Sleep Apnea Detection Using Multi-Error-Reduction Classification System with Multiple Bio-Signals

Xilin Li; Frank H F Leung; Steven Su; Sai Ho Ling

doi:10.3390/s22155560

Sleep Apnea Detection Using Multi-Error-Reduction Classification System with Multiple Bio-Signals

Sensors (Basel). 2022 Jul 25;22(15):5560. doi: 10.3390/s22155560.

Authors

Xilin Li¹, Frank H F Leung², Steven Su¹, Sai Ho Ling³

Affiliations

¹ School of Biomedical Engineering, Faculty of Engineering and Information Technology (FEIT), University of Technology Sydney (UTS), Ultimo, NSW 2007, Australia.
² Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hum, Hong Kong .
³ School of Electrical and Data Engineering, Faculty of Engineering and Information Technology (FEIT), University of Technology Sydney (UTS), Ultimo, NSW 2007, Australia.

Abstract

Introduction: Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task, and automatic diagnosis is much more desirable. The contribution of this work is to detect OSA using a multi-error-reduction (MER) classification system with multi-domain features from bio-signals.

Methods: Time-domain, frequency-domain, and non-linear analysis features are extracted from oxygen saturation (SaO2), ECG, airflow, thoracic, and abdominal signals. To analyse the significance of each feature, we design a two-stage feature selection. Stage 1 is the statistical analysis stage, and Stage 2 is the final feature subset selection stage using machine learning methods. In Stage 1, two statistical analyses (the one-way analysis of variance (ANOVA) and the rank-sum test) provide a list of the significance level of each kind of feature. Then, in Stage 2, the support vector machine (SVM) algorithm is used to select a final feature subset based on the significance list. Next, an MER classification system is constructed, which applies a stacking with a structure that consists of base learners and an artificial neural network (ANN) meta-learner.

Results: The Sleep Heart Health Study (SHHS) database is used to provide bio-signals. A total of 66 features are extracted. In the experiment that involves a duration parameter, 19 features are selected as the final feature subset because they provide a better and more stable performance. The SVM model shows good performance (accuracy = 81.68%, sensitivity = 97.05%, and specificity = 66.54%). It is also found that classifiers have poor performance when they predict normal events in less than 60 s. In the next experiment stage, the time-window segmentation method with a length of 60s is used. After the above two-stage feature selection procedure, 48 features are selected as the final feature subset that give good performance (accuracy = 90.80%, sensitivity = 93.95%, and specificity = 83.82%). To conduct the classification, Gradient Boosting, CatBoost, Light GBM, and XGBoost are used as base learners, and the ANN is used as the meta-learner. The performance of this MER classification system has the accuracy of 94.66%, the sensitivity of 96.37%, and the specificity of 90.83%.

Keywords: feature extraction; feature selection; polysomnography; sleep apnea detection.

MeSH terms

Algorithms
Biosensing Techniques / methods
Humans
Machine Learning
Polysomnography / methods
Sensitivity and Specificity
Sleep / physiology
Sleep Apnea Syndromes / diagnosis*
Sleep Apnea, Obstructive / diagnosis
Support Vector Machine

Grants and funding

This research received no external funding.