American Academy Of Sleep Medicine Research Articles

Higher education enrollment as a risk factor for somnolence and hypersomnolence

This study aimed to compare the prevalence of somnolence and hypersomnolence between a higher education student and non-student sample. Hershner and Chervin [Hershner in Nat sci sleep 10.2147/NSS.S62907, 2014] defined somnolence as lapses into drowsiness, consequently leading to the inability to maintain alertness. This definition aligns with the American Academy of Sleep Medicine's (AASM) definition [Berry in Am Acad Sleep Med 176:2012, 2012]. Hypersomnolence differs from this, as suggested by Lammers et al. [Lammers in Sleep Med Rev 52, 101306, 2020], and refers to the experience of excessive daytime sleepiness. It is commonly observed that individuals enrolled in higher education courses often experience somnolence and hypersomnolence; however, it is currently unclear whether this is more prevalent in students compared to the general population. An online survey was administered to 202 participants measuring somnolence, hypersomnolence, sleep quality, sleep efficiency, sleep disturbance, sleep duration, circadian preference, and daytime dysfunction. 94 participants were non-students, and 108 were enrolled in higher education. Significant differences were found between student and non-student samples for somnolence but not for hypersomnolence. Furthermore, within the non-student sample a multiple linear regression demonstrated that hypersomnolence was predicted by daytime dysfunction. The results suggest that there are differences in the predictors of somnolence and hypersomnolence between a student and non-student sample. Consequently, this study highlights that students experience hypersomnolence and somnolence differently to their non-student counterparts. Thus, warranting the need for further investigating within this unique population.

Who knows how much kids are sleeping? And why it matters

According to the American Academy of Sleep Medicine, children from the ages of 6 to 12 should be getting 9–12 hours of sleep every night (Paruthi et al., 2016). However, many children are not getting enough sleep. The National Survey of Children's Health found that 38.4% of 6–12‐year‐olds in the United States had short sleep duration according to parental report (Wheaton & Claussen, 2021). This sleep deficit may increase with age, as the CDC found that around six in 10 middle schoolers and seven in 10 high schoolers self‐report not getting the recommended amount of sleep on school nights (Wheaton et al., 2018). In addition to age, this difference in the percentage of kids with insufficient sleep may also be due to the differences in informant between those two surveys. One study found that while both children and parents overestimated total sleep time compared with the polysomnography, the child report was slightly more accurate (Combs et al., 2019).

Strengths, weaknesses, opportunities and threats of using AI-enabled technology in sleep medicine: a commentary.

Over the past few years, artificial intelligence (AI) has emerged as a powerful tool used to efficiently automate several tasks across multiple domains. Sleep medicine is perfectly positioned to leverage this tool due to the wealth of physiological signals obtained through sleep studies or sleep tracking devices and abundance of accessible clinical data through electronic medical records. However, caution must be applied when utilizing AI, due to intrinsic challenges associated with novel technology. The Artificial Intelligence in Sleep Medicine committee of the American Academy of Sleep Medicine (AASM) reviews advancements in AI within the sleep medicine field. In this article, the Artificial Intelligence in Sleep Medicine committee members provide a commentary on the scope of AI technology in sleep medicine. The commentary identifies three pivotal areas in sleep medicine which can benefit from AI technologies: clinical care, lifestyle management and population health management. This article provides a detailed analysis of the strengths, weaknesses, opportunities, and threats associated with using AI enabled technologies in each pivotal area. Finally, the article broadly reviews barriers and challenges associated with using AI enabled technologies and offers possible solutions.

Automatic Wake and Deep-Sleep Stage Classification Based on Wigner-Ville Distribution Using a Single Electroencephalogram Signal.

This research paper outlines a method for automatically classifying wakefulness and deep sleep stage (N3) based on the American Academy of Sleep Medicine (AASM) standards. The study employed a single-channel EEG signal, leveraging the Wigner-Ville Distribution (WVD) for time-frequency analysis to determine EEG energy per second in specific frequency bands (δ, θ, α, and entire band). Particle Swarm Optimization (PSO) was used to optimize thresholds for distinguishing between wakefulness and stage N3. This process aims to mimic a sleep technician's visual scoring but in an automated fashion, with features and thresholds extracted to classify epochs into correct sleep stages. The study's methodology was validated using overnight PSG recordings from 20 subjects, which were evaluated by a technician. The PSG setup followed the 10-20 standard system with varying sampling rates from different hospitals. Two baselines, T1 for the wake stage and T2 for the N3 stage, were calculated using PSO to ascertain the best thresholds, which were then used to classify EEG epochs. The results showed high sensitivity, accuracy, and kappa coefficient, indicating the effectiveness of the classification algorithm. They suggest that the proposed method can reliably determine sleep stages, being aligned closely with the AASM standards and offering an intuitive approach. The paper highlights the strengths of the proposed method over traditional classifiers and expresses the intentions to extend the algorithm to classify all sleep stages in the future.

Perspective: Women's leadership and salary inequality in sleep medicine.

Shawa J, Ehsan Z. Perspective: women's leadership and salary inequality in sleep medicine. J Clin Sleep Med. 2024;20(3):475-477.

[Multi-modal physiological time-frequency feature extraction network for accurate sleep stage classification].

Sleep stage classification is essential for clinical disease diagnosis and sleep quality assessment. Most of the existing methods for sleep stage classification are based on single-channel or single-modal signal, and extract features using a single-branch, deep convolutional network, which not only hinders the capture of the diversity features related to sleep and increase the computational cost, but also has a certain impact on the accuracy of sleep stage classification. To solve this problem, this paper proposes an end-to-end multi-modal physiological time-frequency feature extraction network (MTFF-Net) for accurate sleep stage classification. First, multi-modal physiological signal containing electroencephalogram (EEG), electrocardiogram (ECG), electrooculogram (EOG) and electromyogram (EMG) are converted into two-dimensional time-frequency images containing time-frequency features by using short time Fourier transform (STFT). Then, the time-frequency feature extraction network combining multi-scale EEG compact convolution network (Ms-EEGNet) and bidirectional gated recurrent units (Bi-GRU) network is used to obtain multi-scale spectral features related to sleep feature waveforms and time series features related to sleep stage transition. According to the American Academy of Sleep Medicine (AASM) EEG sleep stage classification criterion, the model achieved 84.3% accuracy in the five-classification task on the third subgroup of the Institute of Systems and Robotics of the University of Coimbra Sleep Dataset (ISRUC-S3), with 83.1% macro F1 score value and 79.8% Cohen's Kappa coefficient. The experimental results show that the proposed model achieves higher classification accuracy and promotes the application of deep learning algorithms in assisting clinical decision-making.

Artificial Intelligence Models for the Automation of Standard Diagnostics in Sleep Medicine-A Systematic Review.

Sleep disorders, prevalent in the general population, present significant health challenges. The current diagnostic approach, based on a manual analysis of overnight polysomnograms (PSGs), is costly and time-consuming. Artificial intelligence has emerged as a promising tool in this context, offering a more accessible and personalized approach to diagnosis, particularly beneficial for under-served populations. This is a systematic review of AI-based models for sleep disorder diagnostics that were trained, validated, and tested on diverse clinical datasets. An extensive search of PubMed and IEEE databases yielded 2114 articles, but only 18 met our stringent selection criteria, underscoring the scarcity of thoroughly validated AI models in sleep medicine. The findings emphasize the necessity of a rigorous validation of AI models on multimodal clinical data, a step crucial for their integration into clinical practice. This would be in line with the American Academy of Sleep Medicine's support of AI research.

A bimodal feature fusion convolutional neural network for detecting obstructive sleep apnea/hypopnea from nasal airflow and oximetry signals

The most prevalent sleep-disordered breathing condition is Obstructive Sleep Apnea (OSA), which has been linked to various health consequences, including cardiovascular disease (CVD) and even sudden death. Therefore, early detection of OSA can effectively help patients prevent the diseases induced by it. However, many existing methods have low accuracy in detecting hypopnea events or even ignore them altogether. According to the guidelines provided by the American Academy of Sleep Medicine (AASM), two modal signals, namely nasal pressure airflow and pulse oxygen saturation (SpO2), offer significant advantages in detecting OSA, particularly hypopnea events. Inspired by this notion, we propose a bimodal feature fusion CNN model that primarily comprises of a dual-branch CNN module and a feature fusion module for the classification of 10-second-long segments of nasal pressure airflow and SpO2. Additionally, an Efficient Channel Attention mechanism (ECA) is incorporated into the second module to adaptively weight feature map of each channel for improving classification accuracy. Furthermore, we design an OSA Severity Assessment Framework (OSAF) to aid physicians in effectively diagnosing OSA severity. The performance of both the bimodal feature fusion CNN model and OSAF is demonstrated to be excellent through per-segment and per-patient experimental results, based on the evaluation of our method using two real-world datasets consisting of polysomnography (PSG) recordings from 450 subjects.

518 - Interaction of sleep disturbance and nocturnal scratch in atopic dermatitis patients

Abstract Introduction/Background Itch, scratch and poor sleep are often cited as key symptoms of Atopic Dermatitis (AD). Although approximately 10-41% of children and 7-48% of adults in the general population experience sleep disturbances, this figure climbs to 47-80% in children with AD and 33-90% in adults with AD (Bawany et al., 2021). While great effort has been done to characterize and validate nocturnal scratch and sleep, the relationship between the two remains poorly characterized. Objectives We hypothesize that nocturnal scratch is the reason for the sleep disturbances in patients with AD. Literature suggests that these symptoms are linked, albeit not necessarily causal. Here we investigate the interaction between nocturnal scratch events and sleep disturbances in study participants with AD compared to a healthy cohort. Methods Patients with AD (n=185) and without AD (n=40) aged between 2 to 75 years were recruited across three different studies. Study participants spent the night in a qualified sleep lab while being under observation with thermal videography and polysomnography (PSG). The thermal videography was annotated for both scratch and non-scratch movements by two trained annotators independently with any conflicts being resolved by an arbitrator. The PSG was scored by a credentialed technologist in 30-s epochs per revised American Academy of Sleep Medicine scoring guidelines. The study participants also wore a wrist-worn accelerometer on both the left and right wrists. Results It was noted that sleep disturbances were associated with the observed scratch events, and that the temporal relationship between sleep disturbance and scratching seemed to differ between the AD and healthy cohorts. Surprisingly, however, it was also noted that within a few study participants with AD, scratch events occurred while the patient was in stable REM sleep, most often in the children with AD. Further investigation will provide additional information as to whether this was an artifact or in fact a true observation. We also present an overview of sleep, as well as nocturnal scratch and non-scratch movements in the AD and healthy cohorts, noting specifically how the behaviors differ between the groups, and over the course of the sleep opportunity, across sleep stages and sleep cycles, as well as their relationships to sleep disturbances. Conclusions Nocturnal scratching is often associated with sleep disturbances in AD; however, the causality is not always clear. Moreover, in some cases scratching was observed during stable REM sleep in AD, an observation that requires further investigation.

Insomnia Guidelines—The European Update 2023

The last ten years have seen the development and publication of numerous national and international guidelines devoted to the diagnosis and treatment of insomnia. These include guidelines by the American College of Physicians (ACP), the American Academy of Sleep Medicine (AASM), the British Sleep Society (BSS), the German Sleep Society (GSS), and the European Sleep Research Society (ESRS). Though coming from very diverse authors and backgrounds, these guidelines by and large agree concerning the therapeutic recommendations: cognitive behavioral treatment of insomnia (CBT-I), a multicomponent psychotherapeutic intervention, is unequivocally recommended as a first-line treatment. In this report, we will focus on the most recent guideline update from the ESRS, which was published in November 2023. After suggesting a careful diagnostic procedure, CBT-I, both applied face to face (F2F) or digitally (dCBT-I), is again recommended as a first-line treatment based on the available evidence. Hypnotic medications like benzodiazepines (BZ), benzodiazepine receptor agonists (BZRA), sedating antidepressants, and others are approved for short-term-treatment of up to four weeks. Orexin receptor antagonists (i.e., daridorexant) and prolonged release melatonin are considered as options for longer-term treatment when carefully considering the advantages and disadvantages. Both light therapy and exercise regimens were viewed as promising; however, they still lack convincing evidence for the time being. Given the fact that not every patient responds satisfactorily or even remits following CBT-I or other treatment options, the research agenda calls for the development and evaluation of new therapeutic avenues and combination therapies.

Inflammatory Markers and Sleep Architecture in Sleep Bruxism—A Case-Control Study

Background: Sleep bruxism (SB) is a common sleep-related movement behavior with a multifaceted etiology and a deficiently understood pathophysiology. A recent hypothesis suggests a link between SB and systemic inflammation. The scope of the study was to determine whether bruxers have altered sleep structure and different levels of inflammatory parameters compared to nonbruxers. Methods: A total of 83 adults underwent full-night polysomnography. The polysomnograms were evaluated using the American Academy of Sleep Medicine (AASM) guidelines. Then, the blood samples were obtained from the participants by venipuncture and the analyses were performed. The study group was divided based on bruxism episode index (BEI) into two groups: BEI ≤ 4 and BEI > 4. Results: In comparison with nonbruxers, the oxygen desaturation index (ODI) was significantly higher in severe bruxers (7.5 ± 11.08 vs. 3.33 ± 5.75, p < 0.005), as well as the arousal parameters (7.77 ± 4.68 vs. 4.03 ± 2.97, p < 0.001), and the mean oxygen desaturation (3.49 ± 0.69 vs. 3.01 ± 0.67, p < 0.05). Moreover, the differences in sleep architecture and deprivation of the deep sleep phase were observed, the non-REM sleep stage 3 was significantly shorter in severe bruxers (p < 0.03). Differences were also noted in non-REM sleep stage 1 and REM sleep phase. In the investigated group, there were no statistical differences in inflammatory cytokines levels between bruxers and nonbruxers. Conclusions: Sleep bruxism is associated with sleep structure alterations and may be associated with deep sleep phase deprivation. The inflammatory markers are not linearly correlated with the severity of sleep bruxism expressed as BEI.

Clinical characteristics of obstructive sleep apnea in patients with heart failure

Aims: To investigate the clinical characteristics of obstructive sleep apnea syndrome in patients with heart failure (HF).
 Patients and Methods: From 9/2020 to 9/2021, patients with chronic HF (European Society of Cardiology’s criteria) and obstructive sleep apnea (American Academy of Sleep Medicine’s criteria) were enrolled into the study. Clinical examination, blood test, echocardiography, polysomnography, ventilatory polygraphy were performed in all patients.
 Results: 63 patients were included: 54 male (85.7%), 9 female (14.3%), age 69.57 ± 13.08, BMI 28.63 ± 4.16 kg/m2, neck size >40 cm: 69.3%, 100% male and 88.9% female had waist circumference ≥90 cm, hypertension 78.4%, diabetes 47.6%, HFrEF 12 (18%), HFmrEF 5 (7.9%), HFpEF 46 (73%). 68.3% patients had Epworth ≥10, snoring 92.1%. Compared to patients with apnea-hypopnea index (AHI) <15 and AHI 15-30, patients with AHI>30 had thicker interventricular septum, higher left ventricular mass (LVM) and LVM index, lower e’ velocity, higher E/e’ ratio, higher left atrial volume index, higher systolic artery pulmonary pressure, p<0.05. Multivariate regression showed that age, body mass index, neck size, HbA1C were independent predictors of high AHI, p<0.05.
 Conclusions: Majority of patients with chronic HF and obstructive sleep apnea were male, old age, overweight/obesity, and had large neck size. Hypertension and diabetes and heart failure with preserved ejection fraction were prevalent. Patients with AHI>30 had higher interventricular septal thickness, higher LVM and LVM index, lower e’ velocity, higher E/e’ ratio, higher left atrial volume index, higher systolic artery pulmonary pressure, p<0.05. Age, body mass index, neck size, HbA1C were independent predictors of high AHI, p<0.05.

Exploring Structure Incentive Domain Adversarial Learning for Generalizable Sleep Stage Classification

Sleep stage classification is crucial for sleep state monitoring and health interventions. In accordance with the standards prescribed by the American Academy of Sleep Medicine, a sleep episode follows a specific structure comprising five distinctive sleep stages that collectively form a sleep cycle. Typically, this cycle repeats about five times, providing an insightful portrayal of the subject’s physiological attributes. The progress of deep learning and advanced domain generalization methods allows automatic and even adaptive sleep stage classification. However, applying models trained with visible subject data to invisible subject data remains challenging due to significant individual differences among subjects. Motivated by the periodic category-complete structure of sleep stage classification, we propose a Structure Incentive Domain Adversarial learning (SIDA) method that combines the sleep stage classification method with domain generalization to enable cross-subject sleep stage classification. SIDA includes individual domain discriminators for each sleep stage category to decouple subject dependence differences among different categories and fine-grained learning of domain-invariant features. Furthermore, SIDA directly connects the label classifier and domain discriminators to promote the training process. Experiments on three benchmark sleep stage classification datasets demonstrate that the proposed SIDA method outperforms other state-of-the-art sleep stage classification and domain generalization methods and achieves the best cross-subject sleep stage classification results.

Sleep bruxism and perceived stress: assessment using salivary cortisol levels, a biomarker of stress

Sleep bruxism (SB) corresponds to physiologically based manducatory muscle reactions, with no identifiable neuropathic cause, in relation to anxiety disorders, respiratory disorders or behavioral "tocs". The aim of this study was to assess the relationship between SB and perceived stress by measuring salivary cortisol levels. The study included 61 consenting adult participants: 31 for the study group (bruxers) and 30 for the control group (no bruxers). The diagnosis of BS was made using a standard bruxism assessment questionnaire defined by the American Academy of Sleep Medicine (AASM). The preliminary diagnosis was confirmed by clinical assessment according to AASM criteria. The BRUXi index, a tool proposed by Orthlieb, was used to determine the intensity of bruxism. Perceived stress was measured using the Perceived Stress Scale questionnaire. Unstimulated whole saliva was collected and morning salivary cortisol levels were determined by ELISA. Non-parametric statistical methods were used to analyze the data. The bruxers (study group) had significantly high levels of mean salivary cortisol (12.3±4.2 ng/mL or 34.5±14.6 nmol/L) than the non-bruxers (control group) (5.3±1.2 ng/mL or 14.5±4.6 nmol/L) (p<0.001). The elevated salivary cortisol level was positively associated with perceived stress and SB (p<0.0001) in the study group. These results suggest that bruxing activity is associated with higher levels of perceived psychological stress and salivary cortisol. Despite the absence of polysomnographic recording for the diagnosis of SB, a positive correlation between SB and salivary cortisol levels was observed in bruxers.

Oral Appliance Therapy for Obstructive Sleep Apnea: Clinical Benefits and Limitations

Obstructive sleep apnea (OSA) is a condition characterized by recurrent upper airway obstruction during sleep. It is associated with a wide range of symptoms and complications. Management includes positive airway pressure (PAP), upper airway surgery, oral appliance therapy, positional therapy, and weight loss. Oral appliance therapy treats OSA through direct expansion of the upper airway or indirectly by preventing its collapse. Its effectiveness in improving apnea-hypopnea index and oxygen desaturation is gaining wider approval and the American Academy of Sleep Medicine and the American Academy of Dental Sleep Medicine currently recommend oral appliance as an alternative therapy for those who are unable to PAP therapy. To achieve maximum benefits, it is essential to evaluate factors that can affect treatment outcomes as well as create devices tailored to the individual patient’s dentition and capable of manipulating the degree of mandible advancement. While most are minor and transient, patients should always be warned about the potential adverse effects on mastication and mandibular joint as they can negatively influence treatment compliance. Although PAP shows superior results in improving polysomnography parameters, oral appliance therapy displays comparable health outcomes and greater adherence. Additional studies on establishing patient selection, optimal titration procedure, and measuring objective adherence will further improve oral appliance therapy acceptance.

Recommended protocols for the Multiple Sleep Latency Test and Maintenance of Wakefulness Test in children: guidance from the American Academy of Sleep Medicine.

Maski KP, Amos LB, Carter JC, Koch EE, Kazmi U, Rosen CL. Recommended protocols for the Multiple Sleep Latency Test and Maintenance of Wakefulness Test in children: guidance from the American Academy of Sleep Medicine. J Clin Sleep Med. 2024;20(4):631-641.

Performance of artificial intelligence chatbots in sleep medicine certification board exams: ChatGPT versus Google Bard.

To conduct a comparative performance evaluation of GPT-3.5, GPT-4 and Google Bard in self-assessment questions at the level of the American Sleep Medicine Certification Board Exam. A total of 301 text-based single-best-answer multiple choice questions with four answer options each, across 10 categories, were included in the study and transcribed as inputs for GPT-3.5, GPT-4 and Google Bard. The first output responses generated were selected and matched for answer accuracy against the gold-standard answer provided by the American Academy of Sleep Medicine for each question. A global score of 80% and above is required by human sleep medicine specialists to pass each exam category. GPT-4 successfully achieved the pass mark of 80% or above in five of the 10 exam categories, including the Normal Sleep and Variants Self-Assessment Exam (2021), Circadian Rhythm Sleep-Wake Disorders Self-Assessment Exam (2021), Insomnia Self-Assessment Exam (2022), Parasomnias Self-Assessment Exam (2022) and the Sleep-Related Movements Self-Assessment Exam (2023). GPT-4 demonstrated superior performance in all exam categories and achieved a higher overall score of 68.1% when compared against both GPT-3.5 (46.8%) and Google Bard (45.5%), which was statistically significant (p value < 0.001). There was no significant difference in the overall score performance between GPT-3.5 and Google Bard. Otolaryngologists and sleep medicine physicians have a crucial role through agile and robust research to ensure the next generation AI chatbots are built safely and responsibly.

The ethics of hypopnea scoring.

In February of 2023, the American Academy of Sleep Medicine issued a "recommended" way to score hypopneas using 1A criteria (scoring of hypopneas using a ≥ 3% oxygen desaturation from pre-event baseline) that is at odds with the Centers for Medicare & Medicaid Services mandate of scoring hypopneas using a ≥ 4% oxygen desaturation from pre-event baseline. This dichotomy will present an ethical dilemma for sleep medicine providers. We use the principles of medical ethics to discuss the challenges this discrepancy poses for sleep medicine providers. Disparate hypopnea scoring undermines beneficent patient care and impairs providers' duty to deliver just, equitable care, hence violating the principles of justice and beneficence. This primarily affects older adults, the disabled, the "medically needy," and those living at the federal poverty line dependent on public insurance. This discrepancy creates a situation that falls below acceptable levels of health care justice. It is recommended that the American Academy of Sleep Medicine work with the Centers for Medicare & Medicaid Services to develop a scoring policy that consistently promotes individual sleep health regardless of payor. Skolnik C, Attarian H. The ethics of hypopnea scoring. J Clin Sleep Med. 2024;20(4):615-617.

People with IBD evidence more microarousals during sleep architecture assessments

ObjectivePoor sleep is common in inflammatory bowel disease (IBD) and may be associated with overall worse disease outcomes. While the sleep/IBD literature is growing, the data are often self-reported. Further,...

Diagnostic Accuracy of a Portable Electromyography and Electrocardiography Device to Measure Sleep Bruxism in a Sleep Apnea Population: A Comparative Study.

The gold standard for diagnosing sleep bruxism (SB) and obstructive sleep apnea (OSA) is polysomnography (PSG). However, a final hypermotor muscle activity often occurs after apnea episodes, which can confuse the diagnosis of SB when using portable electromyography (EMG) devices. This study aimed to compare the number of SB episodes obtained from PSG with manual analysis by a sleep expert, and from a manual and automatic analysis of an EMG and electrocardiography (EKG) device, in a population with suspected OSA. Twenty-two subjects underwent a polysomnographic study with simultaneous recording with the EMG-EKG device. SB episodes and SB index measured with both tools and analyzed manually and automatically were compared. Masticatory muscle activity was scored according to published criteria. Patients were segmented by severity of OSA (mild, moderate, severe) following the American Academy of Sleep Medicine (AASM) criteria. ANOVA and the Bland-Altman plot were used to quantify the agreement between both methods. The concordance was calculated through the intraclass correlation coefficient (ICC). On average, the total events of SB per night in the PSG study were (8.41 ± 0.85), lower than the one obtained with EMG-EKG manual (14.64 ± 0.76) and automatic (22.68 ± 16.02) analysis. The mean number of SB episodes decreases from the non-OSA group to the OSA group with both PSG (5.93 ± 8.64) and EMG-EKG analyses (automatic = 22.47 ± 18.07, manual = 13.93 ± 11.08). However, this decrease was minor in proportion compared to the automatic EMG-EKG analysis mode (from 23.14 to 22.47). The ICC based on the number of SB episodes in the segmented sample by severity degree of OSA along the three tools shows a moderate correlation in the non-OSA (0.61) and mild OSA (0.53) groups. However, it is poorly correlated in the moderate (0.24) and severe (0.23) OSA groups: the EMG-EKG automatic analysis measures 14.27 units more than PSG. The results of the manual EMG-EKG analysis improved this correlation but are not good enough. The results obtained in the PSG manual analysis and those obtained by the EMG-EKG device with automatic and manual analysis for the diagnosis of SB are acceptable but only in patients without OSA or with mild OSA. In patients with moderate or severe OSA, SB diagnosis with portable electromyography devices can be confused due to apneas, and further study is needed to investigate this.