The design of RIP belts impacts the reliability and quality of the measured respiratory signals.

PURPOSE: Evaluate the effect of respiratory inductance plethysmography (RIP) belt design on the reliability and quality of respiratory signals. A comparison of cannula flow to disposable cut-to-fit, semi-disposable folding and disposable RIP belts was performed in clinical home sleep apnea testing (HSAT) studies. METHODS: This was a retrospective study using clinical HSAT studies. The signal reliability of cannula, thorax, and abdomen RIP belts was determined by automatically identifying periods during which the signals did not represent respiratory airflow and breathing movements. Results were verified by manual scoring. RIP flow quality was determined by examining the correlation between the RIP flow and cannula flow when both signals were considered reliable. RESULTS: Of 767 clinical HSAT studies, mean signal reliability of the cut-to-fit, semi-disposable, and disposable thorax RIP belts was 83.0 ± 26.2%, 76.1 ± 24.4%, and 98.5 ± 9.3%, respectively. The signal reliability of the cannula was 92.5 ± 16.1%, 87.0 ± 23.3%, and 85.5 ± 24.5%, respectively. The automatic assessment of signal reliability for the RIP belts and cannula flow had a sensitivity of 50% and a specificity of 99% compared with manual assessment. The mean correlation of cannula flow to RIP flow from the cut-to-fit, semi-disposable, and disposable RIP belts was 0.79 ± 0.24, 0.52 ± 0.20, and 0.86 ± 0.18, respectively. CONCLUSION: The design of RIP belts affects the reliability and quality of respiratory signals. The disposable RIP belts that had integrated contacts and did not fold on top of themselves performed the best. The cut-to-fit RIP belts were most likely to be unreliable, and the semi-disposable folding belts produced the lowest-quality RIP flow signals compared to the cannula flow signal.

BreathFinder: A Method for Non-Invasive Isolation of Respiratory Cycles Utilizing the Thoracic Respiratory Inductance Plethysmography Signal.

Introduction: The field of automatic respiratory analysis focuses mainly on breath detection on signals such as audio recordings, or nasal flow measurement, which suffer from issues with background noise and other disturbances. Here we introduce a novel algorithm designed to isolate individual respiratory cycles on a thoracic respiratory inductance plethysmography signal using the non-invasive signal of the respiratory inductance plethysmography belts. Purpose: The algorithm locates breaths using signal processing and statistical methods on the thoracic respiratory inductance plethysmography belt and enables the analysis of sleep data on an individual breath level. Patients and Methods: The algorithm was evaluated against a cohort of 31 participants, both healthy and diagnosed with obstructive sleep apnea. The dataset consisted of 13 female and 18 male participants between the ages of 20 and 69. The algorithm was evaluated on 7.3 hours of hand-annotated data from the cohort, or 8782 individual breaths in total. The algorithm was specifically evaluated on a dataset containing many sleep-disordered breathing events to confirm that it did not suffer in terms of accuracy when detecting breaths in the presence of sleep-disordered breathing. The algorithm was also evaluated across many participants, and we found that its accuracy was consistent across people. Source code for the algorithm was made public via an open-source Python library. Results: The proposed algorithm achieved an estimated 94% accuracy when detecting breaths in respiratory signals while producing false positives that amount to only 5% of the total number of detections. The accuracy was not affected by the presence of respiratory related events, such as obstructive apneas or snoring. Conclusion: This work presents an automatic respiratory cycle algorithm suitable for use as an analytical tool for research based on individual breaths in sleep recordings that include respiratory inductance plethysmography.

Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls.

Introduction: Visual sleep scoring has several shortcomings, including inter-scorer inconsistency, which may adversely affect diagnostic decision-making. Although automatic sleep staging in adults has been extensively studied, it is uncertain whether such sophisticated algorithms generalize well to different pediatric age groups due to distinctive EEG characteristics. The preadolescent age group (10-13-year-olds) is relatively understudied, and thus, we aimed to develop an automatic deep learning-based sleep stage classifier specifically targeting this cohort. Methods: A dataset (n = 115) containing polysomnographic recordings of Icelandic preadolescent children with sleep-disordered breathing (SDB) symptoms, and age and sex-matched controls was utilized. We developed a combined convolutional and long short-term memory neural network architecture relying on electroencephalography (F4-M1), electrooculography (E1-M2), and chin electromyography signals. Performance relative to human scoring was further evaluated by analyzing intra- and inter-rater agreements in a subset (n = 10) of data with repeat scoring from two manual scorers. Results: The deep learning-based model achieved an overall cross-validated accuracy of 84.1% (Cohen's kappa κ = 0.78). There was no meaningful performance difference between SDB-symptomatic (n = 53) and control subgroups (n = 52) [83.9% (κ = 0.78) vs. 84.2% (κ = 0.78)]. The inter-rater reliability between manual scorers was 84.6% (κ = 0.78), and the automatic method reached similar agreements with scorers, 83.4% (κ = 0.76) and 82.7% (κ = 0.75). Conclusion: The developed algorithm achieved high classification accuracy and substantial agreements with two manual scorers; the performance metrics compared favorably with typical inter-rater reliability between manual scorers and performance reported in previous studies. These suggest that our algorithm may facilitate less labor-intensive and reliable automatic sleep scoring in preadolescent children.

Toward Sleep Study Automation: Detection Evaluation of Respiratory-Related Events.

The diagnosis of sleep disordered breathing depends on the detection of respiratory-related events: apneas, hypopneas, snores, or respiratory event-related arousals from sleep studies. While a number of automatic detection methods have been proposed, their reproducibility has been an issue, in part due to the absence of a generally accepted protocol for evaluating their results. With sleep measurements this is usually treated as a classification problem and the accompanying issue of localization is not treated as similarly critical. To address these problems we present a detection evaluation protocol that is able to qualitatively assess the match between two annotations of respiratory-related events. This protocol relies on measuring the relative temporal overlap between two annotations in order to find an alignment that maximizes their F1-score at the sequence level. This protocol can be used in applications which require a precise estimate of the number of events, total event duration, and a joint estimate of event number and duration. We assess its application using a data set that contains over 10,000 manually annotated snore events from 9 subjects, and show that when using the American Academy of Sleep Medicine Manual standard, two sleep technologists can achieve an F1-score of 0.88 when identifying the presence of snore events. In addition, we drafted rules for marking snore boundaries and showed that one sleep technologist can achieve F1-score of 0.94 at the same tasks. Finally, we compared this protocol against the protocol that is used to evaluate sleep spindle detection and highlighted the differences.