Detection of breathing segments in respiratory signals.

The typical approach for analysis of respiratory records consists of detection of respiratory pauses and elimination of segments corrupted by movement artifacts. This is motivated by established rules used for manual scoring of respiratory events, which focus on pause segmentation and do not define criteria to identify breathing segments. With this strategy, breathing segments can only be inferred indirectly from the absence of abnormalities, yielding an unclear and ambiguous definition. In this work we present novel detectors for synchronous and asynchronous breathing, and compare them with AUREA, a novel system for Automated Unsupervised Respiratory Event Analysis, which performs indirect classification of breathing. Results from analysis of real infant respiratory data show an improvement in the identification of synchronous and asynchronous breathing of 9% and 27% respectively, demonstrating that direct detection of breathing enhances the classification performance.