A novel approach to diagnose sleep apnea using enhanced frequency extraction network. (July 2021)

• Record Type: Journal Article
• Authors: Wu, Yitao; Pang, Xiongwen; Zhao, Gansen; Yue, Huijun; Lei, Wenbin; Wang, Yongquan
• Is Part Of: Computer methods and programs in biomedicine. Volume 206(2021)
• Publication Date: 2021-07
• Subjects: Sleep apnea-hypopnea syndrome -- Frequency extraction network -- Frequency decomposition -- Nasal airflow; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106119
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We propose an enhanced frequency extraction network. It is an optimal architecture that automatically extracts sleep apnea-hypopnea events features from the spectrogram of nasal airflow signals. The designed module in the proposed network to meet the specific demands in sleep apnea-hypopnea events detection. In the domain of detecting sleep apnea-hypopnea events, the proposed model achieves superior performance as compared to previous methods using machine learning and deep learning ABSTRACT: Sleep apnea-hypopnea syndrome (SAHS), as a widespread respiratory sleep disorder, if left untreated, can lead to a series of pathological changes. By using Polysomnography (PSG), traditional SAHS diagnosis tends to be complex and costly. Nasal airflow (NA) is the most direct reflection of the severity of SAHS. Therefore, we try to take advantage of NA signals that can be easily recorded by wearable devices. In this paper, we present an automatic detection approach of SAH events based on single-channel signal. Through this approach, an enhanced frequency extraction network is designed, which factorizes the mixed feature maps by their frequencies. And the spatial resolution of low-frequency components is reduced so as to save spending. Besides, in our research, the vanilla convolution block of the high-frequency components are replaced by residual blocks and smaller groups of filters with bigger size kernels. And we use the spatial attention module to facilitate feature extraction. Compared with state-of-the-art networks in this field, the promising results reveal that the proposed network for SAH events multiclass classification shows outstanding performance with accuracy of 91.23%, sensitivity of 90.81% and specificity of 90.59%. Thus, we believe that our approach, as a low-cost and high-efficiency solution, shows a great potential for detecting SAH events.