Objective To evaluate the correlation of vascular endothelial growth factor (VEGF) and vascular endothelial cadherin (VE-Cadherin) in serum with the severity of obstructive sleep apnea (OSA) and explore their clinical value in OSA. Methods A total of 90 patients with OSA admitted to the Sleep Monitoring Center of the Affiliated Hospital of Xuzhou Medical University from April 2023 to June 2024 were prospectively selected. Based on the apnea-hypopnea index (AHI), the patients were divided into a mild group (5 - 15 times/hour, n=30), a moderate group (>15 - 30 times/hour, n=28), and a severe group (>30 times/hour, n=32). Thirty healthy individuals who underwent physical examinations during the same period were included as a control group. The levels of serum VEGF and soluble VE-Cadherin (sVE) in all subjects were detected by enzyme-linked immunosorbent assay. The differences in serum VEGF and sVE levels among the groups were compared, and the correlations between serum VEGF and sVE levels and sleep parameters were explored. The moderate and severe OSA patients were given 3 months of continuous positive airway pressure (CPAP) treatment, and the changes in sleep parameters and serum VEGF and sVE levels before and after treatment were compared. Results The levels of serum VEGF and sVE in the OSA patients increased with the severity of the disease; the levels of serum VEGF and sVE in the moderate and severe OSA groups were significantly higher than those in the healthy control group and the mild OSA group (P<0.05). The levels of serum VEGF and sVE in the severe OSA group were significantly higher than those in the moderate OSA group (P<0.05). There was no significant difference in the expression levels of serum VEGF or sVE between the mild OSA group and the healthy control group (P>0.05). The sensitivity and specificity of serum VEGF in diagnosing OSA were 65.6% and 93.3%, respectively, with an area under curve (AUC) value of 0.845. The sensitivity and specificity of serum VE-Cadherin in diagnosing OSA were 64.4% and 96.7%, respectively, with an AUC value of 0.835. After 3 months of CPAP treatment, AHI, longest apnea time, serum VEGF and sVE levels in the moderate and severe OSA groups decreased significantly, mean arterial oxygen saturation and lowest arterial oxygen saturation increased significantly (P<0.05). Conclusions The levels of VEGF and VE-Cadherin in serum of OSA patients are significantly elevated and positively correlated with the severity of OSA. Monitoring the changes in the levels of VEGF and VE-Cadherin in serum of OSA patients is helpful for evaluating the therapeutic effect of CPAP.
阻塞性睡眠呼吸暫停綜合征( obstructive sleep apnea syndrome, OSAS) 是一種常見的睡眠呼吸障礙性疾病, 在成人發生率為2% ～4% , 由于睡眠中反復發生上氣道部分或完全阻塞而表現為夜間間斷低氧和高碳酸血癥、反復覺醒、睡眠結構紊亂, 臨床上常引起心、腦、腎等多器官損害。越來越多的證據表明, OSAS并發高血壓、冠心病、肺動脈高壓、心力衰竭、中風的危險性增高, 是心腦血管疾病的獨立危險因素。未經治療的重度OSAS 患者5 年病死率高達11% ～13% , 而心腦血管并發癥是主要死因。如果及時給予有效治療, 在一定程度上可以預防并發癥的發生, 甚至逆轉心腦血管并發癥的轉歸。
The existing automatic sleep staging algorithms have the problems of too many model parameters and long training time, which in turn results in poor sleep staging efficiency. Using a single channel electroencephalogram (EEG) signal, this paper proposed an automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning (TL-SDResNet). Firstly, a total of 30 single-channel (Fpz-Cz) EEG signals from 16 individuals were selected, and after preserving the effective sleep segments, the raw EEG signals were pre-processed using Butterworth filter and continuous wavelet transform to obtain two-dimensional images containing its time-frequency joint features as the input data for the staging model. Then, a ResNet50 pre-trained model trained on a publicly available dataset, the sleep database extension stored in European data format (Sleep-EDFx) was constructed, using a stochastic depth strategy and modifying the output layer to optimize the model structure. Finally, transfer learning was applied to the human sleep process throughout the night. The algorithm in this paper achieved a model staging accuracy of 87.95% after conducting several experiments. Experiments show that TL-SDResNet50 can accomplish fast training of a small amount of EEG data, and the overall effect is better than other staging algorithms and classical algorithms in recent years, which has certain practical value.
ObjectiveTo analyze the the characteristics of pulse oximetry (SpO2) curve changes in patients with obstructive sleep apnea (OSA), hypoxic parameters and to explore the difference and connection between obstructive apnea (OA) events and hypopnea (Hyp) events, evaluate the impact of different types of obstructive respiratory events on hypoxia, and provide a theoretical basis for exploration of hypoxic differences in each type of respiratory events and construction of prediction models for respiratory event types in the future. MethodsSixty patients with OSA diagnosed by polysomnography (PSG) were selected for retrospective analysis, and all respiratory events with oxygen drop in the recorded data overnight were divided into OA group (5972) according to the type of events and Hyp group (4110), recorded and scored events were exported from the PSG software as comma-separated variable (.csv) files, which were then imported and analyzed using the in-house built Matlab software. Propensity score matching was performed on the duration of respiratory events and whether they were accompanied by arousal in the two groups, and minimum oxygen saturation of events (e-minSpO2), the depth of desaturation (ΔSpO2), the duration of desaturation and resaturation (DSpO2), the duration of desaturation (d.DSpO2), duration of resaturation (r.DSpO2), duration of SpO2<90% (T90), duration of SpO2<90% during desaturation (d.T90), duration of SpO2<90% during resaturation (r.T90), area under the curve of SpO2<90% (ST90), area under the curve of SpO2<90% during desaturation (d.ST90), area under the curve of SpO2<90% during resaturation (r.ST90), oxygen desaturation rate (ODR) and oxygen resaturation rate (ORR), a total of 13 hypoxic parameters differences. ResultsVarious hypoxic parameters showed that more severe SpO2 desaturation in severe OSA patients, compared with mild and moderate OSA patients (P<0.05); There were statistically significant differences in the respiratory events duration and whether accompanied by arousal between the Hyp group and OA group (P<0.05), and the respiratory events duration and whether accompanied by arousal were significantly correlated with most hypoxic parameters; After accounting for respiratory events duration and whether accompanied by arousal by propensity score matching, compared with the Hyp group, e-minSpO2 was significantly lower in the OA group, ΔSpO2, d.DSpO2, r.DSpO2, ODR, ORR, T90, d.T90, r.T90, ST90, d.ST90, r.ST90 were significantly increased (P<0.05). ConclusionsDue to pathophysiological differences, all hypoxic parameters suggest that OA events will result in a more severe desaturation than Hyp events. Clinical assessment of OSA severity should not equate OA with Hyp events, which may cause more damage to the organism, establishing a basis for applying nocturnal SpO2 to automatically identify the type of respiratory event.
Objective To investigate the risk factors of chronic obstructive pulmonary disease (COPD) combined with obstructive sleep apnea (OSA) and its relationship with apnea-hypopnea index (AHI). Methods Clinical data of 216 COPD patients with OSA were retrospectively chosen in the period from January 2016 to December 2019 in our hospital. All patients were divided into different groups according to with or without OSA and the clinical features of patients with and without OSA were compared. Multivariate analysis was used to analyze the influencing factors of COPD with OSA and the correlation between AHI and COPD with OSA was also evaluated. Results ① The age, body mass index (BMI), neck circumference, smoking index, forced expiratory volume in 1 second (FEV1), FEV1% predicted (FEV1pred), the ratio of FEV1 to the forced vital capacity of the lungs (FEV1/FVC), COPD assessment test (CAT) score, Epworth sleepiness scale (ESS) score, Charlson comorbidity index (CCI) score, sleep apnea clinical score (SACS) score and proportion of patients with essential hypertension in OSA group were significantly higher than non-OSA group (P<0.05). The course of disease and the proportion of severe COPD and GOLD grade 4 in OSA group were significantly less than non-OSA group (P<0.05). ② AHI was positively correlated with age, BMI, neck circumference, smoking index, FEV1%pred, FEV1%pred<50%, CAT score, ESS score, CCI score and SACS score (P<0.05); and negatively correlated with FEV1%pred<50% (P<0.05). ③ Multivariate analysis showed that BMI, FEV1%pred<50%, CAT score and ESS score were the independent factors of COPD patients with OSA (P<0.05). ④ The proportion of AHI<5 times/h in GOLD grade 4 was significantly higher than GOLD grade 1-3 (P<0.05). The proportion of AHI> 30 times/h in GOLD grade 4 was significantly lower than GOLD grade 1-3 (P<0.05). Conclusion The incidence of COPD with OSA was independently correlated with BMI, FEV1%pred, CAT score and ESS score; patients with severe COPD possess lower OSA risk.
Objective To analyze a possible association of -A930G and C242T polymorphism with cognitive dysfunction in obstructive sleep apnea (OSA) patients, and assess potential interactions of CYBA alleles in OSA patients with cognitive dysfunction. Methods A total of 157 OSA patients with cognitive dysfunction were recruited as an experimental group, and 526 matched OSA patients without cognitive dysfunction as an control group. The neurocognitive assessment, polysomnography, genetic analyses, NADHP oxidase (NOX) activity, determination of urinary 8-OH-dG were completed in all subjects. Results Frequencies of the -930G allele carriers were not significantly different between two groups (P>0.05). Frequencies of the TT/CT genotypes were significantly higher in the OSA patients without cognitive dysfunction (P<0.05). NOX activity was assessed and found to be increased in the OSA patients with cognitive dysfunction (P<0.01). NOX activity was significantly higher in whom the allelic T variant was absent (P<0.05). The level of urinary 8-OH-dG was higher in the OSA patients with cognitive dysfunction (P<0.05). The level of urinary 8-OH-dG was significantly higher in whom the allelic T variant was absent (P<0.05). Conclusion The p22phox C242T polymorphism may be involved in the development of oxidative stress reaction in OSA patients with cognitive dysfunction.
Polysomnography (PSG) monitoring is an important method for clinical diagnosis of diseases such as insomnia, apnea and so on. In order to solve the problem of time-consuming and energy-consuming sleep stage staging of sleep disorder patients using manual frame-by-frame visual judgment PSG, this study proposed a deep learning algorithm model combining convolutional neural networks (CNN) and bidirectional gate recurrent neural networks (Bi GRU). A dynamic sparse self-attention mechanism was designed to solve the problem that gated recurrent neural networks (GRU) is difficult to obtain accurate vector representation of long-distance information. This study collected 143 overnight PSG data of patients from Shanghai Mental Health Center with sleep disorders, which were combined with 153 overnight PSG data of patients from the open-source dataset, and selected 9 electrophysiological channel signals including 6 electroencephalogram (EEG) signal channels, 2 electrooculogram (EOG) signal channels and a single mandibular electromyogram (EMG) signal channel. These data were used for model training, testing and evaluation. After cross validation, the accuracy was (84.0±2.0)%, and Cohen's kappa value was 0.77±0.50. It showed better performance than the Cohen's kappa value of physician score of 0.75±0.11. The experimental results show that the algorithm model in this paper has a high staging effect in different populations and is widely applicable. It is of great significance to assist clinicians in rapid and large-scale PSG sleep automatic staging.
Aiming at the problem that the unbalanced distribution of data in sleep electroencephalogram(EEG) signals and poor comfort in the process of polysomnography information collection will reduce the model's classification ability, this paper proposed a sleep state recognition method using single-channel EEG signals (WKCNN-LSTM) based on one-dimensional width kernel convolutional neural networks(WKCNN) and long-short-term memory networks (LSTM). Firstly, the wavelet denoising and synthetic minority over-sampling technique-Tomek link (SMOTE-Tomek) algorithm were used to preprocess the original sleep EEG signals. Secondly, one-dimensional sleep EEG signals were used as the input of the model, and WKCNN was used to extract frequency-domain features and suppress high-frequency noise. Then, the LSTM layer was used to learn the time-domain features. Finally, normalized exponential function was used on the full connection layer to realize sleep state. The experimental results showed that the classification accuracy of the one-dimensional WKCNN-LSTM model was 91.80% in this paper, which was better than that of similar studies in recent years, and the model had good generalization ability. This study improved classification accuracy of single-channel sleep EEG signals that can be easily utilized in portable sleep monitoring devices.
The incidence of perioperative sleep disorders in patients with cervical spondylosis is high, which affects the physiological and psychological rehabilitation effect of patients after surgery. The expert consensus (preliminary draft) was prepared by summarizing expert experience and recommendations. After expert review and revision, the consensus was formed. The consensus was developed based on existing evidence-based medical evidence and expert clinical experience, which is scientific and practical and can provide a basis for clinical medical personnel to prevent and treat perioperative sleep disorders in patients with cervical spondylosis.