Multimodal Classification of Obstructive Sleep Apnea using Feature Level Fusion

Abstract

Obstructive sleep apnea (OSA) is a sleep disorder with long-term consequences. Long-term effects include sleep related issues and cardiovascular diseases. OSA is often diagnosed with an overnight sleep test called a polysomnogram. Monitoring can be costly with long wait times for diagnosis and computer-based efficient algorithms are needed. Here, we employ a multi-modal approach that performs feature-level fusion of two physiological signals, namely electrocardiograph (ECG) and saturation of peripheral oxygen (SpO(2)) for efficient OSA classification. We design Naive Bayes (NB), k-nearest neighbor (kNN), and Support Vector Machine (SVM) classifiers as the learning algorithms and present extensive empirical information regarding the utilized fusion strategy. Compared with other existing methods either considering single modality of signals or perform tests on subjects that have same severity of sleep apnea (i.e., high degree of apnea, low degree of apnea, or without apnea), we also define a test scenario that employs different subjects that have different sleep apnea severity to show the effectiveness of our approach. Our experimental results on real clinical examples from PhysioNet database show that, the proposed multimodal approach using feature-level fusion approach gives best classification rates when using SVM with an average accuracy of 96.64% for all test scenarios, i.e., within Subject with Same Severity (99.49%), between subjects with same sleep apnea severity (95.35%), and between subjects with distinct sleep apnea severity (95.07%).