基于打分类和融合模型的OSAHS患者的AHI估计
Yujun Song1, Xiaoran Sun1, Li Ding1
1School of Physics and Optoelectronics, South China University of Technology, Guangzhou 510640, China.
American journal of otolaryngology
|July 1, 2023
概括
这项研究开发了一种打声音分析方法,用于选阻塞性睡眠呼吸暂停-呼吸暂停综合征 (OSAHS). 人工智能模型准确地预测了呼吸暂停-呼吸暂停指数 (AHI),提供了一个潜在的家庭监控解决方案.
科学领域:
- 睡眠医学 睡眠医学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 阻塞性睡眠呼吸暂停-呼吸暂停综合征 (OSAHS) 是一种普遍的慢性疾病,影响患者的生活质量并增加并发症风险.
- 诊断的黄金标准 - - 聚睡眠学 (PSG) 是昂贵的,需要住院治疗.
- 打是与OSAHS相关的常见症状之一.
研究的目的:
- 开发和验证一种有效的OSAHS查方法,使用打声音分析.
- 要区分与OSAHS相关的打和简单的打.
- 为了非侵入性地估计呼吸暂停-呼吸暂停指数 (AHI).
主要方法:
- 收集并标记基于实时PSG记录的打声音.
- 开发了三种AI模型:XGBoost的声学特征,CNN的Mel频谱和ResNet的Mel频谱.
- 通过软投票将三种模型合并为增强的打声音检测和AHI估计.
主要成果:
- 合并模型在检测打类型方面实现了83.44%的准确性和85.27%的回忆.
- 估计的AHI显示出与PSG衍生的AHI有很强的相关性 (皮尔森r=0.913,R2=0.834).
- 该方法在预测来自打声音的AHI方面表现出显著的有效性.
结论:
- 打声音分析是OSAHS查和AHI预测的有效方法.
- 拟议的方法显示了对OSAHS的方便和可访问的家庭监测的潜力.
- 这种人工智能驱动的方法可以降低医疗保健成本,改善OSAHS患者管理.
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