSTM和Bi-GRU循环神经网络来分析传统的声学特征 (MFCC,ZCR,能量,RMS,chroma_cens).
- 通过2D卷积从语音谱图中提取到Bi-LSTM模型中的集成特征,以进行增强的分类.
主要成果:
- 使用传统特征的Bi-LSTM模型实现了93.99%的准确性,93.93%的特异性和92.39%的结核病检测灵敏度.
- 在Bi-LSTM模型中将传统特征与语音谱图深度特征相结合,显著提高了性能,达到96.33%的精度,94.99%的特异性和98.13%的灵敏度.
- 与现有的PPD检测方法相比,融合特征方法显示出更高的效率和准确性.
结论:
- 传统的声学特征和从语音谱图中提取的深度特征在使用Bi-LSTM建模进行融合时表现出强烈的互补性.
- 拟议的咳声音分类方法为社区环境中初级结核病查提供了高度准确和高效的方法.
- 这种人工智能驱动的技术为传统选方法提供了可行的,具有成本效益的替代方案,解决了资源有限的地区的可访问性问题.
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