融合式音频实例和代表用于呼吸系统疾病检测
Tuan Truong1, Matthias Lenga1, Antoine Serrurier2
1Bayer AG, 13353 Berlin, Germany.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
这项研究引入了一种新的音频分类方法,FAIR,用于检测呼吸系统疾病. 结合咳,呼吸和语音的声音显著提高了COVID-19的诊断准确度.
科学领域:
- 医疗信息学 医疗信息学
- 生物医学信号处理
- 机器学习用于医疗保健
背景情况:
- 基于音频的身体声音的分类对于诊断呼吸系统疾病至关重要.
- 虽然咳是主要的生物标志物,但呼吸和语音声音也显示出潜力,特别是用于COVID-19检测.
研究的目的:
- 提出和评估融合音频实例和表示 (FAIR) 方法,以提高呼吸系统疾病的检测.
- 研究结合各种音频表示 (波形和光谱) 和身体声音 (咳,呼吸,言语) 的有效性.
主要方法:
- 开发了FAIR,这是一种从多个身体声音表示中构建联合特征向量的方法.
- 利用自我注意力机制来整合从咳,呼吸和语音中提取的特征.
- 试验了将波形和光谱图数据用于COVID-19检测的组合.
主要成果:
- FAIR方法实现了0.8658的曲线下的区域 (AUC),对于COVID-19检测具有0.8057的灵敏度和0.7958的特异性.
- 与单个表示模型相比,结合波形和光谱表征显著改善了AUC得分.
- 咳,呼吸和语音声音特征的自我注意整合产生了最高的表现.
结论:
- 通过融合多模式音频功能,FAIR在呼吸系统疾病检测方面展示了卓越的性能.
- 结合波形和谱图表示,丰富了特征提取,优于单一表示方法.
- FAIR方法的灵活性支持在各种疾病的多式联络诊断系统中的更广泛应用.
关键词:
在 COVID-19 疫情中,音频 音频 音频 音频 音频 音频这是分类分类的分类.深度学习是一种深度学习.多级别的学习多级别的学习.呼吸道疾病是一种呼吸道疾病.频谱图是指光谱图中的光谱.波形波形波形波形的波形.更多相关视频
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