Objective To investigate the correlation between serum level of visfatin and obesity in patients with obstructive sleep apnea hypopnea syndrome ( OSAHS) . Methods Forty-seven patients with OSAHS and 20 healthy controls were recruited in this study. Polysomnography was performed in all subjects to detect apnea-hypopnea index ( AHI) . The serumlevels of cisfatin, C-reactive protein ( CRP) , TNF-α, and IL-6 were measured by enzyme linked immunosorbent assay. The body mass inex ( BMI) was calculated.The level of cisfatin was compared between the OSAHS patients with different severity and the controls, and its relationship with the levels of AHI, BMI, CRP, TNF-α, and IL-6 was analyzed. Results The serumlevel of visfatin in the OSAHS patients was higher significantly than that in the controls ( P lt;0. 01) and increased by the severity of OSAHS. There were positive correlations between the serum level of visfatin and AHI,BMI, CRP, TNF-α, IL-6 in the OSAHS patients ( P lt;0. 05) . Conclusion The expression of visfatin may play an important role in the pathogenesis of OSAHS.
ObjectiveTo analyze the risk factors for electrical status epilepticus during sleep (ESES) in patients with self-limited epilepsy with centrotemporal spikes (SeLECTs) and to construct a nomogram model. MethodsThis study selected 174 children with SeLECTs who visited the Third Affiliated Hospital of Zhengzhou University from March 2017 to March 2024 and had complete case data as the research subjects. According to the results of video electroencephalogram monitoring during the course of the disease, the children were divided into non-ESES group (88 cases) and ESES group (86 cases). Multivariate logistic regression analysis was used to identify the risk factors for the occurrence of ESES in SeLECTs patients. ResultsThe multifactor Logistic regression analysis demonstrated that the EEG discharges in bilateral cerebral areas,types of seizure, epileptic seizures after initial treatment were the independent risk factors for the occurrence of ESES in SeLECTs. ConclusionBilateral distribution of electroencephalogram discharges before treatment, emergence of new seizure forms, and epileptic seizures after initial treatment are risk factors for the ESES in SeLECTs patients. The nomogram model constructed based on the above risk factors has a high degree of accuracy.
Sleep stage classification is a necessary fundamental method for the diagnosis of sleep diseases, which has attracted extensive attention in recent years. Traditional methods for sleep stage classification, such as manual marking methods and machine learning algorithms, have the limitations of low efficiency and defective generalization. Recently, deep neural networks have shown improved results by the capability of learning complex pattern in the sleep data. However, these models ignore the intra-temporal sequential information and the correlation among all channels in each segment of the sleep data. To solve these problems, a hybrid attention temporal sequential network model is proposed in this paper, choosing recurrent neural network to replace traditional convolutional neural network, and extracting temporal features of polysomnography from the perspective of time. Furthermore, intra-temporal attention mechanism and channel attention mechanism are adopted to achieve the fusion of the intra-temporal representation and the fusion of channel-correlated representation. And then, based on recurrent neural network and inter-temporal attention mechanism, this model further realized the fusion of inter-temporal contextual representation. Finally, the end-to-end automatic sleep stage classification is accomplished according to the above hybrid representation. This paper evaluates the proposed model based on two public benchmark sleep datasets downloaded from open-source website, which include a number of polysomnography. Experimental results show that the proposed model could achieve better performance compared with ten state-of-the-art baselines. The overall accuracy of sleep stage classification could reach 0.801, 0.801 and 0.717, respectively. Meanwhile, the macro average F1-scores of the proposed model could reach 0.752, 0.728 and 0.700. All experimental results could demonstrate the effectiveness of the proposed model.
ObjectiveTo Affiliated systematically review the efficacy of continuous positive airway pressure (CPAP) for resistant hypertension (RH) patients with obstructive sleep apnea (OSA). MethodsWe electronically searched databases including PubMed, EMbase, The Cochrane Library (Issue 10, 2015), CBM, CNKI and WanFang Data from inception to March 2016, to collect randomized controlled trials (RCTs) about CPAP for RH patients with OSA. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, meta-analysis was performed using RevMan 5.3 software. ResultsA total of 5 RCTs involving 395 patients were included. The results of meta-analysis showed that: After 3 months of follow-up, compared with the antihypertensive drug therapy alone, CPAP plus antihypertensive drug therapy could significantly reduce the 24 h diastolic blood pressure (DBP), day DBP, night DBP, 24 h diastolic blood pressure (SBP) and night SBP of RH patients with OSA (MD=-4.79, 95%CI -7.39 to -2.18, P=0.000 3; MD=-2.94, 95%CI -4.99 to -0.89, P=0.005; MD=-3.19, 95%CI -5.84 to -0.55, P=0.02; MD=-4.36, 95%CI -7.38 to -1.33, P=0.005; MD=-4.90, 95%CI -8.72, -1.08, P=0.01), but there was no significant difference between the two groups in day SBP. After 6 months of follow-up, compared with the antihypertensive drug therapy alone, CPAP plus antihypertensive drug therapy could significantly reduce the 24 h DBP, day DBP of RH patients with OSA (MD=-4.89, 95%CI -6.76 to -3.02, P<0.000 01; MD=-5.01, 95%CI -9.58 to -0.45, P=0.03), but there were no significant differences between the two groups in night DBP, 24 h SBP, day SBP, and night SBP. ConclusionCurrent evidence suggests that CPAP on the basis of antihypertensive drug therapy could effectively reduce the DBP and SBP of RH patients with OSA at short-term follow-up, but the long-term effect on SBP is not obvious. Due to limited quality and quantity of the included studies, the above conclusions need to be verified by more high quality studies.
Sleep electroencephalogram (EEG) is an important index in diagnosing sleep disorders and related diseases. Manual sleep staging is time-consuming and often influenced by subjective factors. Existing automatic sleep staging methods have high complexity and a low accuracy rate. A sleep staging method based on support vector machines (SVM) and feature selection using single channel EEG single is proposed in this paper. Thirty-eight features were extracted from the single channel EEG signal. Then based on the feature selection method F-Score's definition, it was extended to multiclass with an added eliminate factor in order to find proper features, which were used as SVM classifier inputs. The eliminate factor was adopted to reduce the negative interaction of features to the result. Research on the F-Score with an added eliminate factor was further accomplished with the data from a standard open source database and the results were compared with none feature selection and standard F-Score feature selection. The results showed that the present method could effectively improve the sleep staging accuracy and reduce the computation time.
The growing rate of public health problem for increasing number of people afflicted with poor sleep quality suggests the importance of developing portable sleep electroencephalogram (EEG) monitoring systems. The system could record the overnight EEG signal, classify sleep stages automatically, and grade the sleep quality. We in our laboratory collected the signals in an easy way using a single channel with three electrodes which were placed in frontal position in case of the electrode drop-off during sleep. For a test, either silver disc electrodes or disposable medical electrocardiographic electrodes were used. Sleep EEG recorded by the two types of electrodes was compared to each other so as to find out which type was more suitable. Two algorithms were used for sleep EEG processing, i.e. amplitude-integrated EEG (aEEG) algorithm and sample entropy algorithm. Results showed that both algorithms could perform sleep stage classification and quality evaluation automatically. The present designed system could be used to monitor overnight sleep and provide quantitative evaluation.
Objective To study the relationships among health-related quality of life( HRQL) ,social support, excessive daytime sleepiness ( EDS) and PSG parameters in patients with obstructive sleep apnea-hypopnea syndrome ( OSAHS) . Methods Eighty-five patients were recruited who were diagnosed as OSAHS by overnight polysomnography from August 2007 through November 2007 in West China Hospital.The Calgary sleep apnea quality of life index ( SAQLI) was used for HRQL, social support rating scale ( SSRS) was used for social support, and Epworth sleepiness scale( ESS) was used for EDS. The Pearson linear correlation and stepwise multiple regression analysis were used to analyze the correlation among SAQLI, SSRS, ESS, and PSG. Results The SAQLI was correlated with SSRS score ( r =0. 402, P lt;0. 01) ;ESS score ( r = - 0. 505, P lt;0. 01) ; apnea-hypopnea index ( AHI) ( r = - 0. 269, P lt; 0. 05) and lowest artery oxygen saturation ( LSaO2) ( r = 0. 226, P lt; 0. 05) . Stepwise multiple regression analysis determined two variables, the SSRS and ESS score, as independent factors for predicting the total score of SAQLI which accounted for 37. 3% of the total variance in the total score on SAQLI ( R2 = 0. 373, P lt; 0.001) .Conclusions The HRQL of patients with OSAHS was correlated with the SSRS score, ESS score and PSG parameters. The former two were the more important factors to affect the HRQL of patients with OSAHS.
Sleep apnea causes cardiac arrest, sleep rhythm disorders, nocturnal hypoxia and abnormal blood pressure fluctuations in patients, which eventually lead to nocturnal target organ damage in hypertensive patients. The incidence of obstructive sleep apnea hypopnea syndrome (OSAHS) is extremely high, which seriously affects the physical and mental health of patients. This study attempts to extract features associated with OSAHS from 24-hour ambulatory blood pressure data and identify OSAHS by machine learning models for the differential diagnosis of this disease. The study data were obtained from ambulatory blood pressure examination data of 339 patients collected in outpatient clinics of the Chinese PLA General Hospital from December 2018 to December 2019, including 115 patients with OSAHS diagnosed by polysomnography (PSG) and 224 patients with non-OSAHS. Based on the characteristics of clinical changes of blood pressure in OSAHS patients, feature extraction rules were defined and algorithms were developed to extract features, while logistic regression and lightGBM models were then used to classify and predict the disease. The results showed that the identification accuracy of the lightGBM model trained in this study was 80.0%, precision was 82.9%, recall was 72.5%, and the area under the working characteristic curve (AUC) of the subjects was 0.906. The defined ambulatory blood pressure features could be effectively used for identifying OSAHS. This study provides a new idea and method for OSAHS screening.
Objectives To explore the application effect of orthopedic psychological sleep management mode based on enhanced recovery after surgery (ERAS) in orthopedic patients. Methods A non-synchronous clinical controlled study was conducted. The intervention group enrolled 118 orthopedic patients who admitted to our hospital between April and June 2017, and the control group enrolled 111 orthopedic patients who admitted to our hospital between January and March 2017. The control group used routine nursing measures during hospitalization, while the intervention group implemented an ERAS-based orthopedic psychological sleep management mode based on routine nursing measures, which included carrying out a new mode of multidisciplinary collaborative management, implementing the normative path of orthopedic psychological sleep management, and implementing the comprehensive psychological sleep management. The mood, sleep quality and satisfaction of the two groups within 24 hours after admission and before discharge were compared. Results Before the intervention, there was no statistically significant difference in general data, mood or sleep quality between the two groups (P>0.05). After the intervention, the median score (the lower and upper quartiles) of the Huaxi Emotional Index of the intervention group was 1 (0, 5), while the score of the control group was 2 (0, 6); the median score (the lower and upper quartiles) of the Pittsburgh Sleep Quality Index was 4 (3, 7) in the intervention group and 6 (4, 9) in the control group; the satisfaction score in the intervention group was better than that in the control group (96.47±2.72vs. 95.52±2.79); the differences between the two groups were statistically significant (P<0.05). Conclusions The ERAS-based orthopedic psychological sleep management mode is beneficial to improve the patients’ emotional disorder, sleep quality and satisfaction. It facilitates the patients’ accelerated recovery.
The peak period of cardiovascular disease (CVD) is around the time of awakening in the morning, which may be related to the surge of sympathetic activity at the end of nocturnal sleep. This paper chose 140 participants as study object, 70 of which had occurred CVD events while the rest hadn’t during a two-year follow-up period. A two-layer model was proposed to investigate whether hypnopompic heart rate variability (HRV) was informative to distinguish these two types of participants. In the proposed model, the extreme gradient boosting algorithm (XGBoost) was used to construct a classifier in the first layer. By evaluating the feature importance of the classifier, those features with larger importance were fed into the second layer to construct the final classifier. Three machine learning algorithms, i.e., XGBoost, random forest and support vector machine were employed and compared in the second layer to find out which one can achieve the highest performance. The results showed that, with the analysis of hypnopompic HRV, the XGBoost+XGBoost model achieved the best performance with an accuracy of 84.3%. Compared with conventional time-domain and frequency-domain features, those features derived from nonlinear dynamic analysis were more important to the model. Especially, modified permutation entropy at scale 1 and sample entropy at scale 3 were relatively important. This study might have significance for the prevention and diagnosis of CVD, as well as for the design of CVD-risk assessment system.