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.
Sleep stage classification is essential for clinical disease diagnosis and sleep quality assessment. Most of the existing methods for sleep stage classification are based on single-channel or single-modal signal, and extract features using a single-branch, deep convolutional network, which not only hinders the capture of the diversity features related to sleep and increase the computational cost, but also has a certain impact on the accuracy of sleep stage classification. To solve this problem, this paper proposes an end-to-end multi-modal physiological time-frequency feature extraction network (MTFF-Net) for accurate sleep stage classification. First, multi-modal physiological signal containing electroencephalogram (EEG), electrocardiogram (ECG), electrooculogram (EOG) and electromyogram (EMG) are converted into two-dimensional time-frequency images containing time-frequency features by using short time Fourier transform (STFT). Then, the time-frequency feature extraction network combining multi-scale EEG compact convolution network (Ms-EEGNet) and bidirectional gated recurrent units (Bi-GRU) network is used to obtain multi-scale spectral features related to sleep feature waveforms and time series features related to sleep stage transition. According to the American Academy of Sleep Medicine (AASM) EEG sleep stage classification criterion, the model achieved 84.3% accuracy in the five-classification task on the third subgroup of the Institute of Systems and Robotics of the University of Coimbra Sleep Dataset (ISRUC-S3), with 83.1% macro F1 score value and 79.8% Cohen’s Kappa coefficient. The experimental results show that the proposed model achieves higher classification accuracy and promotes the application of deep learning algorithms in assisting clinical decision-making.
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 na?ve 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.
ObjectiveThe aim of this study was to investigate the changes in peripheral blood metabolites and transcriptomes in patients with obstructive sleep apnea (OSA) and to assess their diagnostic value as biomarkers. MethodsIn this study, we utilized liquid chromatography-tandem mass spectrometry (LC-MS/MS) lipid-targeted metabolomics to compare the metabolic profiles of 30 OSA patients with those of 30 healthy controls, identifying differential lipid metabolites. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, we determined that the glycerolipid metabolism pathway was significantly different. Furthermore, we conducted transcriptome analysis on peripheral blood mononuclear cells (PBMCs) from six OSA patients and six healthy controls to evaluate the expression of molecules related to the pathway. ResultsA total of 168 differential lipid metabolites were identified, with significant differences in the glycerolipid metabolism pathway between OSA patients and healthy controls. Transcriptome analysis revealed that glycerolipid metabolism-related molecules GPAT, AGPAT, and LPIN were under expressed in OSA patient PBMCs, suggesting that the glycerolipid metabolism pathway is suppressed in OSA patients. Additionally, diagnostic value analysis showed that GPAT and AGPAT had high AUC values, indicating their potential as biomarkers for OSA. ConclusionThe suppression of the glycerolipid metabolism pathway is closely related to the development of OSA, and the under expression of key genes in this pathway, such as GPAT, AGPAT, and LPIN, may be involved in the pathophysiological process of OSA. These findings not only provide a new perspective for understanding the pathogenesis of OSA but also offer new scientific evidence for the treatment of OSA from the perspective of glycerolipid metabolism regulation.
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.
