Accordingly, several algorithms have been developed for automated breath detection. Manual detection and calculation of respiratory parameters are time-consuming and potentially prone to human error and impractical for large data sets. Physiological events such as sighs, swallows, transient reductions and pauses (hypopneas and apneas) in breathing during sleep recordings, as well as measurement artifacts including signal drift, EKG artifact, electrical noise on the airflow signal and mask leaks, each present unique challenges when attempting to quantify breath timing accurately. ĭespite its importance, breath detection is technically challenging. respiratory sinus arrhythmia and cardiorespiratory synchronization ), and neurological function. For instance, breath detection is required to characterize interactions between the respiratory system and other organs such as the heart (e.g. Breath detection also has multiple applied and cross-disciplinary applications. Indeed, in order to quantify key breathing variables such as minute ventilation and peak inspiratory airflow, accurate identification of inspiration and expiration is required. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist.Īccurate breath detection is crucial in sleep and respiratory physiology research and in clinical practice. Eckert has received support from a NHMRC RD Wright Fellowship, 1049814 ( ). Carberry received support from a National Health and Medical Research Council (NHMRC) of Australia NeuroSleep Centre of Research Excellence Fellowship (1060992). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper and its Supporting Information files.įunding: C. Received: DecemAccepted: Published: June 13, 2017Ĭopyright: © 2017 Nguyen et al. PLoS ONE 12(6):Įditor: Mathias Baumert, University of Adelaide, AUSTRALIA This new algorithm can be used for accurate breath detection including during variable mask pressure conditions which represents a major advance over existing time-consuming manual approaches.Ĭitation: Nguyen CD, Amatoury J, Carberry JC, Eckert DJ (2017) An automated and reliable method for breath detection during variable mask pressures in awake and sleeping humans. The algorithm had excellent performance in response to baseline drifts and noise during variable mask pressure conditions. ![]() Using the Pepi signal, the algorithm correctly identified 89% of the breaths with accuracy of 31☑56ms for inspiration and 9☑47ms for expiration compared to expert visual detection during variable mask pressures asleep. Using the flow signal, the algorithm correctly identified 97.6% of breaths with a mean difference±SD in the onsets of respiratory phase compared to expert visual detection of 23☘9ms for inspiration and 6±56ms for expiration during wakefulness and 10☗4ms for inspiration and 3☒8 ms for expiration with variable mask pressures during sleep. The algorithms were validated using simulated data from a mathematical model and against the standard visual detection approach in 4 healthy individuals and 6 patients with sleep apnea during variable mask pressure conditions. This paper presents a new algorithm for breath detection during variable mask pressures in awake and sleeping humans based on physiological landmarks detected in the airflow or epiglottic pressure signal (Pepi). However, this is an empirical operation potentially prone to human error. Traditional algorithms often require drift correction. This presents an additional unique challenge for breath detection. Recently developed techniques to quantify the multiple causes of obstructive sleep apnea, require intermittent changes in airway pressure applied to a breathing mask. ![]() However, this process is technically challenging due to measurement and physiological artifacts and other factors such as variable leaks in the breathing circuit. Accurate breath detection is crucial in sleep and respiratory physiology research and in several clinical settings.
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