Background and objective

Sleep apnea is a common sleep disorder, usually diagnosed using an expensive, highly specialized, and inconvenient test called polysomnography. A single SpO2 sensor based on an automated classification system can be developed to simplify the apnea detection. The main objective of this work is to develop a classifier based on a convolution neural network with the capability of detecting apnea events from one dimensional SpO2 signal. However, to find an optimum convolution neural network structure is a daunting task is usually done by a trial-and-error method. To solve this problem, a method is proposed to save time and simplify the process of searching for an optimum convolution neural network structure.

Methods

Greedy based optimization is proposed to search for an optimized convolution neural network structure. Three different variants of greedy based optimization are proposed: the topology transfer, the weighted-topology transfer with rough estimation, and the weighted-topology transfer with fine tuning. The subject independent and the cross-database test are performed for the analysis.

Results

Considering the balance between the execution time and the performance, the weighted-topology transfer with rough estimation is the best. An accuracy of 88.49% for the HuGCDN2008 database and 95.14% for the Apnea-ECG database are obtained for apnea events detection per minute. Regarding the apnea patient detection, also referred to as global classification, an accuracy of 95.71% is achieved for the HuGCDN2008 database, and 100% is achieved for the AED database without removing any subjects from both databases.

Conclusions

The proposed one-dimensional convolution neural network performs better in a similar situation than those presented in the literature. The greedy based methods, mainly the weighted-topology transfer with rough estimation, is an alternative method to extensive trial and error method.

中文翻译：

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基于贪婪的卷积神经网络优化算法，用于检测呼吸暂停。

背景和目标

睡眠呼吸暂停是一种常见的睡眠障碍，通常使用称为多导睡眠图的昂贵，高度专业化且不方便的测试来诊断。可以开发基于自动分类系统的单个SpO2传感器，以简化呼吸暂停检测。这项工作的主要目的是开发一种基于卷积神经网络的分类器，该分类器具有从一维SpO2信号中检测呼吸暂停事件的能力。然而，找到最佳的卷积神经网络结构通常是艰巨的任务，这是通过试错法来完成的。为了解决这个问题，提出了一种节省时间并简化寻找最佳卷积神经网络结构的过程的方法。

方法

提出了基于贪婪的优化算法，以寻找优化的卷积神经网络结构。提出了三种不同的基于贪婪的优化方法：拓扑转移，粗略估计的加权拓扑转移和微调的加权拓扑转移。执行独立主题和跨数据库测试以进行分析。

结果

考虑到执行时间和性能之间的平衡，采用粗略估计的加权拓扑转移是最好的。对于每分钟的呼吸暂停事件检测，HuGCDN2008数据库的准确度为88.49％，呼吸暂停-ECG数据库的准确度为95.14％。关于呼吸暂停患者检测，也称为全局分类，对于HuGCDN2008数据库，其准确率达到95.71％，对于AED数据库，其准确率达到100％，而无需从这两个数据库中删除任何受试者。

结论

所提出的一维卷积神经网络在类似情况下的性能要比文献中提出的更好。基于贪婪的方法（主要是带有粗略估计的加权拓扑转移）是广泛试验和错误方法的替代方法。