A Study on Anomaly Detection from Respiratory Motion Curves Based on Time-series Clustering Method

Abstract

In polysomnography, a sleep technologist visually analyzes a time series of respiratory movements, i.e., the respiratory curve, measured with a RIP belt or other equipment to record abnormal respiratory events such as apneas and hypopneas. The present study aims to develop a method for detecting peculiar patterns in the respiratory curve to automate this analysis process. Our approach here is to divide the respiratory curve into partial time series using Singular Spectrum Transformation, then classify them based on the similarity of the time series patterns to identify essential features that characterize abnormal respiratory motions. This paper presents our investigation into applying the respiratory motion patterns obtained by a time series clustering method to detect abnormal respirations.