边缘计算系统用于使用音频分析自动检测慢性呼吸道疾病
José Antonio Rivas-Navarrete1, Humberto Pérez-Espinosa2, A L Padilla-Ortiz3,4
1CICESE-UAT, CICESE, Andador 10 # 109, Tepic, 63173, Nayarit, México. jrivas@cicese.edu.mx.
Journal of medical systems
|March 4, 2025
概括
这项研究开发了一种人工智能驱动的边缘计算系统,用于使用咳和呼吸声音检测慢性呼吸系统疾病 (CRD). 该系统实现了高精度,为偏远地区提供了潜在的低成本选工具.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 慢性呼吸道疾病 (CRD) 构成了全球卫生挑战,在偏远地区存在诊断局限性.
- 呼吸系统的声音包含声学特征,表明CRDs.
- 人工智能 (AI) 和边缘计算提供了新的诊断途径.
研究的目的:
- 开发和评估基于音频的边缘计算系统,用于自主检测CRD.
- 使用机器学习 (ML) 来分析呼吸声 (咳和呼吸).
- 评估系统在智能手机和树派 (Raspberry Pi) 等边缘设备上的性能.
主要方法:
- 开发一个采用ML算法的边缘计算系统.
- 呼吸声特征的分析,包括Mel频率塞普斯特拉系数 (MFCC) 和染色图.
- 在86名个体的数据集上进行培训和测试 (53名有CRD,33名健康),最终对35名个体进行评估.
主要成果:
- 该系统在CRD检测方面表现出高灵敏度 (90.0%) 和特异性 (93.55%).
- 在分类健康和患病的个体中达到91.75%的平衡准确度.
- 在边缘设备上成功对呼吸道声音进行分类.
结论:
- 边缘计算和ML系统显示了呼吸系统疾病检测的巨大潜力.
- 开发的系统可以作为一个高效和具有成本效益的选工具.
- 这项技术可以提高CRD诊断的可访问性,特别是在服务不足的地区.
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