通过对权重谱的参数建模来分析音频咳分析,以解释卷积神经网络的输出
概括
可解释的人工智能 (XAI) 分析了智能手机录音中的咳声音. 这种方法可以区分慢性阻塞性肺病 (COPD) 和其他呼吸道疾病,有助于诊断.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 呼吸系统医学 呼吸系统医学
背景情况:
- 持续咳是各种呼吸道疾病中常见的症状.
- 分析咳模式可以提供有价值的诊断信息.
- 目前用于咳分析的方法可能缺乏详细的解释性.
研究的目的:
- 调查使用可解释的人工智能 (XAI) 来分析咳模式的可行性.
- 为了确定XAI是否可以根据咳特征区分不同的呼吸系统疾病群体.
- 探索从呼吸系统健康中XAI解释性获得的见解.
主要方法:
- 收集了20名成年患者持续咳的24小时智能手机音频录音.
- 应用频域转换以生成1秒光谱图.
- 利用卷积神经网络 (CNN) 来检测咳事件,以及用于特征突出显示的闭塞地图.
主要成果:
- 对谱图区域的定量分析发现了患者群体之间的显著差异.
- XAI强调了慢性阻塞性肺病 (COPD) 患者与其他呼吸道病理患者之间咳模式的区别.
- 解释性分析提供了对慢性和非慢性呼吸道疾病中咳相关变异的见解.
结论:
- 在呼吸道疾病中,XAI方法可用于分析咳模式.
- 神经网络的可解释性为咳声音的区别提供了宝贵的见解.
- 这种方法显示了使用咳分析来区分COPD和其他呼吸道疾病的潜力.
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