Quantifying respiratory variation with force sensor measurements

Introduction Differences in neuroventilatory control can impact the type and severity of sleep disordered breathing between non-REM and REM sleep. We examined the distribution of central apneic episodes in polysomnograms from subjects with predominantly central sleep apnea, who received phrenic nerve stimulation. Methods Baseline in-lab polysomnograms from patients enrolled in the remedē System Pivotal Trial were scored by a central core laboratory (n=151). Participants with predominantly CSA were enrolled if the apnea-hypopnea index (AHI)≥20/hr, the central apnea index (CAI) exceeded the obstructive apnea index (OAI), and the OAI did not exceed 20% of the total AHI. This post-hoc analysis compared sleep apnea indices in REM and non-REM sleep in those with ≥5 minutes REM sleep (n=131). Within-patient median non-REM - REM differences were calculated and compared. Results REM sleep time was 40 [Q1=28, Q3=64] minutes, and non-REM sleep time was 301 [Q1=269,Q3=344] minutes. AHI in REM sleep was 22/hr [Q1=9,Q3=44] and was 46/hr (Q1=33,Q3=60) in non-REM sleep, yielding a within-patient difference between non-REM and REM sleep of 22/hr ([Q1=6, Q3=34], p<.001). CAI in REM was only 4/hr (Q1=0, Q3=11), but in non-REM was 25/hr [Q1=16,Q3=43] with all patients having a CAI≥5/hr during non-REM. In REM, 70% had a CAI>0/hr and 46% had a CAI ≥5/hr. The CAI difference between non-REM and REM sleep was 18/hr [Q1=10, Q3=30; p<.001]. The OAI and mixed apnea index (MAI) differed by <1/hr (p=0.235 and <.001, respectively). Of note, the hypopnea index [HI] did not differ between REM and non-REM sleep (12/hr [Q1=2,Q3=22] vs. 11/hr [Q1=4, Q3=20], respectively, p=0.273), yet hypopneas constituted a greater proportion of total AHI during REM compared to non-REM sleep. Conclusion Among subjects with predominantly CSA, the prevalence and severity of CSA was greater in non-REM than REM sleep, yet low-level CSA persisted in REM sleep. Stage-related differences in CAI but not OAI, MAI or HI can be attributed to alterations in ventilatory rather than upper airway control in this CSA cohort. These differences in the type and severity of sleep disordered breathing episodes comprise a key diagnostic signature, for which specific CSA therapeutic strategies are indicated. Support (If Any) ZOLL Respicardia, Inc. and NIH R01 HL144859

Sleep of paradoxes.

Night-to-night variability of obstructive sleep apnea severity is considerable and may depend on the diagnostic modality used. We investigated the night-to-night variability using peripheral arterial tonometry (PAT). Home sleep apnea testing was performed in 51 patients during 3 consecutive nights using PAT. Patients referred to our sleep clinic were screened and prospectively recruited for this study. All recordings were automatically and manually scored according to the PAT scoring guidelines. No systematic differences in PAT-derived apnea-hypopnea index (pAHI) were found between the nights. The night-to-night variability was comparable between manually and automatically scored data. pAHI varied in 35% of patients more than 10 events/h between the nights. The obstructive sleep apnea severity of 24% of patients was misclassified when using 1 night compared to the average of all nights. On average, pAHI varied by 57% from night to night. The variability of pAHI could partially be explained by the variability of time spent in the supine position with more time supine leading to a higher pAHI. On measuring a subsequent night, 12-14% of patients spontaneously fulfilled the commonly accepted criteria for treatment success without any intervention. With repeated recordings of PAT, we found no first night effect. However, there is considerable night-to-night variability similar to values found for polysomnography, which can partially be explained by the variability of time spent in the supine position. Obstructive sleep apnea severity was frequently misclassified due to the night-to-night variability. Our findings make a strong case for multiple testing in the diagnostic work-up of obstructive sleep apnea patients. Tschopp S, Wimmer W, Caversaccio M, Borner U, Tschopp K. Night-to-night variability in obstructive sleep apnea using peripheral arterial tonometry: a case for multiple night testing. J Clin Sleep Med. 2021;17(9):1751-1758.

What is the central question of this study? A transcranial magnetic stimulation (TMS)-induced twitch applied on isolated single breaths over the motor cortex somatotopic representation of the tongue briefly recruits submental muscles and improves airflow dynamics of flow-limited respiratory cycles without arousing sleep apnoea patients. However, the mechanical impact of the TMS-induced twitch applied during consecutive breathing cycles on airflow dynamics remains unknown. What is the main finding and what is its importance? Our results show that application of TMS with the stimulator output set at the sleep submental motor threshold intensity on consecutive respiratory cycles increases inspiratory flow and reduces the turbulent airflow component. These results indicate an improvement of airflow pattern after two single consecutive TMS-induced twitches without arousing sleep apnoea patients. Transcranial magnetic stimulation (TMS)-induced twitches applied on isolated breaths briefly recruit upper airway dilator muscles and improve airflow and inspiratory volume without arousing apnoeic patients from sleep, but the effects of applying such twitches consecutively on airflow dynamics is unknown. The objective of this study was to quantify the effects of five consecutive TMS-induced twitches applied on sleep-induced obstructive hypopnoeic breaths in 10 obstructive sleep apnoea patients. Submental muscle motor threshold (SUBMT) and motor-evoked potential were measured during wakefulness and sleep. The TMS-induced twitches were applied during stable non-rapid eye movement (NREM) sleep, at the beginning of inspiration of consecutive flow-limited respiratory cycles, with the stimulator output set at sleep SUBMT. Maximal inspiratory flow, inspiratory volume, inspiratory time, shifts of electroencephalogram frequency and pulse rate variability were assessed. During sleep, SUBMT increased (wakefulness, 25.3 ± 4.9%; NREM sleep, 27.0 ± 6.2%; P = 0.02). During each series of stimulations there was a rise in maximal inspiratory flow (from 306.7 ± 123.2 to 359.8 ± 154.1 ml s(-1); P = 0.0002) and in inspiratory volume (from 346.1 ± 128.1 to 414.9 ± 171.2 ml; P = 0.02) without differences in thoraco-abdominal efforts and inspiratory time. These responses were observed in the absence of arousals and ceased immediately after TMS interruption. Transcranial magnetic stimulation-induced cortical and/or autonomic arousal was observed in 30.2% of all series of stimulation. Consecutive twitch TMS of submental muscles may lead to arousals in a minority of patients but can be applied on consecutive respiratory cycles during sleep and can significantly improve maximal inspiratory flow and inspiratory volume of flow-limited cycles.

Rationale: Sleep-disordered breathing (SDB) occurring primarily during rapid eye movement (REM) sleep is a common clinical problem. The natural history of REM-related SDB and the associated cardiovascular sequelae of disease progression remain to be determined.Objectives: The objective of the current study was to describe the natural history of REM-related SDB, ascertain predictors of progression, and determine whether the evolution of REM-related SDB into non-REM (NREM) sleep is associated with incident cardiovascular events.Methods: Participants from the Sleep Heart Health Study with a baseline NREM apnea-hypopnea index (NREM-AHI) of <5 events/h and data from a follow-up sleep study along with information on incident cardiovascular disease were included in the study. Bivariate logistic regression was used to jointly model the predictors of disease progression based on the presence or absence of SDB during NREM and REM sleep using a cut-point of 5 events/h. Explanatory variables such as age, race, body mass index (BMI), change in BMI, and baseline REM-AHI were considered. Proportional hazards regression was then used to establish whether the development of SDB during NREM sleep was associated with incident cardiovascular disease.Results: The majority of the 1,908 participants included in the study did not develop SDB during NREM sleep. The likelihood of progression of SDB into NREM sleep did increase with higher baseline REM-AHI. BMI and an increase in BMI predicted progression of SDB in both NREM and REM sleep in men but not in women. There was a strong interdependence between developing a NREM-AHI of ≥5 events/h and worsening REM-AHI at follow-up with odds ratios of 6.01 and 4.47, in women and men, respectively. Moreover, the relative risk for incident cardiovascular events among those who developed a NREM-AHI of ≥5 events/h at the follow-up visit was elevated only in women with REM-related SDB at baseline.Conclusions: SDB during REM sleep is a relatively stable condition and does not progress in the majority of individuals. Progression of SDB into NREM sleep is associated with sex, weight, and age. SDB during REM and NREM sleep tends to develop concurrently. Finally, the development of SDB during NREM sleep is associated with incident cardiovascular events, but only in women with REM-related SDB at baseline.

Rapid eye movement sleep behaviour disorder is characterized by dream-enacting behaviour and impaired motor inhibition during rapid eye movement sleep. Rapid eye movement sleep behaviour disorder is commonly associated with neurodegenerative disorders, but also reported in narcolepsy with cataplexy. Most narcolepsy with cataplexy patients lack the sleep-wake, and rapid eye movement sleep, motor-regulating hypocretin neurons in the lateral hypothalamus. In contrast, rapid eye movement sleep behaviour disorder and hypocretin deficiency are rare in narcolepsy without cataplexy. We hypothesized that rapid eye movement sleep behaviour disorder coexists with cataplexy in narcolepsy due to hypocretin deficiency. In our study, rapid eye movement sleep behaviour disorder was diagnosed by the International Classification of Sleep Disorders (2nd edition) criteria in 63 narcolepsy patients with or without cataplexy. Main outcome measures were: rapid eye movement sleep behaviour disorder symptoms; short and long muscle activations per hour rapid eye movement and non-rapid eye movement sleep; and periodic and non-periodic limb movements per hour rapid eye movement and non-rapid eye movement sleep. Outcome variables were analysed in relation to cataplexy and hypocretin deficiency with uni- and multivariate logistic/linear regression models, controlling for possible rapid eye movement sleep behaviour disorder biasing factors (age, gender, disease duration, previous anti-cataplexy medication). Only hypocretin deficiency independently predicted rapid eye movement sleep behaviour disorder symptoms (relative risk = 3.69, P = 0.03), long muscle activations per hour rapid eye movement sleep (ln-coefficient = 0.81, P < 0.01), and short muscle activations per hour rapid eye movement sleep (ln-coefficient = 1.01, P < 0.01). Likewise, periodic limb movements per hour rapid eye movement and non-rapid eye movement sleep were only associated with hypocretin deficiency (P < 0.01). A significant association between hypocretin deficiency and cataplexy was confirmed (P < 0.01). In a sub-analysis, hypocretin deficiency suggested the association of periodic limb movements and rapid eye movement sleep behaviour disorder outcomes (symptoms, non-periodic short and long muscle activity) in rapid eye movement sleep. Our results support the hypothesis that hypocretin deficiency is independently associated with rapid eye movement sleep behaviour disorder in narcolepsy. Thus, hypocretin deficiency is linked to the two major disturbances of rapid eye movement sleep motor regulation in narcolepsy: rapid eye movement sleep behaviour disorder and cataplexy. Hypocretin deficiency is also significantly associated with periodic limb movements in rapid eye movement and non-rapid eye movement sleep, and provides a possible pathophysiological link between rapid eye movement sleep behaviour disorder and periodic limb movements in narcolepsy. The study supports the hypothesis that an impaired hypocretin system causes a general instability of motor regulation during wakefulness, rapid eye movement and non-rapid eye movement sleep in human narcolepsy.

Photoplethysmography (PPG) is a non-invasive optical way of measuring variations in blood volume and perfusion in the tissue, used in pulse oximetry for instance. Respiratory-induced intensity variations (RIIVs) in the PPG signal exist, but the physiological background is not fully understood. Respiration causes variations in the blood volume in the peripheral vascular bed. It was hypothesised that the filling of peripheral veins is one of the important factors involved. In 16 healthy subjects, the respiratory synchronous variations from a PPG reflection mode signal and the peripheral venous pressure (PVP) were recorded. Variations of tidal volume, respiratory rate and contribution from abdominal and thoracic muscles gave significant and similar amplitude changes in both RIIV and the respiratory variation of PVP (p<0.01). The highest amplitudes of both signals were found at the largest tidal volume, lowest respiratory rate and during mainly thoracic breathing, respectively. The coherence between PVP and RIIV signals was high, the median (quartile range) being 0.78 (0.42). Phase analysis showed that RIIV was usually leading PVP, but variations between subjects were large. Although respiratory-induced variations in PVP and PPG showed a close correlation in amplitude variation, a causal relationship between the signals could not be demonstrated.

Birds exhibit two types of sleep that are in many respects similar to mammalian rapid eye movement (REM) and non-REM (NREM) sleep. As in mammals, several aspects of avian sleep can occur in a local manner within the brain. Electrophysiological evidence of NREM sleep occurring more deeply in one hemisphere, or only in one hemisphere – the latter being a phenomenon most pronounced in dolphins – was actually first described in birds. Such asymmetric or unihemispheric NREM sleep occurs with one eye open, enabling birds to visually monitor their environment for predators. Frigatebirds primarily engage in this form of sleep in flight, perhaps to avoid collisions with other birds. In addition to interhemispheric differences in NREM sleep intensity, the intensity of NREM sleep is homeostatically regulated in a local, use-depended manner within each hemisphere. Furthermore, the intensity and temporo-spatial distribution of NREM sleep-related slow waves varies across layers of the avian hyperpallium – a primary visual area – with the slow waves occurring first in, and propagating through and outward from, thalamic input layers. Slow waves also have the greatest amplitude in these layers. Although most research has focused on NREM sleep, there are also local aspects to avian REM sleep. REM sleep-related reductions in skeletal muscle tone appear largely restricted to muscles involved in maintaining head posture. Other local aspects of sleep manifest as a mixture of features of NREM and REM sleep occurring simultaneously in different parts of the neuroaxis. Like monotreme mammals, ostriches often exhibit brainstem-mediated features of REM sleep (muscle atonia and REMs) while the hyperpallium shows EEG slow waves typical of NREM sleep. Finally, although mice show slow waves in thalamic input layers of primary sensory cortices during REM sleep, this is not the case in the hyperpallium of pigeons, suggesting that this phenomenon is not a universal feature of REM sleep. Collectively, the local aspects of sleep described in birds and mammals reveal that wakefulness, NREM sleep, and REM sleep are not always discrete states.

Chapter 2 NREM–REM sleep

A salient feature of mammalian sleep is the alternation between rapid eye movement (REM) and non-REM (NREM) sleep. However, how these two sleep stages influence each other and thereby regulate the timing of REM sleep episodes is still largely unresolved. Here, we developed a statistical model that specifies the relationship between REM and subsequent NREM sleep to quantify how REM sleep affects the following NREM sleep duration and its electrophysiological features in mice. We show that a lognormal mixture model well describes how the preceding REM sleep duration influences the amount of NREM sleep till the next REM sleep episode. The model supports the existence of two different types of sleep cycles: Short cycles form closely interspaced sequences of REM sleep episodes, whereas during long cycles, REM sleep is first followed by an interval of NREM sleep during which transitions to REM sleep are extremely unlikely. This refractory period is characterized by low power in the theta and sigma range of the electroencephalogram (EEG), low spindle rate and frequent microarousals, and its duration proportionally increases with the preceding REM sleep duration. Using our model, we estimated the propensity for REM sleep at the transition from NREM to REM sleep and found that entering REM sleep with higher propensity resulted in longer REM sleep episodes with reduced EEG power. Compared with the light phase, the buildup of REM sleep propensity was slower during the dark phase. Our data-driven modeling approach uncovered basic principles underlying the timing and duration of REM sleep episodes in mice and provides a flexible framework to describe the ultradian regulation of REM sleep in health and disease.

A salient feature of mammalian sleep is the alternation between rapid eye movement (REM) and non-REM (NREM) sleep. However, how these two sleep stages influence each other and thereby regulate the timing of REM sleep episodes is still largely unresolved. Here, we developed a statistical model that specifies the relationship between REM and subsequent NREM sleep to quantify how REM sleep affects the following NREM sleep duration and its electrophysiological features in mice. We show that a lognormal mixture model well describes how the preceding REM sleep duration influences the amount of NREM sleep till the next REM sleep episode. The model supports the existence of two different types of sleep cycles: Short cycles form closely interspaced sequences of REM sleep episodes, whereas during long cycles, REM sleep is first followed by an interval of NREM sleep during which transitions to REM sleep are extremely unlikely. This refractory period is characterized by low power in the theta and sigma range of the electroencephalogram (EEG), low spindle rate and frequent microarousals, and its duration proportionally increases with the preceding REM sleep duration. Using our model, we estimated the propensity for REM sleep at the transition from NREM to REM sleep and found that entering REM sleep with higher propensity resulted in longer REM sleep episodes with reduced EEG power. Compared with the light phase, the buildup of REM sleep propensity was slower during the dark phase. Our data-driven modeling approach uncovered basic principles underlying the timing and duration of REM sleep episodes in mice and provides a flexible framework to describe the ultradian regulation of REM sleep in health and disease.

Ginseng Extract Regulates the Alterations of Sleep Architecture and EEG Power Spectra in Restraint Stressed Rats

Preoptic/anterior hypothalamic neurons: thermosensitivity in wakefulness and non rapid eye movement sleep

1. In thirty full-term infants in the first week of life, nursed in a constant volume, closed-circuit metabolism chamber in a neutral thermal environment (31.5-33.5 degrees C), measurements were made of oxygen consumption ( V(O2)) during periods of rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep.2. The mean V(O2) during REM sleep was 5.97 ml. kg(-1). min(-1). In NREM sleep the mean V(O2) was 5.72 ml. kg(-1). min(-1). This difference was significant (paired t test P < 0.05).3. When the direction of sleep state change was taken into account the difference in V(O2) between the two states was much less when REM sleep preceded NREM than when the change was in the opposite direction. In nineteen infants in whom the change was from REM to NREM the difference in V(O2) (6.18 and 6.03 ml. kg(-1). min(-1)) was not significant (P > 0.05). The mean difference when the sleep state change was from NREM to REM was significant (P < 0.01), the values being 5.54 and 5.81 ml. kg(-1). min(-1) respectively.4. In the NREM state, a gradual diminution of V(O2) with time was consistently found. This was not the case in REM sleep.5. In twelve infants studied in a cool environment (29 +/- 0.5 degrees C) V(O2) during REM sleep was 7.77, and during NREM sleep it was 6.58 ml. kg(-1). min(-1), (P < 0.001). Thus even the maximum difference found in a neutral thermal environment of 6.6% was significantly increased to 14.9% (P < 0.01) with mild thermal stress.6. No consistent changes in V(O2) with time were found in either REM or NREM sleep in twelve infants studied in a cool environment, in contrast to the findings in thermal neutrality

Introduction Although obstructive sleep apnea (OSA) is associated with abnormal glycometabolism, current evidence on the association between hyperglycemia and abnormal sleep structure is still limited among patients with OSA. We sought to evaluate whether hyperglycemia was associated with abnormal sleep architecture. Methods A total of 452 patients with OSA, who were free of previously diagnosed diabetes mellitus, were consecutively recruited. All participants underwent overnight polysomnography and 75-g oral glucose tolerance test. Patients were divided into normal glucose tolerance (NGT) and hyperglycemia (i.e. prediabetes and type 2 diabetes) according to the ADA criteria. The association between hyperglycemia and sleep architecture was examined using logistic regression model. Results Of 452 patients, 283 (63%) had hyperglycemia (age 43.9 ± 11.1) and 169 (37%) had NGT (age 40.1 ± 11.1). Compared to the NGT group, the hyperglycemia group had older age (P &amp;lt; 0.05), higher body mass index (27.5 ± 4.1 vs. 26.33 ± 4.4; P &amp;lt; 0.05) and higher AHI (apnea-hypopnea index) (57.41 ± 28.6 vs. 48.3 ± 28.2; P &amp;lt; 0.05). There were no differences in total sleep time, the percentage of time spent in rapid eye movement (REM) or non-rapid eye movement (NREM) sleep between groups. However, patients with hyperglycemia had more microarousal events, especially during the NREM sleep (214 (range 19-662) events/h vs. 148 (range 37-600) events/h; P &amp;lt; 0.05). In addition, sleep variables related to oxygen saturation measures, such as the percentage of time spent with oxygen saturation ≤80%, were significantly greater during the REM sleep in patients with hyperglycemia (1.4 (total range 0-91.1) % vs. 1.1(0-78.6) %; P &amp;lt; 0.05). After adjusting potential confounders, logistic regression analyses showed that the presence of hyperglycemia was independently associated with the number of microarousals in NREM sleep (OR = 1.01, 95% CI = 1.00-1.02, P = 0.02). Conclusion Hyperglycemia is independently associated with abnormal sleep architecture among patients with OSA. Patients with hyperglycemia have significantly increased sleep fragmentation in NREM sleep and significantly increased hypoxia in REM sleep. Support This work was supported by the National Natural Science Foundation of China (81700087).