Gender differences in the upper airway, craniofacial morphological and polysomnographic parameters in patients with obstructive sleep apnoea.

BACKGROUND: Obstructive sleep apnoea (OSA) is a common sleep disorder characterized by repetitive episodes of upper airway collapse during sleep associated with arousals with or without oxygen desaturation. OBJECTIVE: This study aims to assess and analyse the morphological and neurological factors associated with obstructive sleep apnoea using polysomnography study data and two-dimensional cephalometric analysis of airway and skeletal parameters and their correlation in the patients with varying severities of obstructive sleep apnoea. METHODS: This study included 892 patients who underwent a complete work up, including a thorough history, clinical examination, standard polysomnography study and 2D cephalometric analysis to diagnose obstructive sleep apnoea. This study divided the participants into two groups based on the AHI score from the PSG study: AHI < 15 and AHI > 15 groups. The groups were further divided into male and female groups to study the prevalence of OSA. The analysis involved 13 cephalometric parameters: Seven linear and six angular measurements. The airway parameters measured in this study were minimum posterior airway space (PAS_min), hyoid bone to the mandibular plane (H_MNP) and soft palate length (SPL). All the subjects in this study underwent a standard overnight polysomnography study at the sleep centre in Samsung Medical Center. RESULTS: A total of 892 adult participants (M: F = 727:165, mean age: 50.6 ± 13.2 years and age range: 18-85 years). AHI >15 group was significantly older with higher BMI, NC and WC compared to the AHI < 15 groups in both male and female groups. There was statistical significance observed in N1, N3, AI, ODI, lowest saturation (%) and apnoea max length between the groups (p < .001). The arousal index (AI), especially the respiratory arousal index was considerably higher in the male group. There were significantly higher values in all the PSG parameters in the male group. In the airway parameters, hyoid bone position and soft palate length showed significant differences (p < .001), whereas the PAS did not show any differences (p = .225) between the AHI <15 and AHI >15 groups. The overall skeletal cephalometric parameters showed no significant differences between the groups, whereas the gonial angle and AB to mandibular plane angle showed significant differences in the female group (p = .028, p = .041 respectively). CONCLUSION: The partial correlation of cephalometric parameters with AHI showed a stronger correlation between the H_MNP and AHI in both men and women. The position of the hyoid bone and the soft palate length influences the progression of OSA, especially in male patients. This study found no direct association between the minimum PAS and varying severities of OSA in men and women. We speculate that more than the craniofacial morphological factors such as the sagittal and vertical position of the maxilla and the mandible, the position of the hyoid bone might be more responsible for the severity of OSA.

More extensive white matter disruptions present in untreated obstructive sleep apnea than we thought: A large sample diffusion imaging study.

Obstructive sleep apnea (OSA) may lead to white mater (WM) disruptions and cognitive deficits. However, no studies have investigated the full extent of the brain WM, and its associations with cognitive deficits in OSA remain unclear. We thus applied diffusion tensor imaging (DTI) tractography with multi-fiber models and used atlas-based bundle-specific approach to investigate the WM abnormalities for various tracts of the cerebral cortex, thalamus, brainstem, and cerebellum in patients with untreated OSA. We enrolled 100 OSA patients and 63 healthy controls. Fractional anisotropy (FA) and mean diffusivity (MD) values mapped on 33 regions of interest including WM tracts of cortex, thalamus, brainstem, and cerebellum were obtained from tractography-based reconstructions. We compared FA/MD values between groups and correlated FA/MD with clinical data in the OSA group after controlling for age and body mass index. OSA patients showed significantly lower FA values in multiple WM fibers including corpus callosum, inferior fronto-occipital fasciculus, middle/superior longitudinal fasciculi, thalamic radiations, and uncinate (FDR <0.05). Higher FA values were found in medial lemniscus of patients compared to controls (FDR <0.05). Lower FA values of rostrum of corpus callosum correlated with lower visual memory performance in OSA group (p < .005). Our quantitative DTI analysis demonstrated that untreated OSA could negatively impact the integrity of pathways more broadly, including brainstem structures such as medial lemniscus, in comparison to previous findings. Fiber tract abnormalities of the rostral corpus callosum were associated with impaired visual memory in untreated OSA may provide insights into the related pathomechanism.

Predicting the Risk of Sleep Disorders Using a Machine Learning-Based Simple Questionnaire: Development and Validation Study.

BACKGROUND: Sleep disorders, such as obstructive sleep apnea (OSA), comorbid insomnia and sleep apnea (COMISA), and insomnia are common and can have serious health consequences. However, accurately diagnosing these conditions can be challenging as a result of the underrecognition of these diseases, the time-intensive nature of sleep monitoring necessary for a proper diagnosis, and patients' hesitancy to undergo demanding and costly overnight polysomnography tests. OBJECTIVE: We aim to develop a machine learning algorithm that can accurately predict the risk of OSA, COMISA, and insomnia with a simple set of questions, without the need for a polysomnography test. METHODS: We applied extreme gradient boosting to the data from 2 medical centers (n=4257 from Samsung Medical Center and n=365 from Ewha Womans University Medical Center Seoul Hospital). Features were selected based on feature importance calculated by the Shapley additive explanations (SHAP) method. We applied extreme gradient boosting using selected features to develop a simple questionnaire predicting sleep disorders (SLEEPS). The accuracy of the algorithm was evaluated using the area under the receiver operating characteristics curve. RESULTS: In total, 9 features were selected to construct SLEEPS. SLEEPS showed high accuracy, with an area under the receiver operating characteristics curve of greater than 0.897 for all 3 sleep disorders, and consistent performance across both sets of data. We found that the distinction between COMISA and OSA was critical for accurate prediction. A publicly accessible website was created based on the algorithm that provides predictions for the risk of the 3 sleep disorders and shows how the risk changes with changes in weight or age. CONCLUSIONS: SLEEPS has the potential to improve the diagnosis and treatment of sleep disorders by providing more accessibility and convenience. The creation of a publicly accessible website based on the algorithm provides a user-friendly tool for assessing the risk of OSA, COMISA, and insomnia.

Validating a Consumer Smartwatch for Nocturnal Respiratory Rate Measurements in Sleep Monitoring.

Wrist-based respiratory rate (RR) measurement during sleep faces accuracy limitations. This study aimed to assess the accuracy of the RR estimation function during sleep based on the severity of obstructive sleep apnea (OSA) using the Samsung Galaxy Watch (GW) series. These watches are equipped with accelerometers and photoplethysmography sensors for RR estimation. A total of 195 participants visiting our sleep clinic underwent overnight polysomnography while wearing the GW, and the RR estimated by the GW was compared with the reference RR obtained from the nasal thermocouple. For all participants, the root mean squared error (RMSE) of the average overnight RR and continuous RR measurements were 1.13 bpm and 1.62 bpm, respectively, showing a small bias of 0.39 bpm and 0.37 bpm, respectively. The Bland-Altman plots indicated good agreement in the RR measurements for the normal, mild, and moderate OSA groups. In participants with normal-to-moderate OSA, both average overnight RR and continuous RR measurements achieved accuracy rates exceeding 90%. However, for patients with severe OSA, these accuracy rates decreased to 79.45% and 75.8%, respectively. The study demonstrates the GW's ability to accurately estimate RR during sleep, even though accuracy may be compromised in patients with severe OSA.

Novel neuroelectrophysiological age index associated with imaging features of brain aging and sleep disorders.

Sleep architecture and microstructures alter with aging and sleep disorder-led accelerated aging. We proposed a sleep EEG based brain age prediction model using convolutional neural networks. We then associated the estimated brain age index with brain structural aging features, sleep disorders and various sleep parameters. Our model also showed a higher BAI (predicted brain age minus chronological age) is associated with cortical thinning in various functional areas. We found a higher BAI for sleep disorder groups compared to healthy sleepers, as well as significant differences in the spectral pattern of EEG among different sleep disorders (lower power in slow and ϑ waves for sleep apnea vs. higher power in ß and σ for insomnia), suggesting sleep disorder-dependent pathomechanisms of aging. Our results demonstrate that the new EEG-BAI can be a biomarker reflecting brain health in normal and various sleep disorder subjects, and may be used to assess treatment efficacy.

Development and evaluation of myofunctional therapy support program (MTSP) based on self-efficacy theory for patients with obstructive sleep apnea.

PURPOSE: The aim of this study is to determine the impact of myofunctional therapy support program (MTSP) based on self-efficacy theory compared to no support during myofunctional therapy (MT) in patients with obstructive sleep apnea (OSA). METHODS: Thirty-one patients with OSA were randomized into two groups: 12 weeks of treatment with the MTSP developed in this study (experimental group) and one education session of MT (control group). Patients were evaluated at the beginning and the end of the study using questionnaires (self-efficacy scale, Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index, snoring intensity and frequency, dry mouth) and polysomnography. RESULTS: The control (n = 15) and experimental (n = 16) groups had similar results for all variables at study entry. The control group showed no significant change in any variables during the study period. In contrast, the experimental group showed a significant increase in self-efficacy 61.38 ± 9.50 to 65.56 ± 10.89 (p = 0.020) and a significant decrease in apnea-hypopnea index (AHI) 19.51 ± 11.41 to 14.11 ± 9.13 (p = 0.039), daytime sleepiness 9.88 ± 3.84 to 7.56 ± 3.42 (p = 0.028), snoring intensity 5.57 ± 3.13 to 4.44 ± 2.68 (p = 0.008), and dry mouth 6.44 ± 3.14 to 3.63 ± 2.33 (p = 0.005), compared to the baseline. No significant change in lowest SaO2 (p = 0.969), sleep quality (p = 0.307), and snoring frequency (p = 0.321) during the study period. CONCLUSIONS: The intensive and interactive intervention of MTSP improved the self-efficacy of OSA patients, and consequently, resulted in sign and symptom relief, such as AHI, daytime sleepiness, snoring and dry mouth. The MTSP was dedicated to the nurse practitioner to improve the way to dispense the MT. This research has implications for the successful treatment of OSA.

Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG.

BACKGROUND: This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal. METHODS: A convolutional neural network (CNN) was used as an identification model and implemented using a one-dimensional convolutional, pooling, and fully connected layer. An optimal architecture is incorporated into the CNN model for the precise identification of SA severity. A total of 144 subjects were studied. The nocturnal single-lead ECG signal was collected, and the short-term normal ECG was extracted from them. The short-term normal ECG was segmented for a duration of 30 seconds and divided into two datasets for training and evaluation. The training set consists of 82,952 segments (66,360 training set, 16,592 validation set) from 117 subjects, while the test set has 20,738 segments from 27 subjects. RESULTS: F1-score of 98.0% was obtained from the test set. Mild and moderate SA can be identified with an accuracy of 99.0%. CONCLUSION: The results showed the possibility of automatically identifying SA severity based on a short-term normal ECG signal.

The Role of Anterior Thalamic Deep Brain Stimulation as an Alternative Therapy in Patients with Previously Failed Vagus Nerve Stimulation for Refractory Epilepsy.

Deep brain stimulation (DBS) has provided new treatment options for refractory epilepsy; however, treatment outcomes of DBS in refractory epilepsy patients previously treated with vagus nerve stimulation (VNS) have not been clarified. Herein, treatment outcomes of DBS of the anterior nucleus of the thalamus (ANT-DBS) in patients who had previously experienced VNS failure are reported. Seven patients who had previously experienced VNS failure underwent ANT-DBS device implantation. VNS was turned off before DBS device implantation. Monthly seizure counts starting from baseline to 12-18 months after DBS were analyzed. Five (71.3%) of the 7 patients experienced a >50% reduction of seizure counts after DBS; 1 responder reached a seizure-free status after DBS therapy. Of the 2 nonresponders, 1 subject showed improvement in seizure strength and duration, which lessened the impact of the seizures on the patient's quality of life. This is the first study in which favorable outcomes of ANT-DBS surgery were observed in individual patients with refractory epilepsy who had not responded to prior VNS. Further studies with a larger number of subjects and longer follow-up period are needed to confirm the feasibility of ANT-DBS in patients who have previously experienced VNS failure.

SPECT perfusion changes during ictal automatisms with preserved responsiveness in patients with right temporal lobe epilepsy.

Ictal automatism with preserved responsiveness (APR) has been reported, particularly in nondominant temporal lobe epilepsy (TLE), but its pathophysiology remains poorly understood. This study sought to investigate the relationship between APRs and increased cerebral blood flow (CBF) using ictal single photon emission computed tomography (SPECT) in TLE. Forty-seven subjects with right mesial TLE (15 with and 32 without APR) were enrolled. Patients with APR (APR+) were subdivided into four groups according to degree of responsiveness during seizures. Cerebral blood flow changes during these seizures were semiquantitatively assessed by subtraction ictal SPECT coregistered to MRI (SISCOM). Hyperperfusion in temporal regions did not vary significantly between the APR+ and APR- groups. Cerebral blood flow changes in the frontal area, insula, cingulum, and occipital area were also nonsignificant. However, hyperperfusion in the ipsilateral parietal areas was more frequent in the APR- group than in the APR+ group. Furthermore, hyperperfusion of the contralateral basal ganglia showed an inclination to be more common in the APR- group, but without statistical significance. The study suggested that the involvement of the parietal association cortex during seizure may play an important role in ictal loss of consciousness in TLE. Further studies will be needed to elucidate the pathophysiology of changes in consciousness during temporal lobe seizures.

Validation of the Korean Munich Chronotype Questionnaire.

PURPOSE: The Munich Chronotype Questionnaire (MCTQ) assesses actual sleep-wake timing and has advantages compared to prior chronotype questionnaires in that it differentiates sleep-wake patterns between work days and free days and uses corrected mid-sleep time on free days after correcting for accumulated sleep debt over the week to categorize chronotype. The current study, we validated the Korean version of the MCTQ. METHODS: In this study, 310 participants (mean age = 27.09 ± 5.64; 78.1% females) completed the Korean version of the MCTQ. RESULTS: MCTQ parameters were significantly correlated with MEQ (Morningness-Eveningness Questionnaire) scores (│r│ ≥ 0.48), and test-retest reliability was ≥ 0.72. Cutoff scores of 2.5%, which correlated to 2.36 and 8.57 mid-sleep times in our sample, showed the best convergence with MEQ when categorizing chronotype. CONCLUSIONS: Our study suggests that the MCTQ is a useful questionnaire in assessing chronotype in young adults.

Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network.

In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F1-score of 0.99% were attained with the training dataset; these values were all 0.96% when the CNN was applied to the testing dataset. These results show that the proposed CNN model can be used to detect OSA accurately on the basis of a single-lead ECG. Ultimately, this CNN model may be used as a screening tool for those suspected to suffer from OSA.

Clinical and polysomnographic characteristics of excessive daytime sleepiness in children.

PURPOSE: This study aimed to delineate the clinical and polysomnography (PSG) characteristics of sleep disorders in children with excessive daytime sleepiness (EDS). METHODS: Between February 2002 and June 2015, 622 pediatric patients with EDS were evaluated with overnight PSG and the Multiple Sleep Latency Test at the Samsung Medical Center. The medical records; questionnaire responses about depression, sleepiness, sleep habits; and sleep study data of 133 patients without obstructive sleep apnea (OSA) were reviewed retrospectively. RESULTS: The patients (63 girls, 70 boys) slept for an average of 7 h 30 min and 8 h 44 min on weekdays and weekends, respectively. The mean Epworth Sleepiness Scale score was 11.01 ± 4.09 and did not differ significantly among sleep disorders. Among the 102 patients who completed the depression questionnaire, 53 showed depressive feelings, which were moderate or severe in 39, with no significant differences among specific sleep disorders. Thirty-four patients exhibited normal PSG results. Seventeen of them were concluded as not having any sleep disorders, and the others as having delayed sleep phase disorder (DSPD). Narcolepsy (n = 78) was the most common disorder, followed by DSPD (n = 17) and idiopathic hypersomnia (n = 12). CONCLUSIONS: Pediatric patients with EDS had various sleep disorders and some did not have any sleep disorder despite EDS. More than half the patients with EDS showed depressive feelings affecting their daily lives. For pediatric patients with EDS, a systematic diagnostic approach including questionnaires for sleep habits and emotion and PSG is essential for accurate diagnosis and treatment.

Discordant sleep parameters among actigraphy, polysomnography, and perceived sleep in patients with sleep-disordered breathing in comparison with patients with chronic insomnia disorder.

PURPOSE: Although the clinical usefulness of actigraphy (ACT) in chronic insomnia disorder (CID) is extensively investigated, the evaluation on the utility of ACT in sleep-disordered breathing (SDB) is limited and the report of comparison with perceived sleep by patients and polysomnography (PSG) is scarce. METHODS: Thirty-six patients with SDB and 30 with CID underwent PSG and ACT, and they reported perceived sleep time and latency simultaneously. Sleep parameters (total sleep time [TST], sleep onset latency [SOL], sleep efficiency, and wakefulness after sleep onset [WASO]) were compared. Agreement among parameters was assessed with intraclass correlation coefficient (ICC) and 95% confidence interval. RESULTS: ACT overestimated TST and underestimated SOL and WASO relative to PSG in both groups. Perceived sleep was commonly undervalued relative to PSG or ACT. In post hoc analyses, perceived sleep was significantly shorter and SOL significantly longer compared to PSG and ACT in both groups. Perception of sleep time by PSG was significantly higher than perception by ACT. In the CID group, all sleep parameters were significantly correlated between ACT and PSG (ICC = 0.627-0.813). In the SDB group, only TST was significantly correlated (ICC = 0.804). CONCLUSION: ACT is useful to monitor sleep and sleep quality in patients with CID. In patients with SDB, TST by ACT is reliable. However, ACT is insufficient to assess sleep quality due to its low agreement with PSG. Significantly lower sleep perception relative to PSG and ACT should be considered in the treatment of sleep disorders.

Cardiopulmonary Coupling Analysis Using Home Sleep Monitoring System Based on Air Mattress.

This study investigates the feasibility of cardiopulmonary coupling (CPC) using home sleep monitoring system. We have designed a system to measure respiratory signals and normal-to-normal (NN) interval series in a non-contact based on air mattress. Then, CPC analysis was conducted using extracted respiratory signals and NN interval series, and six CPC parameters were extracted (VLFC, LFC, HFC, e-LFC, e-LFCNB and e-LFCBB). To evaluate the proposed method, two statistical analyses were conducted between the CPC parameters extracted by the electrocardiogram-based conventional method and the air mattress-based proposed method for five patients with obstructive sleep apnea and hypopnea (OSAH). Wilcoxon's signed rank test on the CPC parameters of the two methods indicated no significant differences (p > 0.05) and Spearman's rank correlation analysis showed high positive correlations (r ≥ 0.7, p < 0.05) between the two methods. Therefore, the proposed method has the potential for performing CPC analysis using air mattress-based system.

White matter alterations in narcolepsy patients with cataplexy: tract-based spatial statistics.

Functional imaging studies and voxel-based morphometry analysis of brain magnetic resonance imaging showed abnormalities in the hypothalamus-thalamus-orbitofrontal pathway, demonstrating altered hypocretin pathway in narcolepsy. Those distinct morphometric changes account for problems in wake-sleep control, attention and memory. It also raised the necessity to evaluate white matter changes. To investigate brain white matter alterations in drug-naïve narcolepsy patients with cataplexy and to explore relationships between white matter changes and patient clinical characteristics, drug-naïve narcolepsy patients with cataplexy (n = 22) and healthy age- and gender-matched controls (n = 26) were studied. Fractional anisotropy and mean diffusivity images were obtained from whole-brain diffusion tensor imaging, and tract-based spatial statistics were used to localize white matter abnormalities. Compared with controls, patients showed significant decreases in fractional anisotropy of white matter of the bilateral anterior cingulate, fronto-orbital area, frontal lobe, anterior limb of the internal capsule and corpus callosum, as well as the left anterior and medial thalamus. Patients and controls showed no differences in mean diffusivity. Among patients, mean diffusivity values of white matter in the bilateral superior frontal gyri, bilateral fronto-orbital gyri and right superior parietal gyrus were positively correlated with depressive mood. This tract-based spatial statistics study demonstrated that drug-naïve patients with narcolepsy had reduced fractional anisotropy of white matter in multiple brain areas and significant relationship between increased mean diffusivity of white matter in frontal/cingulate and depression. It suggests the widespread disruption of white matter integrity and prevalent brain degeneration of frontal lobes according to a depressive symptom in narcolepsy.

Correlation between headaches and affective symptoms in patients with epilepsy.

Headaches are a neglected entity in patients with epilepsy (PWE), although PWE have a high chance of suffering from seizure-related as well as seizure-unrelated headaches. We aimed to identify the prevalence and characteristics of headaches and investigate the correlation between headaches and affective symptoms in PWE. Consecutive PWE who visited our tertiary outpatient clinic were interviewed about headaches and epilepsy. Affective symptoms were evaluated using the Korean version of the Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI), and suicidality portion of the Mini-International Neuropsychiatric Interview. We classified headaches as interictal or seizure-related headaches (SRHs; pre- and postictal). Tension-type headache and migraine were defined based on International Classification of Headache Disorders criteria. From the initial cohort of 177 patients (92 men, mean age: 37.1years), 73 (41.2%) reported suffering from interictal (N=34, 19.2%), preictal (N=3, 1.7%), and postictal (N=48, 27.1%) headaches. Univariate analysis revealed significantly higher BDI and BAI scores in the headache group. Tension-type headaches were the most frequent, and half of the interictal headaches and most of the SRHs were untreated. Spearman's partial correlation analyses showed that headaches overall were significantly related with depression and anxiety. Interictal headaches were correlated with depression only, and postictal headaches were correlated with depression as well as suicidality, separately. These results show that investigating and controlling headaches may relieve affective symptoms and ultimately improve the quality of life of PWE.

Can an exercise bicycle be safely used in the epilepsy monitoring unit?: An exercise method to provoke epileptic seizures and the related safety issues.

BACKGROUND AND PURPOSE: Long-term videoelectroencephalogram (video-EEG) monitoring is performed to diagnose an epileptic seizure and to investigate the differential diagnosis of paroxysmal events. To provoke an epileptic seizure, an exercise method is performed in some cases during long-term video-EEG recording in the epilepsy monitoring unit (EMU). The purpose of this study was two-fold: to assess the frequency and severity of adverse events associated with the use of an exercise bicycle and to find a way to safely use it in the EMU. METHODS: A retrospective survey was performed on all epileptic seizure videos recorded in the EMU from January 2012 to December 2013. Three hundred and fifty patients were included in this study. RESULTS: Eleven patients experienced an epileptic seizure while riding the exercise bicycle in the EMU. One patient's foot got stuck between the cycling pedal and its strap, and one patient fell off the exercise bicycle during the epileptic seizure. However, there were no serious adverse events over two years. CONCLUSION: Epileptic seizures were not frequent while riding the exercise bicycle, and serious injuries did not occur. But, there is a need to improve the safety in the EMU to control the potentially dangerous factors associated with the use of the exercise bicycle and to continuously monitor the patients with help from the staff.

Resection of individually identified high-rate high-frequency oscillations region is associated with favorable outcome in neocortical epilepsy.

OBJECTIVES: High-frequency oscillations (HFOs) represent a novel electrophysiologic marker of endogenous epileptogenicity. Clinically, this propensity can be utilized to more accurately delineate the resection margin before epilepsy surgery. Currently, prospective application of HFOs is limited because of a lack of an exact quantitative measure to reliably identify HFO-generating areas necessary to include in the resection. Here, we evaluated the potential of a patient-individualized approach of identifying high-rate HFO regions to plan the neocortical resection. METHODS: Fifteen patients with neocortical seizure-onset zones (SOZs) underwent intracranial electroencephalographic monitoring. To identify interictal HFOs, we applied an automated, hypersensitive HFO-detection algorithm followed by post hoc processing steps to reject false detections. The spatial relationship between HFO distribution and the SOZ was evaluated. To address high interpatient variability in HFO properties, we evaluated the high-rate HFO region, an unbiased statistical parameter, in each patient. The relationship between resection of the high-rate HFO region and postoperative outcome was examined. RESULTS: Grouped data demonstrated that the rate of ripple (60-200 Hz) and fast ripple (200-500 Hz) was increased in the SOZ (both p < 0.01). Intrapatient analysis of the HFO distribution localized the SOZ in 11 patients. High-rate HFO regions were determined in all patients by an individually adjusted threshold. Resection of high-rate HFO regions was significantly associated with a seizure-free outcome (p < 0.01). The extent/ratio of SOZ or spiking region resection did not differ between seizure-free and seizure-persistent groups. SIGNIFICANCE: Intrapatient analysis of high-rate HFOs provides more detailed description of HFO-generating areas and can mark the areas of clinically significant epileptogenicity--a crucial component of the neocortical epileptic network that should be removed to achieve a good outcome. Validating and adopting an unbiased quantitative HFO parameter has the potential to propel wider and prospective utilization of HFOs in the surgical treatment of neocortical epilepsy and to improve its outcome.

Sex-specific patterns of discomfort in patients with restless legs syndrome.

STUDY OBJECTIVES: Sex differences in the prevalence of restless legs syndrome (RLS) have been reported, with a higher prevalence in women than in men. However, sex differences in clinical presentation remain unclear. We aimed to investigate the phenotypic differences in patients with RLS between sexes by comparing clinical presentations, iron status, polysomnographic parameters, and treatment. METHODS: We retrospectively evaluated 614 patients (225 men, 389 women) diagnosed with RLS. To enhance the robustness of the study, an age-matched control group of 179 men and 286 women without sleep disorders was also included. Information on demographics and sleep-related questionnaires were collected. Iron status was evaluated using blood samples, and polysomnography was performed to evaluate periodic leg movements and comorbid sleep disorders. RESULTS: Our analysis revealed no sex difference in the severity of RLS but a difference in the pattern of symptoms. Women had more frequent symptoms of pain and awakening during sleep, while men had more common motor symptoms (both self-reported symptoms and periodic leg movement on polysomnography). Women with RLS also had lower iron parameters and received more frequent iron supplementation therapy than men. In contrast to women with RLS, who presented higher sleep disturbances and depressive mood, men with RLS had a higher risk of comorbidities such as hypertension and cardiovascular disease. These sex differences were notably more pronounced than in the control group. CONCLUSIONS: This study suggests that sex differences exist in RLS phenotypes, and clinicians should consider these differences for treatment. CITATION: Kim J, Kim JR, Park HR, Joo EY. Sex-specific patterns of discomfort in patients with restless legs syndrome. J Clin Sleep Med. 2024;20(2):253-259.

Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity.

BACKGROUND: /Objective: Automatic apnea/hypopnea events classification, crucial for clinical applications, often faces challenges, particularly in hypopnea detection. This study aimed to evaluate the efficiency of a combined approach using nasal respiration flow (RF), peripheral oxygen saturation (SpO2), and ECG signals during polysomnography (PSG) for improved sleep apnea/hypopnea detection and obstructive sleep apnea (OSA) severity screening. METHODS: An Xception network was trained using main features from RF, SpO2, and ECG signals obtained during PSG. In addition, we incorporated demographic data for enhanced performance. The detection of apnea/hypopnea events was based on RF and SpO2 feature sets, while the screening and severity categorization of OSA utilized predicted apnea/hypopnea events in conjunction with demographic data. RESULTS: Using RF and SpO2 feature sets, our model achieved an accuracy of 94 % in detecting apnea/hypopnea events. For OSA screening, an exceptional accuracy of 99 % and an AUC of 0.99 were achieved. OSA severity categorization yielded an accuracy of 93 % and an AUC of 0.91, with no misclassification between normal and mild OSA versus moderate and severe OSA. However, classification errors predominantly arose in cases with hypopnea-prevalent participants. CONCLUSIONS: The proposed method offers a robust automatic detection system for apnea/hypopnea events, requiring fewer sensors than traditional PSG, and demonstrates exceptional performance. Additionally, the classification algorithms for OSA screening and severity categorization exhibit significant discriminatory capacity.