Features of Cheyne-Stokes respiration automatically extracted from CPAP airflow signal raw data: Identification of discriminating features to detect heart failure

Abstract

BackgroundContinuous positive airway pressure (CPAP) telemonitoring data is collected daily from millions of sleep apnea patients. This huge amount of data can be used for the early detection of treatment failures and could reveal incident acute or chronic cardiovascular events. However, the available automatically computed metrics do not fully characterize sleep-disordered breathing. ObjectiveTo find methods to process metrics characterizing Cheyne-Stokes respiration (CSR) that are able to discriminate between CSR related to heart failure (HF) and CSR related to other underlying conditions. MethodsThe raw airflow signals recorded by CPAP devices were analysed. Change point detection methods were used to isolate each respiratory cycle and CSR cycle. Simple algorithms were implemented to extract key features from the signal. Binary logistic regression was performed to identify the characteristics of the CSR airflow signal that is associated with the presence of underlying HF. ResultsLonger CSR cycles, a longer CSR episode, a greater variation in the amplitude of inspiration, a smaller increase in big breaths, a lower inter-cycle variability and a shorter breath duration were features associated with the occurrence of CSR in the context of HF. ConclusionThe proposed automated computation of CSR characteristics presents a novel tool to include in the CPAP software or remote monitoring platforms for monitoring patients’ treatment and comorbidities; and a step towards cross-disciplinary patient management.