The Pathogenesis of Central and Complex Sleep Apnea

Roberts, Erin Grattan; Raphelson, Janna R; Orr, Jeremy E.; LaBuzetta, Jamie Nicole; Malhotra, Atul

doi:10.1007/s11910-022-01199-2

Cited by 20 publications

(7 citation statements)

References 80 publications

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“…The occurrence of sleep transition apnea may yield immediate hyperventilation leading to central apnea upon resumption of sleep (Malhotra and Owens, 2010). This alternating pattern has been described as state instability in previous research (Roberts et al, 2022). In this example, most of these epochs had to be scored as wake, because the EEG data indicated wakefulness for more than half of each 30 s epoch.…”

Section: Discussionmentioning

confidence: 91%

Autonomic arousal detection and cardio-respiratory sleep staging improve the accuracy of home sleep apnea tests

Ross,

Fonseca,

Overeem

et al. 2023

Front. Physiol.

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Introduction: The apnea-hypopnea index (AHI), defined as the number of apneas and hypopneas per hour of sleep, is still used as an important index to assess sleep disordered breathing (SDB) severity, where hypopneas are confirmed by the presence of an oxygen desaturation or an arousal. Ambulatory polygraphy without neurological signals, often referred to as home sleep apnea testing (HSAT), can potentially underestimate the severity of sleep disordered breathing (SDB) as sleep and arousals are not assessed. We aim to improve the diagnostic accuracy of HSATs by extracting surrogate sleep and arousal information derived from autonomic nervous system activity with artificial intelligence.Methods: We used polysomnographic (PSG) recordings from 245 subjects (148 with simultaneously recorded HSATs) to develop and validate a new algorithm to detect autonomic arousals using artificial intelligence. A clinically validated auto-scoring algorithm (Somnolyzer) scored respiratory events, cortical arousals, and sleep stages in PSGs, and provided respiratory events and sleep stages from cardio-respiratory signals in HSATs. In a four-fold cross validation of the newly developed algorithm, we evaluated the accuracy of the estimated arousal index and HSAT-derived surrogates for the AHI.Results: The agreement between the autonomic and cortical arousal index was moderate to good with an intraclass correlation coefficient of 0.73. When using thresholds of 5, 15, and 30 to categorize SDB into none, mild, moderate, and severe, the addition of sleep and arousal information significantly improved the classification accuracy from 70.2% (Cohen’s κ = 0.58) to 80.4% (κ = 0.72), with a significant reduction of patients where the severity category was underestimated from 18.8% to 7.3%.Discussion: Extracting sleep and arousal information from autonomic nervous system activity can improve the diagnostic accuracy of HSATs by significantly reducing the probability of underestimating SDB severity without compromising specificity.

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Section: Discussionmentioning

confidence: 91%

Autonomic arousal detection and cardio-respiratory sleep staging improve the accuracy of home sleep apnea tests

Ross,

Fonseca,

Overeem

et al. 2023

Front. Physiol.

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“…( Figure 4 ). CSA is less prevalent and results from the abnormal regulation of breathing in the brainstem respiratory centers, which leads to an absence of or diminution in involuntary respiratory effort while asleep [ 84 ]. OSA is a highly prevalent sleep-related disorder characterized by the repetitive complete obstruction (apnea) or partial obstruction (hypopnea) of the upper airway that results from loss of muscle tone in anatomically susceptible persons [ 85 ].…”

Section: Sleep Apneamentioning

confidence: 99%

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Neri

Oberdier

Abeelen

et al. 2023

Sensors

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Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

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“…Loop gain can be further classified as controller gain, mixing gain and plant gain. Controller gain refers to the sensitivity of the system to changes in chemical stimuli like carbon dioxide and is governed by central and peripheral chemoreceptors (Orr et al, 2017;Roberts et al, 2022). High controller gain suggests that a given change in PaCO 2 will result in a greater change in ventilation, while a low controller gain will generate a lesser response in ventilation for the same change in PaCO 2 .…”

Section: Sleep Apnea Phenotypes and Control Of Respiration-the Import...mentioning

confidence: 99%

Enhanced expiratory rebreathing space for high loop gain sleep apnea treatment

Quinn,

Thomas,

Heckman

2023

Front. Sleep

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The pathophysiology of sleep apnea goes beyond anatomic predisposition to airway collapse and includes additional factors such as arousal threshold and loop gain. High loop gain is a prominent feature in central and complex sleep apnea (with a mixture of obstructive and central features) where relative hypocapnia can lead to respiratory instability and periodic breathing. Existing therapies, including continuous positive airway pressure (CPAP) and adaptive servo-ventilators, often inadequately treat sleep apnea with high loop gain features. Enhanced expiratory rebreathing space (EERS) targets prevention of the hypocapnia that triggers central events in sleep by increasing dead space in amounts less than typical tidal volumes. This is accomplished by covering traditional exhalation ports on positive airway pressure masks and adding small additional tubing with distal exhalation and safety valves. This technique reduces carbon dioxide (CO2) blow-off during arousals and the associated large recovery breaths, typically producing a maximal increase in resting CO2 by 1–2 mmHg, thus increasing the CO2 reserve and making it less likely to encounter the hypocapnic apneic threshold. Typically, the amount of EERS is titrated in response to central events and periodic breathing rather than aiming for a goal CO2 level. Ideally CO2 monitoring is used during titration of EERS and the technique is avoided in the setting of baseline hypercapnia. This method has been used in clinical practice at our sleep center for over 15 years, and retrospective data suggests an excellent safety profile and high rates of successful therapy including in patients who have previously failed CPAP therapy. Limitations include decreased effectiveness in the setting of leak and decreased tolerance of the bulkier circuit. EERS represents a simple, affordable modification of existing positive airway pressure modalities for treatment of central and complex sleep apnea. Areas of future study include randomized controlled trials of the technique and study of use of EERS in combination with adaptive ventilation, and pharmacologic adjuncts targeting high loop gain physiology.

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The Pathogenesis of Central and Complex Sleep Apnea

Cited by 20 publications

References 80 publications

Autonomic arousal detection and cardio-respiratory sleep staging improve the accuracy of home sleep apnea tests

Autonomic arousal detection and cardio-respiratory sleep staging improve the accuracy of home sleep apnea tests

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Enhanced expiratory rebreathing space for high loop gain sleep apnea treatment

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