Ostadieh Javad, Amirani Mehdi Chehel
Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran.
J Electr Bioimpedance. 2020 Mar 18;11(1):4-11. doi: 10.2478/joeb-2020-0002. eCollection 2020 Jan.
Apnea is one of the deadliest diseases that can be prevented and cured if it is detected in time. In this paper, we propose a precise method for early detection of the obstructive sleep apnea (OSA) disease using the latest feature selection and extraction methods. The feature selection in this paper is based on the Dual tree complex wavelet (DT-CWT) coefficients of the ECG signals of several patients. The feature extraction from these coefficients is done using frequency and time techniques. The Feature selection is done using the spectral regression discriminant analysis (SRDA) algorithm and the classification is performed using the hybrid RBF network. A hybrid RBF neural network is introduced in this paper for detecting apnea that is much less computationally demanding than the previously presented SVM networks. Our findings showed a 3 percent improvement in the detection and at least a 30 percent reduction in the computational complexity in comparison with methods that have been presented recently.
呼吸暂停是最致命的疾病之一，如果能及时发现，是可以预防和治愈的。在本文中，我们提出了一种使用最新的特征选择和提取方法来早期检测阻塞性睡眠呼吸暂停（OSA）疾病的精确方法。本文中的特征选择基于多名患者心电图信号的双树复小波（DT-CWT）系数。从这些系数中提取特征是使用频率和时间技术完成的。特征选择使用谱回归判别分析（SRDA）算法完成，分类使用混合RBF网络进行。本文引入了一种混合RBF神经网络来检测呼吸暂停，其计算要求比之前提出的支持向量机网络低得多。我们的研究结果表明，与最近提出的方法相比，检测率提高了3%，计算复杂度至少降低了30%。
