The method of using deep learning technology to realize automatic sleep staging needs a lot of data support, and its computational complexity is also high. In this paper, an automatic sleep staging method based on power spectral density (PSD) and random forest is proposed. Firstly, the PSDs of six characteristic waves (K complex wave, δ wave, θ wave, α wave, spindle wave, β wave) in electroencephalogram (EEG) signals were extracted as the classification features, and then five sleep states (W, N1, N2, N3, REM) were automatically classified by random forest classifier. The whole night sleep EEG data of healthy subjects in the Sleep-EDF database were used as experimental data. The effects of using different EEG signals (Fpz-Cz single channel, Pz-Oz single channel, Fpz-Cz + Pz-Oz dual channel), different classifiers (random forest, adaptive boost, gradient boost, Gaussian naïve Bayes, decision tree, K-nearest neighbor), and different training and test set divisions (2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, single subject) on the classification effect were compared. The experimental results showed that the effect was the best when the input was Pz-Oz single-channel EEG signal and the random forest classifier was used, no matter how the training set and test set were transformed, the classification accuracy was above 90.79%. The overall classification accuracy, macro average F1 value, and Kappa coefficient could reach 91.94%, 73.2% and 0.845 respectively at the highest, which proved that this method was effective and not susceptible to data volume, and had good stability. Compared with the existing research, our method is more accurate and simpler, and is suitable for automation.
Huntington’s disease (HD) is characterized by chorea, cognitive impairment, and psychiatric symptoms. Sleep and circadian rhythm disturbances are one of the important symptoms of HD that have been gradually recognized in recent years, and have a serious impact on the quality of life of patients and their caregivers. The clinical manifestations of sleep and circadian rhythm disturbances in HD are different from those of other neurodegenerative diseases. The exact pathological mechanisms of these disturbances remain unclear and there is no specific treatment. This article reviews the current progress in the study of sleep and circadian rhythm disturbances in HD, including its pathological mechanisms, clinical manifestations, assessment methods, correlation with cognitive impairment and psychiatric symptoms, treatment and management.
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.
ObjectivesTo review the value of sleep deprivation EEG methodology in the diagnosis of epilepsy.MethodsSuch databases as Pubmed, MEDLINE, The Cochrane Library, Wanfang, VIP and CNKI Data are searched electronically and comprehensively for literature on the diagnosis of epilepsy by sleep deprivation EEG from inception to January 2021. Two reviewers independently screened literature according to the inclusion and exclusion criteria, extracted data, and assessed methodological quality. Then, meta-analysis was performed using Stata software.ResultsA total of 14studies involving 1221 patients were included in total. The results of meta-analysis showed that: Duration of sleep deprivation and effect value of positive rate [ r=0.670, 95%CI (0.664, 0.696), P<0.001 ], duration of the awake period records and effect value of positive rate [ r=0.659, 95%CI (0.596, 0.722), P<0.001 ], duration of sleep period records and effect value of positive rate [ r=0.67, 95%CI(0.619, 0.721), P<0.001 ], with significant differences.ConclusionsThe duration of sleep deprivation, the awake period records, and the sleep period records of sleep deprivation EEG examination, sleep deprivation time between 16 h to 24 h, the awake recording time ≥30 min, and the sleep recording time ≥ 60 min (≤ 3 h) can obviously improve the positive rate of sleep deprivation EEG.
ObjectiveTo systematically review the risk factors associated with sleep disorders in ICU patients.MethodsWe searched The Cochrane Library, PubMed, EMbase, Web of Science, CNKI, Wanfang Data, VIP and CBM databases to collect cohort studies, case-control studies and cross-sectional studies on the risk factors associated with sleep disorders in ICU patients from inception to October, 2018. Two reviewers independently screened literature, extracted data and evaluated the bias risk of included studies. Then, meta-analysis was performed by using RevMan 5.3 software.ResultsA total of 9 articles were included, with a total of 1 068 patients, including 12 risk factors. The results of meta-analysis showed that the combined effect of equipment noise (OR=0.42, 95%CI 0.26 to 0.68, P=0.000 4), patients’ talk (OR=0.53, 95%CI 0.42 to 0.66, P<0.000 01), patients’ noise (OR=0.39, 95%CI 0.21 to 0.74, P=0.004), light (OR=0.29, 95%CI 0.18 to 0.45, P<0.000 01), night treatment (OR=0.36, 95%CI 0.26 to 0.50, P<0.000 01), diseases and drug effects (OR=0.17,95%CI 0.08 to 0.36, P<0.000 01), pain (OR=0.37, 95%CI 0.17 to 0.82, P=0.01), comfort changes (OR=0.34,95%CI 0.17 to 0.67,P=0.002), anxiety (OR=0.31,95%CI 0.12 to 0.78, P=0.01), visit time (OR=0.72, 95%CI 0.53 to 0.98, P=0.04), economic burden (OR=0.63, 95%CI 0.48 to 0.82, P=0.000 5) were statistically significant risk factors for sleep disorders in ICU patients.ConclusionCurrent evidence shows that the risk factors for sleep disorders in ICU patients are environmental factors (talking voices of nurses, patient noise, and light), treatment factors (night treatment), disease factors (disease itself and drug effects, pain,) and psychological factors (visiting time, economic burden). Due to the limited quality and quantity of included studies, more high quality studies are needed to verify the above conclusions.
Sleep-related breathing disorder (SRBD) is a sleep disease with high incidence and many complications. However, patients are often unaware of their sickness. Therefore, SRBD harms health seriously. At present, home SRBD monitoring equipment is a popular research topic to help people get aware of their health conditions. This article fully compares recent state-of-art research results about home SRBD monitors to clarify the advantages and limitations of various sensing techniques. Furthermore, the direction of future research and commercialization is pointed out. According to the system design, novel home SRBD monitors can be divided into two types: wearable and unconstrained. The two types of monitors have their own advantages and disadvantages. The wearable devices are simple and portable, but they are not comfortable and durable enough. Meanwhile, the unconstrained devices are more unobtrusive and comfortable, but the supporting algorithms are complex to develop. At present, researches are mainly focused on system design and performance evaluation, while high performance algorithm and large-scale clinical trial need further research. This article can help researchers understand state-of-art research progresses on SRBD monitoring quickly and comprehensively and inspire their research and innovation ideas. Additionally, this article also summarizes the existing commercial sleep respiratory monitors, so as to promote the commercialization of novel home SRBD monitors that are still under research.
ObjectiveTo provide the possibility to explain the relationship between genotype and phenotype, and to provide reference for the clinical treatment of Sleep-related hypermotor epilepsy (SHE). MethodsWe retrospectively analyzed the case data of the child (patient 1) diagnosed with SHE in the outpatient department of the Second Affiliated Hospital of Wenzhou Medical University in December 2017, and inquired about his family history and growth and development history. We learned that the father (patient 2) of the child had a history of epilepsy, and we also collected his medical history and growth and development history of patient 2. We carried out the basic physical examination for the two patients, and basic blood routine and blood biochemical indicators have also been done. In addition, electroencephalogram, Wechsler intelligence assessment and cranial magnetic resonance imaging were performed. After the diagnosis of patients 1 and 2, we treated them with antiepileptic drugs and make them long-term follow-up. What’more, we collected the peripheral blood of patient 1 and his father and mother, sequenced the gene, established phylogenetic tree for the mutation gene, and compared the homologous protein sequence to judge the conservation of the mutation. Moreover, in silico analysis was used to analyze the pathogenicity of the mutant gene. ResultsWe find a family with epilepsy, of whom patient 1 and his father are with epilepsy. Their clinical manifestations are atypical, and their seizures are all in sleep. After a long-term follow-up of two patients' drug treatments, it is found that patient 1 and patient 2 respond well to the drugs. Gene test shows that the mutations of DEPDC5 (c.484-1del c.484_485del) and KCNQ2 (c.1164A> T) are at the same site in both patient 1 and patient 2, and the mutation sites are first reported. What’more, the homologous protein alignment shows that the amino acids corresponding to the two mutant genes are highly conserved. ConclusionThis study mainly reports a family with sleep-related hypermotor epilepsy. Patients 1 and patient 2 have novel mutations of DEPDC5 and KCNQ2 genes. In the long-term follow-up of this study, it is found that the patients are effective the antiepileptic drugs.
Objective To investigate the effect of anti-seizure medications (ASMs) pregabalin (PGB) monotherapy on sleep structure and quality of patients with focal epilepsy. MethodsAdult patients whom newly diagnosed focal epilepsy were collected and treated with PGB monotherapy. The main outcome measures were the changes of polysomnography and video-electroencephalography (PSG-VEEG), Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI) and Epworth Sleepiness Scale (ESS) in epilepsy patients with PGB and baseline. Results PGB improved significantly sleep structural parameters, including increased total sleep time (P<0.001), decreased sleep latency (P<0.001), improved sleep efficiency (P<0.001), reduced wake time after sleep onset (P<0.001), increased sleep maintenance efficiency (P<0.001) and proportion of N3 sleep stage (P<0.001). In the group with poor sleep efficiency, 86.7% of patients achieved sleep efficiency>85% after PGB treatment. The difference was statistically significant (P<0.01). PGB reduced significantly PSQI score (P<0.001) and ISI score (P<0.001). No significant change in ESS score was observed (P>0.05). ConclusionsPGB could enhance slow-wave sleep (SWS), increase sleep quality and improve insomnia in patients with epilepsy without causing daytime sleepiness.
由于高血压的高患病率与高致残致死率, 已经成为我国重点防治的心血管疾病和社会普遍关注的重大公共卫生问题之一。大量流行病学、临床和基础研究已证实睡眠呼吸暂停低通气综合征( sleep apnea-hypopnea syndrome, SAHS) 与高血压发病和疗效关系密切[ 1-8 ] , 是高血压发生的主要病因之一, 由此“睡眠呼吸暂停相关性高血压”一词便应运而生[ 9-1 0] , 它是指由SAHS 引发和加重的高血压。本期刊载的“阻塞性睡眠呼吸暂停相关性高血压临床诊断和治疗专家共识”( 以下简称共识) , 为睡眠呼吸暂停相关性高血压的诊治提供了规范性的指导意见, 对推动我国该领域的防治水平有重要作用。我们期望“共识”能为读者认识和防治睡眠呼吸暂停相关性高血压提供必要的指导和帮助, 使我国为数众多的睡眠呼吸暂停相关性高血压患者得到规范的诊治。
ObjectiveTo assess the polysomnographic characteristics of insomnia patients with comorbid obstructive sleep apnea-hypopnea syndrome (OSAHS). MethodsWe performed a comparative analysis on the polysomnographic features among patients with pure insomnia (n=80), patients with pure OSAHS (n=80), and patients with insomnia and OSAHS (n=50) between August and December 2013. ResultsCompared with OSAHS group, patients with insomnia and comorbid OSAHS had a higher percentage of female, older age, lower body mass index, shorter total sleep time during the night, longer sleep latent period and wake after sleep onset (WASO), lower sleep efficacy, lower arousal index and apnea hypoventilation index (AHI), higher average and the lowest oxygen saturation of blood, lower Epworth Sleepiness Scale scores and sleep perception (P < 0.05). Compared with the insomnia group, patients with insomnia and comorbid OSAHS had a lower percentage of female, shorter total sleep time, lower sleep efficacy, longer WASO and higher AHI (P < 0.05). ConclusionPatients with insomnia and comorbid OSAHS have all the characteristics of insomnia and OSAHS patients:nocturnal hypoxia, sleep fragmentation, broken sleep continuity, decreased sleep efficiency, damaged perception of sleep time and sleep perception.