ITI/Larsys/Madeira Interactive Technologies Institute, Portugal; Universidade de Lisboa, Instituto Superior Técnico, Portugal.
Universidad de Las Palmas de Gran Canaria, Institute for Technological Development and Innovation in Communications, Spain; ITI/Larsys/Madeira Interactive Technologies Institute, Portugal.
Comput Methods Programs Biomed. 2020 Dec;197:105640. doi: 10.1016/j.cmpb.2020.105640. Epub 2020 Jul 4.
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.
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.
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.
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.
睡眠呼吸暂停是一种常见的睡眠障碍，通常使用一种昂贵、高度专业化且不便的测试，即多导睡眠图来诊断。可以开发一种基于自动化分类系统的单 SpO2 传感器来简化呼吸暂停检测。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，能够从一维 SpO2 信号中检测呼吸暂停事件。然而，找到一个最优的卷积神经网络结构是一项艰巨的任务，通常需要通过试错法来完成。为了解决这个问题，提出了一种方法来节省时间并简化寻找最优卷积神经网络结构的过程。
提出了基于贪心算法的优化方法来搜索最优的卷积神经网络结构。提出了三种不同的基于贪心算法的变体：拓扑转移、带粗糙估计的加权拓扑转移和带精细调整的加权拓扑转移。进行了基于主题的独立和跨数据库测试的分析。
考虑到执行时间和性能之间的平衡，带粗糙估计的加权拓扑转移是最佳的。在每分钟检测呼吸暂停事件方面，HuGCDN2008 数据库的准确率为 88.49%，Apnea-ECG 数据库的准确率为 95.14%。对于呼吸暂停患者的检测，也称为全局分类，在 HuGCDN2008 数据库中的准确率为 95.71%，在 AED 数据库中的准确率为 100%，无需从两个数据库中删除任何受试者。
所提出的一维卷积神经网络在类似情况下的表现优于文献中的那些。基于贪心算法的方法，主要是带粗糙估计的加权拓扑转移，是一种替代广泛试错法的方法。
