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Objective: The aim of this study was to assess how the photoplethysmogram frequency and amplitude responses to arousals from sleep differ between arousals caused by apneas and hypopneas with and without blood oxygen desaturations, and spontaneous arousals. Stronger arousal causes were hypothesized to lead to larger and faster responses., Methods and Procedures: Photoplethysmogram signal segments during and around respiratory and spontaneous arousals of 876 suspected obstructive sleep apnea patients were analyzed. Logistic functions were fit to the mean instantaneous frequency and instantaneous amplitude of the signal to detect the responses. Response intensities and timings were compared between arousals of different causes., Results: The majority of the studied arousals induced photoplethysmogram responses. The frequency response was more intense ([Formula: see text]) after respiratory than spontaneous arousals, and after arousals caused by apneas compared to those caused by hypopneas. The amplitude response was stronger ([Formula: see text]) following hypopneas associated with blood oxygen desaturations compared to those that were not. The delays of these responses relative to the electroencephalogram arousal start times were the longest ([Formula: see text]) after arousals caused by apneas and the shortest after spontaneous arousals and arousals caused by hypopneas without blood oxygen desaturations., Conclusion: The presence and type of an airway obstruction and the presence of a blood oxygen desaturation affect the intensity and the timing of photoplethysmogram responses to arousals from sleep., Clinical Impact: The photoplethysmogram responses could be used for detecting arousals and assessing their intensity, and the individual variation in the response intensity and timing may hold diagnostically significant information., (© 2024 The Authors.)

Objective: Hypoxic load is one of the main characteristics of obstructive sleep apnea (OSA) contributing to sympathetic overdrive and weakened cardiorespiratory coupling (CRC). Whether this association changes with increasing hypoxic load has remained obscure. Therefore, we aimed to study our hypothesis that increasing hypoxic load acutely decreases the CRC., Methods: We retrospectively analyzed the electrocardiography and nasal pressure signals in 5-min segment pairs (n = 36 926) recorded during clinical polysomnographies of 603 patients with suspected OSA. The segment pairs were pooled into five groups based on the hypoxic load severity described with the the total integrated area under the blood oxygen saturation curve during desaturations. In these severity groups, we determined the frequency-domain heart rate variability (HRV) parameters, the HRV and respiratory high-frequency (HF, 0.15-0.4 Hz) peaks, and the difference between those peaks. We also computed the spectral HF coherence between HRV and respiration in the HF band., Results: The ratio of low-frequency (LF, 0.04-0.15 Hz) to HF power increased from 1.047 to 1.805 (p < 0.001); the difference between the HRV and respiratory HF peaks increased from 0.001 Hz to 0.039 Hz (p < 0.001); and the spectral coherence between HRV and respiration in the HF band decreased from 0.813 to 0.689 (p < 0.001) as the hypoxic load increased., Conclusion and Significance: The vagal modulation decreases and CRC weakens significantly with increasing hypoxic load. Thus, the hypoxic load could be utilized more thoroughly in contemporary OSA diagnostics to better assess the severity of OSA-related cardiac stress.

Objective: We aimed to investigate how acute and long-term effects of atrial arrhythmias affect the desaturation severity and characteristics determined from the oxygen saturation signal in obstructive sleep apnea (OSA) patients., Methods: 520 suspected OSA patients were included in retrospective analyses. Eight desaturation area and slope parameters were calculated from blood oxygen saturation signals recorded during polysomnographic recordings. Patients were grouped based on whether they had previously diagnosed atrial arrhythmia (i.e., atrial fibrillation (AFib) or atrial flutter) or not. Furthermore, patients with a previous atrial arrhythmia diagnosis were sub-grouped based on whether they had continuous AFib or sinus rhythm during the polysomnographic recordings. Empirical cumulative distribution functions and linear mixed models were utilized to investigate the connection between diagnosed atrial arrhythmia and the desaturation characteristics., Results: Patients with previous atrial arrhythmia diagnosis had greater desaturation recovery area when the 100% oxygen saturation baseline reference was considered (β = 0.150--0.127, p ≤ 0.039) and more gradual recovery slopes (β = -0.181 to -0.199, p < 0.004) than patients without a previous atrial arrhythmia diagnosis. Furthermore, patients with AFib had more gradual oxygen saturation fall and recovery slopes than patients with sinus rhythm., Conclusion: Desaturation recovery characteristics in the oxygen saturation signal contains essential information about the cardiovascular response to hypoxemic periods., Significance: More comprehensive consideration of the desaturation recovery section could provide more detailed information about OSA severity, for example when developing new diagnostic parameters.

Objective: Obstructive sleep apnea (OSA) is diagnosed using the apnea-hypopnea index (AHI), which is the average number of respiratory events per hour of sleep. Recently, machine learning algorithms for automatic AHI assessment have been developed, but many of them do not consider the individual sleep stages or events. In this study, we aimed to develop a deep learning model to simultaneously score both sleep stages and respiratory events. The hypothesis was that the scoring and subsequent AHI calculation could be performed utilizing pulse oximetry data only., Methods: Polysomnography recordings of 877 individuals with suspected OSA were used to train the deep learning models. The same architecture was trained with three different input signal combinations (model 1: photoplethysmogram (PPG) and oxygen saturation (SpO 2 ); model 2: PPG, SpO 2 , and nasal pressure; model 3: SpO 2 , nasal pressure, electroencephalogram (EEG), oronasal thermocouple, and respiratory belts)., Results: Model 1 reached comparative performance with models 2 and 3 for estimating the AHI (model 1 intraclass correlation coefficient (ICC) = 0.946; model 2 ICC = 0.931; model 3 ICC = 0.945), and REM-AHI (model 1 ICC = 0.912; model 2 ICC = 0.921; model 3 ICC = 0.883). The automatic sleep staging accuracies (wake/N1/N2/N3/REM) were 69%, 70%, and 79% with models 1, 2, and 3, respectively., Conclusion: AHI can be estimated using pulse oximetry-based automatic scoring. Explicit scoring of sleep stages and respiratory events allows visual validation of the automatic analysis, and provides information on OSA phenotypes., Significance: Automatic scoring of sleep stages and respiratory events with a simple pulse oximetry setup could allow cost-effective, large-scale screening of OSA.

Objective: We aimed to investigate the differences in electroencephalogram (EEG) gamma power (30-40 Hz) of respiratory arousals between varying types and severities of respiratory events, and in different sleep stages., Methods: Power spectral densities of EEG signals from diagnostic Type I polysomnograms of 869 patients with clinically suspected obstructive sleep apnea were investigated. Arousal gamma powers were compared between sleep stages, and between the type (obstructive apnea and hypopnea) and duration (10-20 s, 20-30 s, and >30 s) of the related respiratory event. Moreover, we investigated whether the presence of a ≥3% blood oxygen desaturation influenced the arousal gamma power., Results: Gamma power of respiratory arousals was the lowest in Stage R sleep and increased from Stage N1 towards Stage N3. Gamma power was higher when the arousals were caused by obstructive apneas compared to hypopneas. Moreover, arousal gamma power increased when the duration of the related apnea increased, whereas an increase in the hypopnea duration did not have a similar effect. Furthermore, respiratory events associated with desaturations increased the arousal gamma power more than respiratory events not associated with desaturations., Conclusion: Gamma power of respiratory arousals increased towards deeper sleep and as the severity of the related respiratory event increased in terms of type and duration of obstruction, and presence of desaturation., Significance: As increased gamma power might indicate a greater shift towards wakefulness, the present findings demonstrate that the respiratory arousal intensity and the magnitude of sleep disruption may vary depending on the event type and severity.