جستجوی مقالات مرتبط با کلیدواژه « polysomnography » در نشریات گروه « پزشکی »

A sleep apnea monitor (BM2000A) is a wrist-worn device that measures oxygen saturation and pulse rate during sleep. This study aimed to evaluate the efficacy of the watch-like BM2000A for screening obstructive sleep apnea (OSA).BM2000A; Home sleep apnea testing; Obstructive sleep apnea; Polysomnography; Wrist pulse oximeter 102 patients complaining of sleep breathing disorders were included; 81% were men and 19% were women. All participants underwent overnight simultaneous polysomnography (PSG) and BM2000A sleep monitoring. The number of apneas and hypopneas, apnea-hypopnea index (AHI), percentage of time spent with oxygen saturation under 90%, average oxygen saturation, lowest oxygen saturation, and duration of sleep were computed by the BM2000A and PSG. Then, these parameters were compared to validate the BM2000A.All parameters, measured with BM2000A, had a good correlation (r ≥ 0.6, p < 0.0001) with PSG-derived indexes, except for sleep time (r = 0.19, p = 0.061) and hypopnea index (r = 0.4, p < 0.0001). AHI had the strongest correlation (r = 0.87, p < 0.0001). The mean difference between AHI values calculated with PSG and wrist-worn pulse oximeter (WPO) was -17.66 events/h (95% CI: -50.39 to 15.06). In AHI ≥ 5, BM2000A had 90.7% sensitivity, 100% specificity, 91.2% accuracy, and 0.994 area under the curve. Using AHI ≥ 5, ≥ 15, and ≥30 as the screening criteria, optimal WPO-AHI cutoffs to improve the screening accuracy were 3.10, 8.92, and 13.05.BM2000A-derived results properly correlate with PSG and can provide OSA screening with good sensitivity and specificity.

There are studies about polysomnographic (PSG) characteristics of patients with either obesity hypoventilation syndrome (OHS) or addiction. We aimed to investigate the PSG characteristics of obstructive sleep apnea (OSA) patients with opium addiction, those on methadone maintenance treatment (MMT), and non-addicts for the treatment of addiction.

In this cross-sectional study, we enrolled 75 patients with OHS in the Bamdad Respiratory and Sleep Research Center affiliated with the Isfahan University of Medical Sciences between January 2020 and February 2021. The patients were categorized into three groups: Opium addicts (OA), MMT, and non-addicts (NA). All patients completed screening questionnaires for OSA. This included the Epworth sleepiness scale (ESS), stop-bang questionnaire, and Berlin questionnaire and the data analyzed by SPSS software, version 24.

A total of 75 OHS patients (54 men [72%] and 21 women [28%]) were studied in three groups, including OA (n=30), MMT (n=15), and NA (n=30). The apnea hypopnea index was not significantly different between the three groups. The longest apnea duration was higher in the OA than in other groups (P=0.001). Central apnea index (P=0.01), longest hypopnea duration (P=0.04), PaCO2 (P=0.04), and time with SpO2˂90% (T90) (P=0.009) were higher in the MMT than in other groups. Furthermore, the minimum SpO2 was lower in the MMT than in other groups (P=0.03). 

Some of the sleep disturbances were worse in the MMT than in the OA group. This suggests the need for further studies to compare the effects of opium and methadone on sleep in OHS patients.

Primary sleep disorders are common in patients with epilepsy. Seizures, epileptiform discharges, and antiepileptic drugs alter the sleep architecture of patients with juvenile myoclonic epilepsy (JME). We evaluated sleep architecture and its quality in these patients.

Thirty patients with JME (11 men and 19 women with mean age of 21.10 ± 4.55 years) and 30 healthy controls underwent overnight polysomnography (PSG). Sleep quality and daytime sleepiness were assessed using Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS), respectively.

Myoclonus and generalized tonic-clonic seizures (GTCS) were present in all patients with JME, while absence seizures were in 13.3%. Sleep deprivation was the most frequent precipitating factor for seizures (56.6%) followed by fatigue, sound, and photic stimulation. Patients with JME reported a statistically significant drop in sleep efficiency (P<0.001) with prolonged sleep onset latency (P<0.001). There was prolongation in the N1 stage of non-rapid eye movement (NREM) sleep (P=0.002), and reduction in the N2 stage of NREM (P<0.001) and rapid eye movement (REM) sleep (P<0.001). The median PSQI score was higher in patients with JME, suggesting poor sleep quality (P<0.001), and the daytime sleepiness was not different as indicated by the similar median ESS score (P=0.033).

Our results suggest a significant alteration in the sleep architecture of patients with JME with reduced sleep efficiency and poor sleep quality. The possible role of the disease itself is suggested for these alterations as a simi-lar trend was also observed in drug naïve patients.

Periodic limb movements of sleep (PLMS) and obstructive sleep apnea (OSA) are two common sleep disorders that frequently co-occur in one subject. In this study, we evaluated the polysomnographic (PSG) features of patients with OSA with and without PLMS.

Patients with OSA diagnosed by PSG who referred to our sleep clinic over 2 years were
studied for PLMS during a standard diagnostic sleep study. PSG features including apnea-hypopnea index (AHI), oxygen desaturation index (ODI), and sleep quality were evaluated and compared between patients with OSA with and without PLMS.

We evaluated 122 patients with OSA, of whom 17 had comorbid PLMS. Mean sleep quality was significantly lower in patients with PLMS compared to those without PLMS (P < 0.05). There was no significant difference in terms of mean age, gender, arousal index (AI), ODI, and apnea/hypopnea between the two groups.

Patients with OSA with PLMS comorbidity have remarkably lower sleep quality and this finding is independent of the severity of arousals or respiratory events. Proper evaluation, diagnosis, and treatment of PLMS comorbidity in patients with OSA might improve treatment response.

Sleep apnea is one of the most common sleep disorders that facilitating and accelerating its diagnosis will have positive results on its future trend.  This study aimed to diagnosis the sleep apnea types using the optimized neural network. This descriptive-analytical study was done on 50 cases of patients referred to the sleep clinic of Imam Khomeini Hospital in Tehran, including 11 normal, 13 mild, 17 moderate and 9 severe cases. At the first, the data were pre-processed in three stages, then The Electrocardiogram (ECG) signal was decomposed to 8 levels using wavelet transform convert and 6 nonlinear features for the coefficients of this level and 10 features were calculated for RR Intervals. For apnea categorizing classes, the multilayer perceptron neural network was used with the backpropagation algorithm. For optimizing Multi-layered Perceptron (MLP) weights, the Particle Swarm Optimization (PSO) evolutionary optimization algorithm was used.  The simulation results show that the accuracy criterion in the MLP network is allied with the Backpropagation (BP) training algorithm for different types of apnea. By optimizing the weights in the MLP network structure, the accuracy criterion for modes normal, obstructive, central, mixed was obtained %96.86, %97.48, %96.23, and %96.44, respectively. These values indicate the strength of the evolutionary algorithm in improving the evaluation criteria and network accuracy.   Due to the growth of knowledge and the complexity of medical decisions in the diagnosis of the disease, the use of artificial neural network algorithms can be useful to support this decision.

Klein-Levin Syndrome (KLS) is a rare disorder identified by recurrent hypersomnia, over-eating, or hypersexuality and is associated with cognitive dysfunction or abnormal behaviors. 

In this paper, we presented a 36-year-old single female with chief complaints of headaches, tiredness, and sleepiness. She reported attacks of hypersomnia and memory deficits from childhood. She experienced addiction to alcohol, caffeine, and sugar, also depression, and suicidal taught. The full medical work-up did not indicate any organic illness. Polysomnography and Multiple Sleep Latency Tests indicated narcolepsy. Finally, she was diagnosed with Klein-Levin syndrome. 

Kleine-Levin Syndrome is a challenging diagnosis. The medical history of clinical symptoms and exclusion of other diagnoses can lead to better diagnosis in patients with hypersomnolence.

Obstructive sleep apnea (OSA) is associated with arousals due to thecessation of breathing during sleep. On the other hand, sleep spindles, an EEG wave mainlyseen in stage 2 of non-REM sleep (N2), are responsible for many functions including themaintenance of sleep. We aimed to investigate the association between sleep spindles andOSA and compare the additional polysomnography (PSG) metrics in a group of patientswith OSA. Fifty consecutive patients with moderate and severe OSA wererecruited. Association of apnea-hypopnea index (AHI) with spindles in N2 and arousalswere evaluated. Other PSG metrics were compared in the moderate versus severe group. Body mass and snore indices were significantly more in the severe group (p=0.002and p<0.001, respectively). Arousals were more frequently seen in severe OSA cases(p=0.064). Sleep spindle index did not have any relationship with AHI and the numberof arousals. However, arousals were weakly correlated with AHI (Spearman’s rho= 0.293,p=0.039) and snore index (Spearman’s rho= 0.365, p=0.010). Severity of OSA did not show a clear correlation with spindle density in N2.Further studies with larger samples and a control group are needed to prove a relationshipbetween sleep spindles and OSA.

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.

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.

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.

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.

Polysomnography is a gold standard method for examination of information obtained from physiological changes in the body related to sleep. The aim of this study was to diagnose respiratory disorders in children and adolescents with sleep disorders by the use of polysomnography.
The sample of this cross-sectional retrospective study included 112 children and adolescents aged 0-18 years who were referred to the sleep ward of Qazvin children's hospital due to sleep disorders. After recording the participants’ comprehensive demographic and medical history, questionnaires regarding their sleep history were filled in by their parents. Then the results of polysomnography and severity of obstructive sleep apnea (OSA) were identified. SPSS 21 software and frequency tables were used to determine the prevalence of the variables.
The most common sleep disorder was restless sleep (68; 60.71%). One hundred and four (92.85%) patients had sleep apnea. Also, 66 (58.92%) patients with severe OSA, 19 (16.96%) patients with moderate OSA, 14 (12.5%) patients with mild OSA and 5 (4.46%) patients with central sleep apnea were observed. Eighty eight (78.57%) children had less than normal sleep efficiency (less than 90%) and 34 (30.35%) had normal and desirable sleep efficiency. Total adenotonsillectomy, medical therapy for OSA and non-invasive ventilation (NIV) were recommended for 46 (41.7%), 27 (24.10%) and 20 (17.85%) patients, respectively.
Symptoms of respiratory disorders during sleep were seen in our results, especially in children with a history of adenotonsillectomy. Based on the severity of symptoms, medication was prescribed for children. Referral of children suspected of sleep disorder to a physician is essential for control and treatment of this disease.

The concurrence of chronic obstructive pulmonary disease (COPD) and obstructive sleep apnea (OSA) is known as overlap syndrome (OS). The obstruction of the upper airway leads to OSA and the obstruction of the lower airway leads to COPD. The aim of this study was to compare polysomnographic findings of patients with OS according to severity of lower airway obstruction.

Seventy‑two patients were included in this cross‑sectional study. Patients with COPD referred to a sleep clinic with suspicion of OSA were evaluated by polysomnography (PSG). PSG findings were interpreted based on the American Academy of Sleep Association criteria (2012). COPD severity was categorized into four groups based on GOLD criteria using forced expiratory volume in the first second (FEV1 ). PSG findings also were compared between patients regarding severity of lower airway obstruction (FEV1 ≥50% and FEV1 <50%).

Sixty‑eight of the patients had OS. Twenty‑nine (42.6%) were male. The mean age was 62.3 ± 6.88 years. Thirty‑two (54.4%) of the patients were in GOLD 2. The mean apnea/hypopnea index was 57.41 ± 36.16. Seventy‑two percent of patients had severe OSA. Severe OSA was more prevalent in patients of GOLD 2 and 3 groups compared to the other groups. Among PSG findings, only N2 sleep stage was significantly longer in patients with FEV1 < 50% than in patients with FEV1 ≥50% (61.5 ± 11.2, 55.3 ± 13.4, P = 0.039).

Polysomnographic findings (except N2 stage) are not different in patients with OS with respect to severity of lower airway obstruction.

Among sleep-related disorders, Sleep apneahas been under more attention and it’s the most common respiratory disorder in which respirationceases frequently which can lead to serious health disorders and even mortality. Polysomnography is the standard method for diagnosing this disease at the moment which is costly and time-consuming. The present study aimed at analyzing vital signals to diagnose Sleep apneausing machine learning algorithms.

This analytical–descriptive was conducted on 50 patients (11 normal, 13 mild, 17 moderate and 9 severe patients) in the sleep clinic of Imam Khomeini hospital. Initially, data pre-processing was carried out in two steps(noise elimination and moving average algorithm). Next, using thesingular value decompositionmethod, 12 features were extracted for airflow. Finally, to classify data, SVM with quadratic, polynomialand RBF kernels were trained and tested.

After applying different kernel functions on SVM, the RBF kernel showed the most efficient performance.After 10 fold cross validation method for evaluation, the mean accuracy obtained for normal, apnea, and hypopnea modes were 92.74%, 91.70%, 93.26%.

The results show that in online applications or applications where the volume and time of calculations and at the same time the accuracy of the result is very important, The disease can be diagnosed with acceptable accuracy using machine learning algorithms.

Sleep apnea syndrome can be considered as one of the most serious risk factors of sleep disorder. Due to the lack of information about this disease, many causes of unexpected deaths have been identified. With increasing the number of patients with this disease around the world, many patients suffer apnea complications. Most of them are not treated because of the complex and costly and time - c onsuming polysomnography (PSG) diagnostic procedure.

This descriptive - analytical study was performed on 50 patients referred to sleep clinic of Imam Khomeini Hospital in Tehran, Attempts to design, and develop a system for detection of sleep apnea and its severity using ECG signals, RR intervals and airflow. The random forest algorithm and MATLAB2016 were used in the design of the system that the algorithm inputs are extracted 8 features nonlinear in time - frequency domain from airflow and ECG signals and 10 nonlinear features of RR intervals.

The accuracy for normal, obstructive, central and mixed apnea was obtained at 95.3%, 97.92%, 99.60%, and 97.29%, respectively, and the accuracy For detection of normal, mild, moderate and severe apnea was obtained 96%, 94%, 94%, 96% respectively. According to the results, the proposed system can correctly classify the types of sleep apnea and its severity.

The proposed system, which has high performance capability in addition t o increasing the physician speed and accuracy in the diagnosis of apnea can be used in home systems and the areas where healthcare facilities are not sufficient.

Co-occurring central sleep apnea (CSA) and obstructive sleep apnea (OSA) are a developing apprehension because many patients referred to sleep studies have co-morbidities such as cardiovascular and/or neurological disorders which increase the possibility of central and obstructive episodes. Here, we report a patient without excessive daytime sleepiness and a combination of CSA and OSA.

We present a 16-year-old boy with a history of snoring, poor quality of sleep, nightmare, sleep walking, and sleep talking since he was two-years old. His STOP-Bang score was 7. Standard attended polysomnography (PSG) with audio-video monitoring was performed. The PSG results contained Apnea Hypopnea Index (AHI): 30.2 (number of OSAs was 50 and number of CSAs was 49 during sleep). Then, a titration study was performed and continuous positive airway pressure (CPAP) setting as low as eight cmH2O was effective in eliminating obstructive events, but there was emerging CSAs in favour of Treatment Emergent CSA (TCSA).

This case represents a non-sleepy phenotype of OSA in combination with many CSAs in PSG. We suggest that further studies be performed on the association between the concomitant presence of CSA and OSA among nonsleepy patients with OSA.

 Severe obstructive sleep apnea (OSA), defined by apnea-hypopnea index (AHI) as more than 30 events per hour, was previously related to more comorbidity. However, limited studies separated the patients with AHI > 100 from those with a less severe manifestation of the disease.

 The current study aimed at describing the characteristics of this subgroup and comparing them with less severe conditions.

 A retrospective analysis was conducted on 114 patients with OSA. Nocturnal polysomnography was used to diagnose severe OSA. Patients were categorized into two groups: (1) 60 < AHI < 100 (very severe OSA), (2) AHI ≥ 100 (extreme OSA). Demographic, medical history, and polysomnographic variables were evaluated and compared between the two groups.

 Extreme OSA was diagnosed in 19 patients, the mean body mass index (BMI) was significantly higher in this group (39.26 ± 5.93 vs. 35.68 ± 6.45 kg/m2, P = 0.025). They also had lower minimal O2 saturation (65.68 ± 10.16 vs. 74.10 ± 8.74, P = 0.003) and more time with < 90% O2 saturation (T < 90%) (81.78 ± 22.57 vs. 58.87 ± 33.14, P = 0.01). OHS prevalence was significantly higher in the group with extreme OSA (P = 0.04). The most frequent comorbidity was hypertension, with an incidence of 60.5%, for the extreme group, although there was no significant difference between the two groups in terms of clinical associations.

 The current study results suggested that greater BMI and lower minimal O2 saturation, as well as increased T < 90%, were associated with extreme OSA, although no differences were observed in the associated diseases between the compared groups.

Simple snoring affects millions of people and their partners in the world and it indicates increased upper airways resistance and pharyngeal collapsibility. Snoring, particularly loud and habitual, may indicate obstructive sleep apnea (OSA).

Case Report:

 The presenting patient was a middle-aged man with chief compliant of snoring, who was diagnosed with simple snoring after undergoing an overnight polysomnography (PSG). By using a simple oral appliance that retracted the tongue and improved airway patency, snoring improved completely.

Simple snoring is a common condition and after excluding OSA, particularly in suspected patients, it can be managed by some instructions and interventions such as using available oral appliances.

Obstructive sleep apnea (OSA) is an important health problem, which is commonly under-diagnosed especially in workplace settings. We tried to obtain a model with more objective variables due to the greater reliability in occupational settings.

A total of 374 suspected patients with OSA who underwent their first polysomnography (PSG) at Baharloo Sleep Clinic in Tehran, Iran, were enrolled in the study. Before PSG, all patients completed a questionnaire including demographic characteristics. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured for all participants. Furthermore, a blood sample was collected for measuring fasting blood sugar (FBS) and hemoglobin A1c (HbA1c). All the patients underwent full PSG. Respiratory Disturbance Index (RDI) was calculated and recorded for all patients. Different multiple adjusted logistic regression models were constructed to find the best model for prediction of OSA.

A total of 271 (72.5%) participants were men. The mean age and body mass index (BMI) were 48.58 ± 13.04 years and 30.4 ± 5.0 kg/m2 , respectively. The prevalence of RDI ≥ 15 was 78.87% (n = 295). Using regression analysis, several models were obtained, where the best one yielded sensitivity and specificity of 77.29% and 67.09%, respectively. Area under the curve (AUC) of this model was 82%. The variables of this model included SBP, age, neck circumference-height ratio (NHR), FBS, BMI, and gender (PAN apnea index) with a cutoff point ≥ 8 for high-risk individuals.

In this study, we considered only objective parameters to predict OSA which enhances reliability for diagnosis especially in occupational settings.

Various types of abnormal movements such as pain, cramp, jerk, creeping, or itching may occur during sleep, many of which often involve the legs. In this study, we reported a case of periodic limb movement in sleep (PLMS) in the setting of a neurological disease.

We report a patient with involuntary left leg movements during sleep. The patient developed this prob-lem after an ischemic brain stroke that involved right temporal and basal ganglia. The patient underwent an overnight polysomnography (PSG) for the diagnosis of PLMS.

Although we do not know the exact pathogenesis of PLMS, it has been proposed that the brain lesions might cause PLMS. The present case provided evidence to support that brain lesions could be considered as a cause of unilateral PLMS.

Individuals with psychophysiological insomnia (Psych‑Insomnia) would show raised cortical arousal through their initiating sleep. Frequent changes in the alpha activity can be indicative of visual cortical activation, even without visual stimulation or retinal input. Therefore, we aimed to investigate alpha‑wave characteristics in Psych‑Insomnia before and after sleep onset. In a case– control study, 11 individuals with Psych‑Insomnia (age: 44.00 ± 13.27) and 11 age‑, sex‑, and body mass index‑matched healthy individuals (age: 41.64 ± 15.89) were recruited for this study. An overnight polysomnography monitoring was performed. Alpha characteristics were calculated from wake before sleep onsets (WBSOs), wake after sleep onset, rapid eye movement, and nonrapid eye movement in the both groups. They include the alpha power and alpha frequency and their variability in the central region. In the WBSO, alpha activity and variability were higher in the Psych‑Insomnia individuals compared to healthy individuals. In both groups, alpha frequency variability was observed at approximately 1 Hz. Alpha‑wave synchronization in Psych‑Insomnia individuals was higher than the group with normal sleep. Individuals with Psych‑Insomnia have a lot of imagination in the wake before sleep, which can be caused by stress, everyday concerns, and daily concerns.