Objective To analyze the causes of missed diagnosis of sleep apnea hypopnea syndrome ( SAHS) . Methods 42 missed diagnosed cases with SAHS from May 2009 to May 2011 were retrospectively analyzed and related literatures were reviewed. Results The SAHS patients often visited the doctors for complications of SAHS such as hypertension, diabetes mellitus, metabolic syndrome, etc. Clinical misdiagnosis rate was very high. Lack of specific symptoms during the day, complicated morbidities, and insufficient knowledge of SAHS led to the high misdiagnosis rate and the poor treatment effect of patients with SAHS. Conclusion Strengthening the educational propaganda of SAHS, detail medical history collection, and polysomnography monitoring ( PSG) as early as possible can help diagnose SAHS more accurately and reduce missed diagnosis.
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 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.
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.
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.
【摘要】 目的 了解和分析玉树地震伤员急性应激期睡眠问题。 方法 2010年4月，对90例玉树地震伤员的急性应激反应采用创伤后应激障碍症状清单平民版(PCL-C)17项量表进行筛查评估，并应用SPSS 17.0软件进行统计学分析。 结果 在PCL-C 17个条目中，提示睡眠障碍的条目2和条目13发生率分别为61.10%、63.30%，分别排列第5位、第3位，其得分分别与PCL-C总得分、闪回症状得分、回避症状得分及高警觉性症状得分均呈正相关(P值均lt;0.01)。 结论 睡眠障碍是地震伤员急性应激反应中的常见问题，需高度重视，并进行积极有效的处理。【Abstract】 Objective To learn and analyze the sleep disorders in acute stress of the wounded persons in Yushu earthquake. Methods The acute stress reaction of 90 wounded persons in Yushu earthquake were screened with post-traumatic stress disorder (PTSD) Checklist-Civilian (PCL-C) version-17 in April 2010. Sleep disorders were statistically analyzed with SPSS 17.0. Results In the 17 items of PCL-C, the incidences of the second and the thirteenth item which were related to sleep disorders were respectively 61.10% ranking at the fifth and 63.30% ranking at the third. Both scores of these two items had significant positive correlation with the total score of PCL-C and the scores of the flashback symptom, the avoidance symptom and the heightened alertness symptom (Plt;0.01). Conclusion Sleep disorder is a common problem in acute stress reaction of wounded persons in earthquakes, which needs high attention to be treated positively.
Sleep disorder is related to many comorbidities, such as diabetes, obesity, cardiovascular diseases, and hypertension. Because of its increasing prevalence rate, it has become a global problem that seriously threatens people’s health. Various forms of sleep disorder can cause increased insulin resistance and/or decreased sensitivity, thus affecting the occurrence, development and prognosis of diabetes. However, sleep health has not been paid attention to in recent years. Therefore, this article summarizes the findings of the correlation between sleep disorder and diabetes mellitus in recent years, by elaborating the relationship between various types of sleep disorder (including sleep apnea syndrome) and diabetes mellitus, as well as their mechanisms and intervention measures, in order to enhance the attention of clinical workers to sleep health, and to provide basis for reducing the risk of diabetes.
In order to guide diagnosis and treatment in children with sleep disordered breathing aged 1 to 23 months, the European Respiratory Society(ERS) summarized the evidence and released the European Respiratory Society statement based on clinical experience in 2016. This article aims to interpret the ERS statement. Children with apparent upper airway obstruction during wakefulness and those with SDB symptoms and complex conditions requires treatment. Adenotonsillectomy and continuous positive airway pressure are the most frequently used treatment measures along with interventions targeting specific conditions. Obstructive SDB in children aged 1 to 23 months is a multifactorial disorder that requires objective assessment and treatment of all underlying abnormalities.
Objective To evaluate the changes of right ventricular function in patients with obstructive sleep apnea hypopnea syndrome (OSAHS) before and after continuous positive airway pressure (CPAP) treatment by two-dimensional speckle tracking imaging (2D-STI). Methods Fifty patients with moderate and severe OSAHS were selected for CPAP treatment, and another 40 healthy volunteers were selected as a control group. 2D-STI and traditional echocardiography were conducted in the study group before treatment, after 3 months of continuous treatment and after 6 months of continuous treatment and in the control group. Results The differences between the control subjects and the OSAHS patients were statistically significant in right ventricular global longitudinal strain (RVGLS), right ventricular free lateral wall longitudinal strain (RVLLS), apical segment of the right ventricular free wall longitudinal strain (Apical RV-SL), basal segment of the right ventricular free wall longitudinal strain (Basal RV-SL), and media segment of the right ventricular free wall longitudinal strain (Media RV-SL) (all P<0.05). RVGLS, RVLLS and Apical RV-SL were significantly improved after 3 months of CPAP treatment (all P<0.05). Basal RV-SL was significantly improved after 6 months of CPAP treatment (P<0.05). Conclusions The right ventricular function of patients with OSAHS is abnormal. CPAP treatment can improve the right ventricular function of OSAHS patients. 2D-STI can accurately assess the changes of right ventricular function.