Sleep Apnea Hypopnea Syndrome classification in SpO2 signals using wavelet decomposition and phase space reconstruction

Abstract

Sleep Apnea Hypopnea Syndrome (SAHS) is a sleep disorder where patients experience multiple airflow cessations or reductions during the night. It is recognized as a common condition with a population prevalence of 1% to 6.5%. The Apnea Hypopnea Index (AHI) characterizes the severity of SAHS using signals obtained from Polysomnography (PSG); this requires the use of multiple sensors on the patient and an overnight hospital stay. The development of cheaper and more comfortable screening techniques involving wearable devices is, therefore, desirable. This paper presents a method based on wavelet decomposition and phase space reconstruction with embedding dimensions for feature extraction from oxygen saturation measured in SpO2 signals. The proposed characteristics are the areas spanned by each wavelet level in the phase space calculated using the convex hull algorithm. These areas are then fed into a classifier that groups the patients in categories of AHI higher or lower than 5. The results show an accuracy of 93% using K-Nearest Neighbors (Knn), and 88.61% using Least Square Support Vector Machines (LS-SVM).