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有效的基于深度学习的混合模型来检测阻塞性睡眠呼吸暂停

Prashant Hemrajani¹, Vijaypal Singh Dhaka¹, Geeta Rani¹

¹Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India.

Sensors (Basel, Switzerland)

|July 11, 2023

概括

研究人员开发了一种新的机器学习方法,使用MobileNet V1和反复的神经网络来检测阻塞性睡眠呼吸暂停 (OSA). 这种方法为OSA诊断提供了比传统的多睡眠学更准确,更可靠的替代方案.

科学领域:

生物医学工程生物医学工程
医疗保健中的人工智能
睡眠医学睡眠医学

背景情况:

阻塞性睡眠呼吸暂停 (OSA) 是一个越来越多的关注与有限的意识.
目前的诊断标准,多睡眠学,是复杂的,昂贵的,和难以接近的许多.
现有的使用单信号的机器学习方法缺乏准确性和可靠性.

研究的目的:

开发一种准确且易于使用的方法来检测阻塞性睡眠呼吸暂停.
评估深度学习模型的有效性,特别是MobileNet V1及其与循环神经网络 (LSTM,GRU) 的融合,用于OSA诊断.

主要方法:

实现了MobileNet V1架构,用于分析单生理信号.
集成MobileNet V1与长期短期存储器 (LSTM) 和门式循环单元 (GRU) 网络.
使用PhysioNet呼吸暂停电心图数据库验证,包括真实的患者数据.

主要成果:

移动网络V1在检测阻塞性睡眠呼吸暂停时达到89.5%的准确性.
移动网络V1与LSTM和GRU的融合显示出卓越的性能,分别达到90%和90.29%的准确性.
提出的深度学习模型显著超过了现有的最先进的方法.

结论:

开发的深度学习范式,特别是MobileNet V1与循环神经网络相结合,为阻塞性睡眠呼吸暂停检测提供了高度准确和可靠的解决方案.
一个原型可穿戴设备被设计用于实时ECG监测和安全的云传输,展示了实际的临床应用.
这种方法为广泛的阻塞性睡眠呼吸暂停查和诊断提供了一种有希望的,可访问的多睡眠学替代方案.

关键词:

门式经常性单位门式经常性单位长期短期记忆长期短期记忆移动网络V1 移动网络V1 阻塞性睡眠呼吸暂停症是什么单线电心电图 (ECG) 是一个单线电线.

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