Prucnal Monika A, Polak Adam G
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:287-290. doi: 10.1109/EMBC.2018.8512201.
Sleep apnea (SA) is one of the most common disorders manifesting during sleep and the electroencephalo-gram (EEG) belongs to these biomedical signals that change during apnea and hypopnea episodes. In recent years, a few publications reported approaches to the automatic classification of sleep apnea episodes based only on the EEG. The purpose of this work was to analyze statistical features extracted from the EEG epochs by combined discrete wavelet transform (DWT) and Hilbert transform (HT). Additionally, the selected most discriminative 30 features were then used in the automatic classification of normal breathing and obstructive (OSA) and central (CSA) apnea by a feedforward neural network with 17+7 neurons in two hidden layers. This classifier returned the accuracy of 73.9% for the training and 77.3% for the testing set. The analysis of features extracted from EEG epochs revealed the importance of theta, beta and gamma brain waves.
睡眠呼吸暂停（SA）是睡眠期间最常见的疾病之一，脑电图（EEG）属于在呼吸暂停和呼吸不足发作期间会发生变化的生物医学信号。近年来，有一些出版物报道了仅基于脑电图对睡眠呼吸暂停发作进行自动分类的方法。这项工作的目的是分析通过离散小波变换（DWT）和希尔伯特变换（HT）相结合从脑电图片段中提取的统计特征。此外，然后将选定的最具区分性的30个特征用于通过具有两个隐藏层、分别有17 + 7个神经元的前馈神经网络对正常呼吸、阻塞性（OSA）和中枢性（CSA）呼吸暂停进行自动分类。该分类器在训练集上的准确率为73.9%，在测试集上的准确率为77.3%。对从脑电图片段中提取的特征进行分析，揭示了θ波、β波和γ脑电波的重要性。
