Deep Learning for Diagnosis and Classification of Obstructive Sleep Apnea: A Nasal Airflow-Based Multi-Resolution Residual Network

Abstract

This study evaluated a novel approach for diagnosis and classification of obstructive sleep apnea (OSA), called Obstructive Sleep Apnea Smart System (OSASS), using residual networks and single-channel nasal pressure airflow signals. Methods: Data were collected from the sleep center of the First Affiliated Hospital, Sun Yat-sen University, and the Integrative Department of Guangdong Province Traditional Chinese Medical Hospital. We developed a new model called the multi-resolution residual network (Mr-ResNet) based on a residual network to detect nasal pressure airflow signals recorded by polysomnography (PSG) automatically. The performance of the model was assessed by its sensitivity, specificity, accuracy, and F1-score. We built OSASS based on Mr-ResNet to estimate the apnea-hypopnea index (AHI) and to classify the severity of OSA, and compared the agreement between OSASS output and the registered polysomnographic technologist (RPSGT) score, assessed by two technologists. Results: In the primary test set, the sensitivity, specificity, accuracy, and F1-score of Mr-ResNet were 90.8%, 90.5%, 91.2%, and 90.5%, respectively. In the independent test set, the Spearman correlation for AHI between OSASS and the RPSGT score determined by two technologists was 0.94 (p < 0.001) and 0.96 (p < 0.001), respectively. Cohen's Kappa scores for classification between OSASS and the two technologists' scores were 0.81 and 0.84, respectively. Conclusion: Our results indicated that OSASS can automatically diagnose and classify OSA using signals from a single-channel nasal pressure airflow, which is consistent with polysomnographic technologists' findings. Thus, OSASS holds promise for clinical application.

A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

“…Table 1 summarizes the different sleep study approaches followed by various researchers. PSG signals contain ECG, EEG, EMG, nasal sounds, and breathing patterns [ 6 , 21 , 35 ]. Figure 2 portrays the distribution of the signal source used in the literature.…”

“Comparison of machine learning and deep learning classifier to detect sleep apnea using single-channel ECG and HRV: A Systematic Literature Review”

“…Many studies have shown that a hybrid model has better performance than a specific classifier. This paper [32] has anticipated the Mr-ResNet framework using nasal pressure airflow signals for OSA and verified good feasibility for clinical application.…”

“…It was observed that automatic detection of sleep apnea can be performed reliably on clinical data too. [32] Automatic sleep apnea detection using LS SVM performs better although the study considered single-channel ECG but could not classify apnea and hypopnea. [33] The OSA detection using SpO2 is better than IHR from ECG according to the work.…”

Data Analysis and System Development for Medical Professionals on Sleep Apnea Syndrome and Orthostatic Dysregulation by Processing-Healthcare Professionals and Patients

“…Therefore, many studies have been conducted on the possibility of diagnosing SAS using breath sounds during sleep. Recently, studies using machine learning and AI have been reported [ 23 , 24 ].…”

“…In another example, we used a continuous wavelet transform (CWT) to visualize breath sounds during sleep and to communicate the condition to the patient. CWT is a useful analysis method (considering time and frequency) and is expressed by Equation (1) [ 23 , 24 ]. …”