采用变压器神经网络来提高阻断性睡眠呼吸暂停氧度的诊断性能
Malak Abdullah Almarshad1,2, Saad Al-Ahmadi1, Md Saiful Islam1
1Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
一种新的变压器神经网络方法使用氧计数据准确诊断阻塞性睡眠呼吸暂停 (OSA). 这种方法可以减少诊断时间和成本,而不是传统的多睡眠学.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 睡眠医学 睡眠医学
背景情况:
- 通过多睡眠学来诊断阻塞性睡眠呼吸暂停 (OSA) 是耗时且昂贵的.
- 机器学习,特别是深度神经网络,为更快,更便宜的OSA诊断提供了潜力.
- 现有的方法通常需要大量的信号预处理,这限制了它们的实际应用.
研究的目的:
- 开发一种新的深度学习方法,用于使用氧计数据诊断阻塞性睡眠呼吸暂停 (OSA).
- 提高OSA检测的诊断性能和效率.
- 为了使医生能够对诊断模型的输出进行解释.
主要方法:
- 一个具有可学习定位编码的变压器神经网络被开发用于OSA诊断.
- 该模型以一秒颗粒度进行注释,以提高可解释性.
- 在OSASUD数据集上评估了不同的位置编码设计和时间分辨率.
主要成果:
- 拟议的变压器网络实现了AUC为0.89,准确度为0.80,F1得分为0.79.
- 基于具有结构新性的自动编码器的可学习位置编码产生了最佳性能.
- 与现有的最先进的解决方案相比,该方法显示出更高的性能.
结论:
- 新型变压器网络提供了一种有希望的,高效的,可解释的方法,用于使用氧计来诊断OSA.
- 这种方法有可能大大减少与传统的多睡眠学相关的时间和成本.
- 该模型的1秒颗粒度注释有助于临床医生的解释.
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