Sleep Apnea Classification in an Activity Tracker Environment

Abstract

Sleep apnea is one of the most common sleep disorders. It can have a variety of detrimental effects ranging from trouble breathing to increased risk of heart failure. Sleep apnea often goes undiagnosed due to the practicalities and cost of getting tested. To be diagnosed with sleep apnea, a patient must undertake a polysomnography (PSG) or sleep study where multiple physiological signals are recorded in a specialised sleep laboratory. Diagnosing sleep apnea using data gathered in a distributed manner could help to remove barriers to diagnosis. Prior work has largely focused on creating machine learning models trained on a subset of the physiological signals that are recorded in a medical setting. Activity trackers are becoming more advanced with the current generation being capable of deriving blood oxygen saturation (SpO2). SpO2 has been shown to be an important indicator for sleep apnea. Activity tracker data acquisition faces challenges, such as the sampling rate of data and noise associated with the signal. This work compares the performance of ML models in a simulated activity tracker environment. The results show that a Support Vector Machine (SVM) classifier trained with features extracted from a Convolutional Neural Network (CNN) is capable of classifying sleep apnea with a high degree of accuracy.

ResearchGate

Recommended citation: Brendan Lyden, Ruairi O'Reilly (2021). “Sleep Apnea Classification in an Activity Tracker Environment.” 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI).