Chin Electromyogram Research Articles

Contribution of basal ganglia activity to REM sleep disorder in Parkinson’s disease

BackgroundRapid eye movement (REM) sleep behaviour disorder (RBD) is one of the most common sleep problems and represents a key prodromal marker in Parkinson’s disease (PD). It remains unclear whether...

Automated sleep staging on reduced channels in children with epilepsy.

This study aimed to validate a sleep staging algorithm using in-hospital video-electroencephalogram (EEG) in children without epilepsy, with well-controlled epilepsy (WCE), and with drug-resistant epilepsy (DRE). Overnight video-EEG, along with electrooculogram (EOG) and chin electromyogram (EMG), was recorded in children between 4 and 18 years of age. Classical sleep staging was performed manually as a ground truth. An end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging (SeqSleepNet) was used to perform automated sleep staging using three channels: C4-A1, EOG, and chin EMG. In 176 children sleep stages were manually scored: 47 children without epilepsy, 74 with WCE, and 55 with DRE. The 5-class sleep staging accuracy of the automatic sleep staging algorithm was 84.7% for the children without epilepsy, 83.5% for those with WCE, and 80.8% for those with DRE (Kappa of 0.79, 0.77, and 0.73 respectively). Performance per sleep stage was assessed with an F1 score of 0.91 for wake, 0.50 for N1, 0.83 for N2, 0.84 for N3, and 0.86 for rapid eye movement (REM) sleep. We concluded that the tested algorithm has a high accuracy in children without epilepsy and with WCE. Performance in children with DRE was acceptable, but significantly lower, which could be explained by a tendency of more time spent in N1, and by abundant interictal epileptiform discharges and intellectual disability leading to less recognizable sleep stages. REM sleep time, however, significantly affected in children with DRE, can be detected reliably by the algorithm.Clinical trial registration: ClinicalTrials.gov, identifier NCT04584385.

Ikelos-RWA. Validation of an Automatic Tool to Quantify REM Sleep Without Atonia.

Study Objectives. To present and evaluate an automatic scoring algorithm for quantification of REM-sleep without atonia (RWA) in patients with REM-sleep behaviour disorder (RBD) based on a generally accepted, well-validated visual scoring method, ("Montreal" phasic and tonic) and a recently developed, concise scoring method (Ikelos-RWA). Methods. Video-polysomnographies of 20 RBD patients (68.2 ± 7.2 years) and 20 control patients with periodic limb movement disorder (65.9 ± 6.7 years) were retrospectively analysed. RWA was estimated from chin electromyogram during REM-sleep. Visual and automated RWA scorings were correlated, and agreement (a) and Cohen's Kappa (k) calculated for 1735 minutes of REM-sleep of the RBD patients. Discrimination performance was evaluated with receiver operating characteristic (ROC) analysis. The algorithm was then applied on the polysomnographies of a cohort of 232 RBD patients (total analysed REM-sleep: 17,219 minutes) and evaluated, while correlating the different output parameters. Results. Visual and computer-derived RWA scorings correlated significantly (tonic Montreal: rTM = 0.77; phasic Montreal: rPM = 0.78; Ikelos-RWA: rI = 0.97; all p < 0.001) and showed good to excellent Kappa coefficients (kTM = 0.71; kPM = 0.79; kI = 0.77). The ROC analysis showed high sensitivities (95%-100%) and specificities (84%-95%) at the optimal operation points, with area under the curve (AUC) of 0.98, indicating high discriminating capacity. The automatic RWA scorings of 232 patients correlated significantly (rTM{I} = 0.95; rPM{I} = 0.91, p < 0.0001). Conclusions. The presented algorithm is an easy-to-use and valid tool for automatic RWA scoring in patients with RBD and may prove effective for general use being publicly available.

Experimental study of the negative effects of raised bedroom temperature and reduced ventilation on the sleep quality of elderly subjects.

This study investigated the effects of air temperature and ventilation on the sleep quality of elderly subjects and elucidated the mechanisms involved. Sixteen subjects aged over 65 years old were exposed to four conditions in a 2 × 2 design: air temperatures of 27°C and 30°C (with a ceiling fan in operation at 30°C) and two ventilation conditions (with and without mechanical ventilation) in experimental bedrooms. Their electroencephalogram, electrooculogram, chin electromyogram, electrocardiogram, respiration, oxygen saturation, and wrist skin temperature were measured continuously during sleep. Saliva samples were collected, and blood pressure was measured both before and after sleep. The results showed that at the temperature of 30°C, the total sleep time, sleep efficiency, and duration of REM sleep of the elderly decreased by 26.3min, 5.5%, and 5.3min, respectively, and time awake increased by 27.0min, in comparison with 27°C, indicating that the sleep quality of the elderly is very vulnerable to heat exposure. Even a small heat load led to an overactive sympathetic nervous system and increased wrist skin temperature, which reduced sleep quality. Improving the ventilation increased the duration of deep sleep and REM sleep by 10.3min and 3.7min, respectively. Higher pollutant concentrations affected the respiration and autonomous nervous systems to reduce sleep quality. The benefits of improved thermal environment and ventilation on sleep quality were found to be additive. Good ventilation and the avoidance of raised temperatures in the bedroom are thus both important for the sleep quality of the elderly.

Spectrogram classification of patient chin electromyography based on deep learning: A novel method for accurate diagnosis obstructive sleep apnea

Obstructive sleep apnea (OSA) is the most common sleep-related breathing disorder and the cause is an increased upper airway resistance during sleep, leading to partial or complete interruption of airflow. It is important to find and treat OSA because it is caused starting of many dangerous illnesses. For diagnosing OSA, polysomnography (PSG) is the gold standard but it is complex, costly, and time-consuming Hence, a simple and automated recognition system can be very useful. Numerous studies have been conducted on sleep-disordered breathing (SDB) based on a variety of signals and algorithms in recent years. Researchers have always attempted to intelligently diagnose OSA with fewer and more accessible signals using faster and simpler algorithms due to the complexity of biological signals and the difficulty of visual recognition and interpretation. In this paper, a novel and effective technic is proposed by using artificial intelligence. This study introduced and examined for the first time the idea of applying chin electromyogram signal directly and independently to diagnose OSA. Two-dimensional spectrograms generated from the chin electromyogram of 100 patients were fed as input to three pre-trained deep learning models and their performances were compared with a multilayer perceptron network. Results showed that our proposed newfound approach outperforms the current states and it proved chin electromyography spectral analysis and classification of produced 2D spectrograms with the deep learning model provides an effective, rapid, and accurate diagnosis and prediction tool in this field with an accuracy of more than 99%.

Trazodone affects periodic leg movements and chin muscle tone during sleep less than selective serotonin reuptake inhibitor antidepressants in children.

To test the hypothesis that children taking trazodone have less leg movements during sleep (LMS) and higher rapid eye movement (REM) sleep atonia than children taking selective serotonin reuptake inhibitors (SSRIs) but more than normal controls. Fifteen children (9 girls and 6 boys, mean age 11.7 years, standard deviation [SD] 3.42) taking trazodone (median dosage 50 mg/d, range 25-200 mg) for insomnia and 19 children (11 girls and 8 boys, mean age 13.7 years, SD 3.07) taking SSRIs for depression, anxiety, or both were consecutively recruited, as well as an age- and sex-matched group of 25 control children (17 girls and 8 boys, mean age 13.7 years, SD 3.11). LMS were scored and a series of parameters was calculated, along with the analysis of their time structure. The Atonia Index was then computed for each non-REM sleep stage and for REM sleep. Children taking trazodone exhibited slightly higher leg movement indices than controls but lower than those found in children taking SSRIs and their time structure was different. Chin electromyogram atonia in all sleep stages was not significantly altered in children taking trazodone but was decreased in children taking SSRIs, especially during non-REM sleep. In children, SSRIs but not trazodone are associated with a significantly increased number of LMS, including periodic LMS, and increased chin tone in all sleep stages. The assessment of periodic limb movement disorder and REM sleep without atonia might not be accurate when children are taking SSRIs because of their significant impact. DelRosso LM, Mogavero MP, Bruni O, Schenck CH, Fickenscher A, Ferri R. Trazodone affects periodic leg movements and chin muscle tone during sleep less than selective serotonin reuptake inhibitor antidepressants in children. J Clin Sleep Med. 2022;18(12):2829-2836.

An Automated Wavelet-Based Sleep Scoring Model Using EEG, EMG, and EOG Signals with More Than 8000 Subjects.

Human life necessitates high-quality sleep. However, humans suffer from a lower quality of life because of sleep disorders. The identification of sleep stages is necessary to predict the quality of sleep. Manual sleep-stage scoring is frequently conducted through sleep experts’ visually evaluations of a patient’s neurophysiological data, gathered in sleep laboratories. Manually scoring sleep is a tough, time-intensive, tiresome, and highly subjective activity. Hence, the need of creating automatic sleep-stage classification has risen due to the limitations imposed by manual sleep-stage scoring methods. In this study, a novel machine learning model is developed using dual-channel unipolar electroencephalogram (EEG), chin electromyogram (EMG), and dual-channel electrooculgram (EOG) signals. Using an optimum orthogonal filter bank, sub-bands are obtained by decomposing 30 s epochs of signals. Tsallis entropies are then calculated from the coefficients of these sub-bands. Then, these features are fed an ensemble bagged tree (EBT) classifier for automated sleep classification. We developed our automated sleep classification model using the Sleep Heart Health Study (SHHS) database, which contains two parts, SHHS-1 and SHHS-2, containing more than 8455 subjects with more than 75,000 h of recordings. The proposed model separated three classes if sleep: rapid eye movement (REM), non-REM, and wake, with a classification accuracy of 90.70% and 91.80% using the SHHS-1 and SHHS-2 datasets, respectively. For the five-class problem, the model produces a classification accuracy of 84.3% and 86.3%, corresponding to the SHHS-1 and SHHS-2 databases, respectively, to classify wake, N1, N2, N3, and REM sleep stages. The model acquired Cohen’s kappa () coefficients as 0.838 with SHHS-1 and 0.86 with SHHS-2 for the three-class classification problem. Similarly, the model achieved Cohen’s of 0.7746 for SHHS-1 and 0.8007 for SHHS-2 in five-class classification tasks. The model proposed in this study has achieved better performance than the best existing methods. Moreover, the model that has been proposed has been developed to classify sleep stages for both good sleepers as well as patients suffering from sleep disorders. Thus, the proposed wavelet Tsallis entropy-based model is robust and accurate and may help clinicians to comprehend and interpret sleep stages efficiently.

A surface electrode adjacent to vagal nerve stimulator lead can aid in characterizing vagal nerve stimulator-mediated pediatric sleep-disordered breathing: a case series of 7 patients.

The vagal nerve stimulator (VNS) is a nonpharmacological treatment for refractory epilepsy. A side effect of the VNS is sleep-disordered breathing. The purpose of this study was to demonstrate how a surface electrode placed over the VNS lead can help distinguish whether sleep-disordered breathing is due to VNS discharge. Seven pediatric patients (aged 7.7 ± 2.2 years) with a VNS underwent a polysomnogram with an additional surface electrode on the left anterolateral neck to detect VNS discharge. The VNS-associated apnea-hypopnea index was calculated by determining the number of hypopneas and apneas occurring during VNS discharge. We evaluated the veracity of the VNS electrode by comparing signal duration and total number to those expected by programmed settings. We compared these findings to chin electromyogram signal change. Three patients had an obstructive pattern with VNS discharge, and 3 had an increase in respiratory rate without gas exchange abnormalities, including 1 with both patterns; 1 patient experienced no respiratory abnormalities. The mean obstructive apnea-hypopnea index was 8.2 ± 8.3 events/h. The mean VNS-associated apnea-hypopnea index was 4.8 ± 6.2 events/h and accounted for 46.9 ± 30.2% of the total obstructive apnea-hypopnea index. The additional electrode captured a statistically high percentage of expected discharges (94.7 ± 6.5%) compared to chin electromyogram (36.1 ± 35.8%; P < .05). We demonstrated that a surface electrode on the VNS lead can temporally coregister VNS discharges and enabled us to attribute sleep-disordered breathing to VNS stimulation in 4 patients. We propose that this sensor be standard procedure in patients with VNS undergoing polysomnogram. Chan JHM, DelRosso LM, Ruth C, Wrede JE. A surface electrode adjacent to vagal nerve stimulator lead can aid in characterizing vagal nerve stimulator-mediated pediatric sleep-disordered breathing: a case series of 7 patients. J Clin Sleep Med. 2022;18(8):1973-1981.

Chin Electromyogram, an Effectual and Useful Biosignal for the Diagnosis of Obstructive Sleep Apnea

Background and Objective: Obstructive sleep apnea (OSA) is among the critical sleep disorders, and researchers have been investigating its novel diagnostic methods. Polysomnography signals' complexity, difficult visual interpretation, and the need for an efficient algorithm based on simpler signals have made the study of sleep apnea a compelling issue. In this study, the accuracy of chin electromyogram in the diagnosis of OSA was evaluated.&#x0D; Materials and Methods: The amplitude variation and power spectral density (PSD) of chin electromyograms of 100 patients during apnea and before-after apnea occurrences (non-apnea) periods were compared after complete processing of the raw signal. Two-dimensional (2D) spectrograms related to the specified periods were extracted and fed into the residual neural network (ResNet). The network performance was reported by model evaluation parameters.&#x0D; Results: The results showed that OSA event influences the patient's chin muscle and increases the amplitude variances and power spectrum of the chin electromyogram. The ResNet-50 deep model classified the dataset of this sleep disorder with about 97% accuracy, which was higher than previous studies in this field.&#x0D; Conclusion: Chin electromyogram can be introduced as a practical and useful biosignal for accurate OSA diagnosis with a deep classifier without the need for current specialized equipment and multiple vital signals.&#x0D;

Increased chin muscle tone during all sleep stages in children taking selective serotonin reuptake inhibitor antidepressants and in children with narcolepsy type 1.

To assess if selective serotonin reuptake inhibitor (SSRI) antidepressants are able to modify the chin electromyogram (EMG) tone during sleep also in children. Twenty-three children and adolescents (12 girls, mean age 14.1 years, SD 2.94) under therapy with antidepressant for their mood disorder were consecutively recruited and had a PSG recording. Twenty-one were taking were taking SSRI and treatment duration was 2-12 months. An age- and sex matched group of 33 control children (17 girls, mean age 14.2 years, SD 2.83) and 24 children with narcolepsy type 1 (12 girls, mean age 13.7 years, SD 2.80) were also included. The Atonia Index was then computed for each NREM sleep stage and for REM sleep, also all EMG activations were counted. Atonia Index in all sleep stages was found to be significantly reduced in children with narcolepsy followed by the group taking SSRI antidepressants and the number of EMG activations was also increased in both groups. Fluoxetine, in particular, was found to be significantly associated with reduced Atonia index during NREM sleep stages N1, N2, and N3, and with an increased number of EMG activations/hour during sleep stage N3. Similarly to adults, SSRI antidepressants are able to modify the chin EMG tone in children during REM sleep, as well as during NREM sleep stages. Different pharmacological properties of the different SSRI might explain the differential effect on chin tone during sleep found in this study.

Pilot study of the effects of ventilation and ventilation noise on sleep quality in the young and elderly.

Three conditions were established to investigate the effects of ventilation and related ventilation noise on sleep quality: No mechanical ventilation/low noise (A); Mechanical ventilation /low noise (B); Mechanical ventilation/high noise (C). The interventions were achieved by idling a mechanical ventilation system or operating it in two different modes. Nine young people and nine older people were all exposed to each of the three conditions for a whole night's sleep, but data from only 15 subjects were analyzed as three young subjects apparently slept with open windows in condition A. Sleep quality was measured objectively with polysomnography (PSG), which monitored signals of electroencephalogram (EEG), bilateral electrooculogram (EOG), and chin electromyogram (EMG) continuously during the sleeping period. Saliva samples were collected before sleep at night and after waking in the morning, and the concentrations of cortisol and lysozyme in them were determined. Without mechanical ventilation, the indoor CO2 level averaged about 1400ppm during the night. Operating the mechanical ventilation decreased the indoor CO2 to below 1000ppm, which improved objectively measured sleep quality: wake time after sleep onset (WASO) decreased on average by 15min (p<0.05) and sleep efficiency (SE) increased on average by about 4% (p<0.05). Increased ventilation noise level (50.8dB(A) vs. 34.7dB(A); 54.9dB(C) vs. 48dB(C)) did not significantly change SE or WASO but did change the duration of sleep stages: It decreased the duration of deep sleep by 11min (p<0.05) and REM sleep by 17min (p<0.01) and increased the duration of light sleep by 17min (p<0.05). The ventilation noise significantly increased the concentration of lysozyme in the elderly (p<0.05) although no significant effects on cortisol could be shown. These results confirm that a low ventilation rate has negative effects on sleep quality and that ventilation noise at or above 50dB(A) may disrupt sleep.

Sleep problems among children with Fetal Alcohol Spectrum Disorders (FASD)- an explorative study

BackgroundFetal alcohol spectrum disorders (FASD) is a group of conditions resulting from prenatal alcohol exposure (PAE). Patients with FASD experience a variety of neuropsychological symptoms resulting from central nervous system impairment. Little is known about sleep disorders associated with PAE. The objective of this study was to investigate sleep problems related to FASD.MethodsForty patients (median age 8 years (6; 11)) diagnosed with FASD and forty typically developing children (median age 10 years (8; 13)) were recruited for the 1st phase of the study. In the 1st phase, the screening of sleep problems was performed with Child Sleep Habit Questionnaire (CSHQ) filled in by a caregiver. Those of the FASD group who scored above 41 points were qualified to the 2nd phase of the study and had an in-lab attended polysomnography (PSG) performed. The measurements consisted of electroencephalogram, electrooculograms, chin and tibial electromyogram, electrocardiogram, ventilatory monitoring, breathing effort, pulse oximetry, snoring and body position. Their results were compared to PSG laboratory reference data.ResultsThe number of participants with sleep disturbances was markedly higher in the FASD group as compared to typically developing children (55% vs. 20%). The age-adjusted odds ratio for a positive result in CSHQ was 4.31 (95% CI: 1.54–12.11; p = 0.005) for FASD patients as compared to the control group. Significant differences between the FASD as compared to the typically developing children were observed in the following subscales: sleep onset delay, night wakings, parasomnias, sleep disordered breathing, and daytime sleepiness. Children from the FASD group who underwent PSG experienced more arousals during the sleep as compared with the PSG laboratory reference data. The respiratory indices in FASD group appear higher than previously published data from typically developing children.ConclusionThe results support the clinical observation that sleep disorders appear to be an important health problem in individuals with FASD. In particular distorted sleep architecture and apneic/hypopneic events need further attention.

Automatic scoring of non-apnoea arousals using hand-crafted features from the polysomnogram

Objective: We present a system for automated annotation of non-apnoea arousals using twelve signals from the polysomnogram (PSG) including airflow, six signals of electroencephalogram, the electrooculogram, chin electromyogram, oximetry signal, and chest and abdominal respiratory effort signals. Approach: Fifty-nine time- and frequency-domain features were extracted from the twelve signals using 15 s epochs. Features from an epoch were combined with features from adjacent epochs and then processed with a bank of feed-forward networks that provided a probability estimate of the occurrence of a non-apnoea arousal event in every epoch. Data from the 2018 PhysioNet/Computing in Cardiology Challenge was used to develop and test the system. Ten-fold cross validation on the 994 PSGs of training data was used to compare the performance of different network configurations. Main results: Our highest performing configuration utilised a bank of 30 feed-forward neural networks. Each network processed ±4 epochs of features and each used a single hidden layer of 20 units. The performance of this configuration was evaluated on the independent test set of 989 PSGs and achieved an area under the receiver operator curve of 0.848 and an area under the precision-recall curve of 0.325 for correctly discriminating non-apnoea arousals from non-arousals samples. Significance: The classification performance results of our system demonstrate that automated annotation of non-apnoea arousals can be achieved with a high degree of reliability.

The estimation of excessive daytime sleepiness in post-stroke patients - a polysomnographic study

Abstract Excessive daytime sleepiness (EDS) has been reported in stroke patients. EDS in acute stroke was studied repeatedly, but there is a modest amount of data in post-stroke patients. The aim of this study was to assess the frequency of EDS and characterize sleep architecture in patients >3 months after stroke and identify factors which may affect EDS. 66 patients were enrolled, of which 33 had experienced stroke. All underwent a standardized overnight, diagnostic single night polysomnography, including electrocephalogram (EEG) leads, electrooculograms (EOG), chin electromyogram (EMG), and electrocardiogram (ECG). Epworth Sleepiness Scale (ESS) was used to measure subjects’ level of daytime sleepiness. We observed similar total ESS score, total sleep time (TST), sleep efficiency, as well as respiratory disturbance index /apnea-hypopnea index (RDI/AHI), oxygen desaturation index (ODI) and mean heart rate in both groups. We observed positive linear correlation between EDS and mean heart rate in the stroke group (r = 0.46, p In summary, in stroke patients subjective daytime sleepiness is associated with heart rate, but not with the severity of OSA. Thus ESS (Epworth Scale Score) may be not as useful as a marker of obstructive sleep apnea (OSA) presence or severity in post stroke patient as in the general population.

The estimation of excessive daytime sleepiness in post-stroke patients - a polysomnographic study

Excessive daytime sleepiness (EDS) has been reported in stroke patients. EDS in acute stroke was studied repeatedly, but there is a modest amount of data in post-stroke patients. The aim of this study was to assess the frequency of EDS and characterize sleep architecture in patients >3 months after stroke and identify factors which may affect EDS.66 patients were enrolled, of which 33 had experienced stroke. All underwent a standardized overnight, diagnostic single night polysomnography, including electrocephalogram (EEG) leads, electrooculograms (EOG), chin electromyogram (EMG), and electrocardiogram (ECG). Epworth Sleepiness Scale (ESS) was used to measure subjects’ level of daytime sleepiness.We observed similar total ESS score, total sleep time (TST), sleep efficiency, as well as respiratory disturbance index /apnea-hypopnea index (RDI/AHI), oxygen desaturation index (ODI) and mean heart rate in both groups. We observed positive linear correlation between EDS and mean heart rate in the stroke group (r = 0.46, p < 0.05) as well as between EDS and REM duration (r = 0.23, p < 0.05). In the non-stroke group EDS didn’t correlate with the heart rate or with the REM duration. In the non-stroke group EDS correlated positively with RDI/AHI and ODI index (r = 0.46; p = <0.05 r = 0.41, p < 0.05 and maximal desaturation (r = 0.55, p < 0.05), this correlation was not observed in post-stroke group. In the both groups we observed negative linear correlation between BMI and saturation (stroke group - mean as well as minimal saturation (r = −0.458, p < 0.05; r = −0.578, p < 0.05), non stroke group - minimal but not mean saturation rate (r = −0.544, p < 0.05). We also noticed in both groups positive correlation between BMI and both AHI and ODI index (stroke group respectively: r = 0.430, p < 0.05; r = 0.451, p < 0.05; non-stroke group - BMI and ODI: r = 0.405, p < 0.05). The positive BMI correlation with RDI/AHI was not significant in the non-stroke group. We noticed that BMI correlates with non-REM sleep N1 (r = 0.760, p < 0.05) in post-stroke group, while it correlates negatively with REM sleep (r = -0.709, p < 0.05). There was no such correlation in the non-stroke group.In summary, in stroke patients subjective daytime sleepiness is associated with heart rate, but not with the severity of OSA. Thus ESS (Epworth Scale Score) may be not as useful as a marker of obstructive sleep apnea (OSA) presence or severity in post stroke patient as in the general population.

Obstructive Sleep Apnea and Sleep Architecture in Adolescents With Severe Obesity: Effects of a 9-Month Lifestyle Modification Program Based on Regular Exercise and a Balanced Diet.

Physical exercise and lifestyle modification are recognized as adjunct therapy for obstructive sleep apnea (OSA) in overweight adults. The objectives of this study were to investigate the effects of long-term physical exercise combined with a balanced diet on sleep architecture, sleep duration, and OSA in adolescents with severe obesity. This interventional study was conducted in a nursing institution. Participants were aged 14.6 ± 1.2 years with obesity (body mass index (BMI) = 40.2 ± 6.5 kg/m2). At admission and at 9 months, participants underwent ambulatory polysomnography and incremental maximal exercise testing to determine cardiorespiratory fitness. Twenty-four subjects completed the study. Analyses were performed on the whole population and on a subgroup of subjects with OSA (OSA-subgroup). OSA, defined as obstructive apnea-hypopnea index (OAHI) ≥ 2 events/h, was diagnosed in 58.3% of the population. OAHI was only associated with fat mass in males (r = .75, P < .05). At 9 months postintervention, weight loss (-11.1 kg, P < .0001) and improved cardiorespiratory fitness (VO2peak: +4.9 mL/min/kg, P < .001) were found in the whole population. Sleep duration was increased (+34 minutes, P < .05) and sleep architecture was changed with an increase of rapid eye movement sleep (+2.5%, P < .05) and a decrease of stage N3 sleep (-3.1%, P < .001). Similar results were found in the OSA subgroup. However, OAHI remained unchanged (P = .18). A combination of supervised aerobic exercise and a balanced diet led to weight loss, improved aerobic capacity, and modified sleep architecture without changes in OSA. A commentary on this article appears in this issue on page 907. Registry: ClinicalTrials.gov, Title: Exercise and Venous Compression on Upper Airway Resistance in Obese Teenagers With OSA (OBESOMAC), URL: https://clinicaltrials.gov/ct2/show/NCT02588469, Identifier: NCT02588469.

CON: Persistent Central Sleep Apnea/Hunter-Cheyne-Stokes Breathing, Despite Best Guideline-Based Therapy of Heart Failure With Reduced Ejection Fraction, Is Not a Compensatory Mechanism and Should Be Suppressed.

CON: Persistent Central Sleep Apnea/Hunter-Cheyne-Stokes Breathing, Despite Best Guideline-Based Therapy of Heart Failure With Reduced Ejection Fraction, Is Not a Compensatory Mechanism and Should Be Suppressed.

Tonic Electromyogram Density in Multiple System Atrophy with Predominant Parkinsonism and Parkinson's Disease.

Background:Both Parkinson's disease (PD) and multiple system atrophy (MSA) have associated sleep disorders related to the underlying neurodegenerative pathology. Clinically, MSA with predominant parkinsonism (MSA-P) resembles PD in the manifestation of prominent parkinsonism. Whether the amount of rapid eye movement (REM) sleep without atonia could be a potential marker for differentiating MSA-P from PD has not been thoroughly investigated. This study aimed to examine whether sleep parameters could provide a method for differentiating MSA-P from PD.Methods:This study comprised 24 MSA-P patients and 30 PD patients, and they were of similar age, gender, and REM sleep behavior disorder (RBD) prevalence. All patients underwent clinical evaluation and one night of video-polysomnography recording. The tonic and phasic chin electromyogram (EMG) activity was manually quantified during REM sleep of each patient. We divided both groups in terms of whether they had RBD to make subgroup analysis.Results:No significant difference between MSA-P group and PD group had been found in clinical characteristics and sleep architecture. However, MSA-P patients had higher apnea-hypopnea index (AHI; 1.15 [0.00, 8.73]/h vs. 0.00 [0.00, 0.55]/h, P = 0.024) and higher tonic chin EMG density (34.02 [18.48, 57.18]% vs. 8.40 [3.11, 13.06]%, P < 0.001) as compared to PD patients. Subgroup analysis found that tonic EMG density in MSA + RBD subgroup was higher than that in PD + RBD subgroup (55.04 [26.81, 69.62]% vs. 11.40 [8.51, 20.41]%, P < 0.001). Furthermore, no evidence of any difference in tonic EMG density emerged between PD + RBD and MSA - RBD subgroups (P > 0.05). Both disease duration (P = 0.056) and AHI (P = 0.051) showed no significant differences during subgroup analysis although there was a trend toward longer disease duration in PD + RBD subgroup and higher AHI in MSA - RBD subgroup. Stepwise multiple linear regression analysis identified the presence of MSA-P (β = 0.552, P < 0.001) and RBD (β = 0.433, P < 0.001) as predictors of higher tonic EMG density.Conclusion:Tonic chin EMG density could be a potential marker for differentiating MSA-P from PD.

Muscle Activity During Sleep in Human Subjects, Rats, and Mice: Towards Translational Models of REM Sleep Without Atonia.

Rapid-eye-movement (REM) sleep without atonia (RSWA) is a marker of REM sleep behavior disorder (RBD) and is common in narcolepsy. Available techniques for electromyogram (EMG) analysis are species-specific, limiting translational research on RSWA. We developed an automated technique based on distributions of normalized EMG values (DNE) to overcome this limitation. With DNE, we tested whether the control of neck and tibialis anterior (TA) muscles during sleep in wild-type rats and mice validly models the control of submentalis (chin) and TA muscles in healthy humans. We then applied DNE to REM sleep recordings of patients with idiopathic RBD and of mouse models of narcolepsy, testing for a common DNE signature of RSWA. Retrospective analysis of sleep recordings from 20 idiopathic RBD patients, 34 control subjects, 8 wild-type rats, 21 orexin-neuron deficient mice, 8 orexin knock-out mice, and 22 wild-type mice. Neck EMG of rats and mice and human chin EMG progressively decreased from wakefulness to non-REM sleep and REM sleep, whereas the effects of sleep on TA EMG differed between rats, mice, and humans. DNE discriminated idiopathic RBD patients from controls based on higher median values of normalized chin EMG during REM sleep. The same parameter, computed on neck muscle EMG, discriminated narcoleptic mice from wild-type mice. These results support the application of DNE in translational research on RSWA. Increased median values of normalized EMG of chin (humans) and neck (rats and mice) muscles may be a signature of RSWA in different species and pathological conditions.

Isolated rapid eye movement sleep without atonia in amyotrophic lateral sclerosis

ObjectiveThe aim of this study was to quantitatively analyze, with the most recent and advanced tools, the presence of periodic leg movements during sleep (PLMS) and/or rapid eye movement (REM) sleep without atonia (RSWA), in a group of patients with amyotrophic lateral sclerosis (ALS), and to assess their eventual correlation with the clinical severity of the disease. MethodsTwenty-nine ALS patients were enrolled (mean age 63.6 years) along with 28 age-matched “normal” controls (mean age 63.8 years). Functional impairment due to ALS was evaluated using the ALS-Functional Rating Scale-Revised (ALS-FRS) and the ALS severity scale (ALSSS). Full video polysomnographic night recordings were obtained, and PLMS were analyzed by considering their number/hour of sleep and periodicity index, the distribution of intermovement intervals, and the distribution during the night. The characteristics of the chin electromyogram (EMG) amplitude during REM sleep were analyzed by means of the automatic atonia index and the number of chin EMG activations (movements). ResultsThe ALS patients showed longer sleep latency than the controls, together with an increase in number of stage shifts, increased sleep stage 1, and decreased sleep stage 2. None of the leg PLMS parameters were different between the ALS patients and controls. The REM atonia index was significantly decreased in the ALS patients, and the number of chin movements/hour tended to increase. Both REM atonia index and number of chin movements/hour correlated significantly with the ALS-FRS; REM atonia was higher and chin movements were less in ALS patients with more preserved function (higher scores on the ALS-FRS). ConclusionAbnormal REM sleep atonia seemed to be a genuine effect of ALS pathology per se and correlated with the clinical severity of the disease. It is unclear if this might constitute the basis of a possible risk for the development of REM sleep behavior disorder or represent a form of isolated RSWA in ALS.