Breathing rate variability in obstructive sleep apnea during wakefulness

Annual review of selected scientific literature: A report of the Committee on Scientific Investigation of the American Academy of Restorative Dentistry

The Scientific Investigation Committee of the American Academy of Restorative Dentistry offers this review of the 2022 dental literature to briefly touch on several topics of interest to modern restorative dentistry. Each committee member brings discipline-specific expertise in their subject areas that include (in order of the appearance in this report): prosthodontics; periodontics, alveolar bone, and peri-implant tissues; dental materials and therapeutics; occlusion and temporomandibular disorders; sleep-related breathing disorders; oral medicine and oral and maxillofacial surgery; and dental caries and cariology. The authors focused their efforts on reporting information likely to influence the daily dental treatment decisions of the reader with an emphasis on innovations, new materials and processes, and future trends in dentistry. With the tremendous volume of literature published daily in dentistry and related disciplines, this review cannot be comprehensive. Instead, its purpose is to update interested readers and provide valuable resource material for those willing to subsequently pursue greater detail on their own. Our intent remains to assist colleagues in navigating the tremendous volume of newly minted information produced annually. Finally, we hope that readers find this work helpful in managing patients.

Interictal respiratory variability predicts severity of hypoxemia after generalized convulsive seizures

OBJECTIVE

Severe respiratory dysfunction induced by generalized convulsive seizures (GCS) is now thought to be a common mechanism for sudden unexpected death in epilepsy (SUDEP). In a mouse model of seizure-induced death, increased interictal respiratory variability was reported in mice that later died of respiratory arrest after GCS. We studied respiratory variability in epilepsy patients as a predictive tool for severity of postictal hypoxemia, a potential biomarker for SUDEP risk. We then explored the relationship between respiratory variability and central CO2 drive, measured by the hypercapnic ventilatory response (HCVR).

METHODS

We reviewed clinical, video-electroencephalography, and respiratory (belts, airflow, pulse oximeter, and HCVR) data of epilepsy patients. Mean, SD, and coefficient of variation (CV) of interbreath interval (IBI) were calculated. Primary outcomes were: (1) nadir of capillary oxygen saturation (SpO2 ) and (2) duration of oxygen desaturation. Poincaré plots of IBI were created. Covariates were evaluated in univariate models, then, based on Akaike information criteria (AIC), multivariate regression models were created.

RESULTS

Of 66 GCS recorded in 131 subjects, 30 had interpretable respiratory data. In the multivariate model with the lowest AIC value, duration of epilepsy was a significant predictor of duration of oxygen desaturation. Duration of tonic phase and CV of IBI during the third postictal minute correlated with SpO2 nadir, whereas CV of IBI during non-rapid eye movement sleep had a negative correlation. Poincaré plots showed that long-term variability was significantly greater in subjects with ≥200 s of postictal oxygen desaturation after GCS compared to those with <200 s desaturation. Finally, HCVR slope showed a negative correlation with measures of respiratory variability.

SIGNIFICANCE

These results indicate that interictal respiratory variability predicts severity of postictal oxygen desaturation, suggesting its utility as a potential biomarker. They also suggest that interictal respiratory control may be abnormal in some patients with epilepsy.

Temporal Feature Extraction and Machine Learning for Classification of Sleep Stages Using Telemetry Polysomnography

Accurate sleep stage detection is crucial for diagnosing sleep disorders and tailoring treatment plans. Polysomnography (PSG) is considered the gold standard for sleep assessment since it captures a diverse set of physiological signals. While various studies have employed complex neural networks for sleep staging using PSG, our research emphasises the efficacy of a simpler and more efficient architecture. We aimed to integrate a diverse set of feature extraction measures with straightforward machine learning, potentially offering a more efficient avenue for sleep staging. We also aimed to conduct a comprehensive comparative analysis of feature extraction measures, including the power spectral density, Higuchi fractal dimension, singular value decomposition entropy, permutation entropy, and detrended fluctuation analysis, coupled with several machine-learning models, including XGBoost, Extra Trees, Random Forest, and LightGBM. Furthermore, data augmentation methods like the Synthetic Minority Oversampling Technique were also employed to rectify the inherent class imbalance in sleep data. The subsequent results highlighted that the XGBoost classifier, when used with a combination of all feature extraction measures as an ensemble, achieved the highest performance, with accuracies of 87%, 90%, 93%, 96%, and 97% and average F1-scores of 84.6%, 89%, 90.33%, 93.5%, and 93.5% for distinguishing between five-stage, four-stage, three-stage, and two distinct two-stage sleep configurations, respectively. This combined feature extraction technique represents a novel addition to the body of research since it achieves higher performance than many recently developed deep neural networks by utilising simpler machine-learning models.

Characterisation of the acute hypoxic response using breathing variability parameters: a pilot study in humans

PURPOSE

We aimed to investigate respiratory rate variability (RRV) and tidal volume (V_t) variability during exposure to normobaric hypoxia (i.e., reduction in the fraction of inspired oxygen - FiO₂), and the association of the changes in RRV and V_t variability with the changes in pulse oxygen saturation (SpO₂).

METHODS

Thirty healthy human participants (15 females) were exposed to: (1) 15-min normoxia, (2) 10-min hypoxia simulating 2200m, (3) 10-min hypoxia simulating 4000m, (4) 10-min hypoxia simulating 5000m, (5) 15-min recovery in normoxia. Linear regression modelling was applied with SpO₂ (dependent variable) and the changes in RRV and V_t variability (independent variables), controlling for FiO₂, age, sex, changes in heart rate (HR), changes in HR variability (HRV), and changes in minute ventilation (V_E).

RESULTS

When modelling breathing parameter variability as root-mean-square standard deviation (RMSSD), a significant independent association of the changes in RRV with the changes in SpO₂ was found (B=-4.3e-04, 95% CI=-8.3e-04/-2.1e-05, p=0.04). The changes in V_t variability showed no significant association with the changes in SpO₂ (B=-1.6, 95% CI=-5.5/2.4, p=0.42). When modelling parameters variability as SD, a significant independent association of the changes in RRV with the changes in SpO₂ was found (B=-8.2e-04, 95% CI=-1.5e-03/-9.4e-05, p=0.03). The changes in V_t variability showed no significant association with the changes in SpO₂ (B=1.4, 95% CI=-5.8/8.6, p=0.69).

CONCLUSION

Higher RRV is independently associated with lower SpO₂ during acute hypoxic exposure, while V_t variability parameters are not. Therefore, RRV may be a potentially interesting parameter to characterize individual responses to acute hypoxia.