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基于咳的呼吸道疾病分类的新型音频特征依赖特征提取和数据增强方法
Jiakun Shen1, Xueshuai Zhang1, Yu Lu2
1Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
Computers in biology and medicine
|July 19, 2024
概括
这项研究引入了新的AI方法,使用咳声音来诊断呼吸系统疾病. 新的音频特征提取和数据增强技术显著提高了分类准确性,有助于早期发现疾病.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 呼吸道疾病是一个全球性的健康挑战,需要准确和早期诊断.
- 咳的声音包含有价值的信息,用于识别不同的呼吸道病理.
- 目前用于咳分析的AI方法往往忽略了咳信号的特定特征.
研究的目的:
- 设计一个人工智能系统,根据咳的声音对呼吸系统疾病进行分类.
- 开发针对咳信号的新音频特征提取和数据增强方法.
- 为了提高人工智能驱动的呼吸系统疾病诊断的准确性和稳定性.
主要方法:
- 建议最大重叠的Mel谱图以捕捉咳声音的快速转换.
- 实施了各种数据增强策略,包括一种基于自我能量的新方法,以解决有限的标记数据.
- 利用测试时间增强来融合原始和增强音频的结果,以改进分类.
主要成果:
- 在接收器操作特征 (宏观AUC) 下的宏观区域中,实现了平均3.33%的绝对性能改善.
- 在未加权平均回忆 (UAR) 中显示了平均3.10%的绝对性能改善.
- 梯度加权类激活映射 (Grad-CAM) 可视化证实了特征对分类决策的贡献.
结论:
- 拟议的音频特征依赖方法在基于AI的呼吸系统疾病分类中从咳声音提供了显著的进步.
- 这些技术提高了诊断的准确性,可能导致更早,更有效的患者管理.
- 该研究强调了信号特征工程和增强在医疗AI应用中的重要性.
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