睡眠障碍和呼吸暂停事件检测框架具有高性能,使用双层学习模型设计
1Computer Engineering, Kayseri University, Kayseri, Turkey.
PeerJ. Computer science
|October 9, 2023
概括
一个新的双层深度学习模型使用多睡眠学数据准确地检测睡眠呼吸暂停及其类型. 这种方法提高了诊断的准确性,帮助睡眠诊所对这种常见的呼吸障碍进行早期干预.
科学领域:
- 医疗技术 医疗技术 医学技术
- 人工智能在医学中的应用
- 睡眠医学 睡眠医学
背景情况:
- 睡眠呼吸暂停是一种普遍的呼吸障碍,影响睡眠质量和整体健康.
- 通过多睡眠学 (PSG) 手动诊断睡眠呼吸暂停是耗时和主观的.
- 准确和高效的检测方法对于及时的患者干预至关重要.
研究的目的:
- 开发和评估一种新的双层深度学习模型,用于自动检测睡眠呼吸暂停.
- 提高诊断睡眠呼吸暂停及其亚型的准确性和效率.
- 为睡眠诊所提供一个强大的,独立于患者的系统.
主要方法:
- 一个双层架构,将第一层的深度学习模型 (DNN,GRU,RNN,LSTM) 和第二层的机器学习元学习器结合起来.
- 训练使用了23个特征向量,包括打,氧和,兴奋和睡眠得分,以及PSG数据.
- 应用了数据预处理和不足抽样技术,以解决50名儿科和成人患者数据集中的类不平衡问题.
主要成果:
- 拟议的方法在检测呼吸暂停,呼吸暂停和正常呼吸方面达到95.74%的准确性.
- 它在分类呼吸暂停类型 (中心性,混合性,阻塞性) 中显示了99.4%的准确性.
- 实验结果证实了高精度和独立于患者的一致性.
结论:
- 开发的双层深度学习模型为睡眠呼吸暂停检测提供了高度准确和强大的解决方案.
- 这个系统可以显著帮助睡眠诊所在详细的睡眠障碍诊断.
- 该模型的性能表明,它有可能在临床上得到广泛采用,以改善患者的治疗结果.
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