SomnNET：一种基于SpO2的深度学习网络，用于智能手表中的睡眠呼吸暂停检测
SomnNET: An SpO2 Based Deep Learning Network for Sleep Apnea Detection in Smartwatches
摘要：
呼吸中的异常暂停或速率降低被称为睡眠呼吸暂停低通气综合征，并影响个体的睡眠质量。本文讨论了一种从可穿戴设备获得的外周血氧饱和度（SpO 2）信号中检测睡眠呼吸暂停事件（呼吸暂停）的新方法。本文详细介绍了一个非常高的分辨率每秒的基础上，1维卷积神经网络-我们称之为SomnNET -的呼吸暂停检测算法。该网络的准确率为97.08%，优于几种分辨率较低的最先进的呼吸暂停检测方法。探讨了通过模型剪枝和二值化来降低计算复杂性的可行性。稀疏度为80%的剪枝网络的准确率为89.75%，二值化网络的准确率为68.22%。所提出的网络的性能进行比较，对几个国家的最先进的算法。
英文摘要：
The abnormal pause or rate reduction in breathing is known as the sleep-apnea hypopnea syndrome and affects the quality of sleep of an individual. A novel method for the detection of sleep apnea events (pause in breathing) from peripheral oxygen saturation (SpO2) signals obtained from wearable devices is discussed in this paper. The paper details an apnea detection algorithm of a very high resolution on a per-second basis for which a 1-dimensional convolutional neural network -- which we termed SomnNET -- is developed. This network exhibits an accuracy of 97.08% and outperforms several lower resolution state-of-the-art apnea detection methods. The feasibility of model pruning and binarization to reduce the computational complexity is explored. The pruned network with 80% sparsity exhibited an accuracy of 89.75%, and the binarized network exhibited an accuracy of 68.22%. The performance of the proposed networks is compared against several state-of-the-art algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
