Sleep Apnea Detection Based on Respiratory Curve Segmentation by Singular Spectrum Transformation

Abstract

This paper proposes a data analysis method for detecting abnormal breathing events during sleep from respiratory curves. The proposed method segments a respiration time series using Singular Spectral Transformations and classifies resulting partial respiratory curves based on their features consisting of autoregressive coefficients and respiratory amplitude ratios. Applying it to a polysomnography dataset confirmed that the proposed method could extract temporal patterns characteristic of respiratory abnormalities such as apnea and hypopnea.