Insomnia disorder is a common clinical mental disorder.Currently, clinical subtyping of insomnia disorder relies primarily on symptomatic descriptions, lacking objective measures and subtyping-based treatment approaches. In recent years, increasing attention has been drawn to sleep electroencephalography (EEG) as a valuable tool for observing abnormal sleep architecture and continuity of insomnia disorder. Sleep EEG analysis holds the potential to elucidate the underlying biological mechanisms of insomnia disorder, facilitating data-driven subtyping and enhancing personalized therapeutic strategies.Five types of sleep EEG subtypes of insomnia disorder were systematically searched and summarized: classifications derived from objective sleep duration, power spectral characteristics, cyclic alternating pattern, spindle and microarousal.EEG characteristics of each subtype and clinical outcomes are discussed.This review aims to provide evidence-based insights for clinical subtyping and personalized treatment of insomnia disorder.

Paradoxical insomnia(Para-I),also known as pseudoinsomnia or sleep state misperception,is a condition in which the patient complains of severe insomnia but has no objective evidence of sleep disorder,and daytime functioning may be disrupted disproportionately to the degree of patient-reported sleep loss.Para-I is characterized by overestimation of sleep latency(SL)and underestimation of total sleep time(TST).Incorrect assessment of sleep quality hinders the diagnosis,evaluation of severity,and assessment of clinical efficacy of sleep disorders.The pathogenesis of Para-I remains unclear,but may be related to factors such as depression,anxiety,personality traits,social relationships and specific changes in brain structure and function.Studies on the polysomnography(PSG)of the patients with insomnia have found that changes in non-rapid eye movement(NREM)and rapid eye movement(REM)sleep may be related to the degree of subjective-objective sleep discrepancy.PSG is a valuable diagnostic tool for sleep disorders.It allows for the analysis of sleep structure and related physiological and behavioral changes by monitoring various parameters,including electroencephalogram(EEG),electromyogram(EMG),electrooculogram(EOG),oro-nasal airflow,thoracic and abdominal respiratory motions,oxygen saturation,electrocardiogram(ECG)and snoring.In recent years,studies have increasingly explored the sleep EEG and treatment of Para-I with PSG,resulting in significant progress.This article reviews the latest advances in the electroencephalographic characteristics and treatment of Para-I,providing new ideas for precise treatment.

Insomnia disorder is the most common sleep-wake disorder,and long-term insomnia has a serious negative impact on the physical and mental health of individuals.It is crucial for researchers and clinicians to select appropriate measurement tools as evaluative indicators for insomnia.There are some commonly used insomnia assessment scales in the world,including Pittsburgh Sleep Quality Index(PSQ1),Insomnia Severity Index(ISI),etc.These scales are widely used to assess insomnia symptoms and sleep quality,providing researchers and clinicians with reliable quantitative tools.In addition to conventional insomnia assessment scales,some scales evaluate sleep cognition,sleep hygiene,and sleep conditions of different groups of people.Domestic scholars are actively developing sleep assessment tools suitable for the Chinese population,which also include sleep assessment for special groups.In addition,some sleep assessment with traditional Chinese medicine characteristics have also been developed to meet the needs of integrated traditional Chinese and Western medicine treatment.During the process of scale development,researchers should clarify the purpose of scale,select appropriate psychometric methods,and emphasize the reliability and validity of the scale.Furthermore,it is important to develop scales that can differentiate subtypes of insomnia and enhance the diversity of insomnia-related measures.This article summarizes the current situation of development of Chinese versions of insomnia-related scales,and provides evaluation and future prospects for existing scales.

Objective:To investigate the physical and mental experience, treatment compliance and use barriers of patients with insomnia in using digital cognitive behavioral therapy for insomnia (dCBT-I) in order to provide qualitative evidence for the development and application optimization of the dCBT-I technology paradigm.Methods:From July to November 2021, a semi-structured interview outline was used to conduct in-depth interviews with the dCBT-I users ( n=10) to record their original feelings about the use of dCBT-I. Interpretative phenomenology's text analysis was used to explore the participants' experience and cognition of dCBT-I. Results:Text analysis and key information calibration were carried out on the verbatim transcripts of semi-structured interview recordings, and three core themes were extracted, namely stickiness factor, use barrier and optimization direction, as well as eight sub-themes, namely professionalism, accessibility, benefit experience, difficulty in task execution, instruction generalization, difficulty in software operation, enrich treatment content and personalized guidance.Conclusion:The present study showed that participants were receptive to the dCBT-I intervention and would be benefited from it.However, dCBT-I still needs to be optimized and improved to reduce the operating difficulty and explore more appropriate timing of manual intervention.

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.
Humans ; Polysomnography ; China ; Sleep Stages ; Sleep ; Algorithms

Sleep staging is the basis for solving sleep problems. There's an upper limit for the classification accuracy of sleep staging models based on single-channel electroencephalogram (EEG) data and features. To address this problem, this paper proposed an automatic sleep staging model that mixes deep convolutional neural network (DCNN) and bi-directional long short-term memory network (BiLSTM). The model used DCNN to automatically learn the time-frequency domain features of EEG signals, and used BiLSTM to extract the temporal features between the data, fully exploiting the feature information contained in the data to improve the accuracy of automatic sleep staging. At the same time, noise reduction techniques and adaptive synthetic sampling were used to reduce the impact of signal noise and unbalanced data sets on model performance. In this paper, experiments were conducted using the Sleep-European Data Format Database Expanded and the Shanghai Mental Health Center Sleep Database, and achieved an overall accuracy rate of 86.9% and 88.9% respectively. When compared with the basic network model, all the experimental results outperformed the basic network, further demonstrating the validity of this paper's model, which can provide a reference for the construction of a home sleep monitoring system based on single-channel EEG signals.
China ; Sleep Stages ; Sleep ; Electroencephalography ; Databases, Factual

Objective To analyze the features of hypertensive retinopathy (HRP),and to evaluate the correlation of serum cystatin C (Cys-C) and retinopathy in patients with primary hypertension.Methods From July 2015 to October 2017,280 cases of primary hypertension in the Second People's Hospital of Ji'nan were recruited to receive fundus examination by funduscopy and eye-ground photography.Based on the findings,4 groups were established according to Chinese Ophthalmology (Third Edition) classification standard,normal,mild,moderate and malignant four levels.Clinical courses were monitored and Cys-C levels were determined.Results There were 204 cases of HRP,accounting for 72.86％.Compared with that of the normal fundus group (0.76 ±0.12)mg/L,the serum Cys-C level of the mild HRP group was (0.82 ± 0.19)mg/L,the difference was statistically significant (t =2.424,P ＜ 0.05).The serum Cys-C levels of the moderate HRP group and severe HRP group were (2.37 ± 0.13) mg/L and (3.24 ± 0.45) mg/L,respectively,the differences were statistically significant compared with that of the control group (t =80.917,42.153,all p ＜ 0.01).Conclusion The severity of HRP is positively correlated with Cys-C.

Objective·To compare the clinical features between different subtype bipolar patients with first mania episode, and to contribute to early identification of bipolar disorder. Methods·This study was based on the database named as National Bipolar Mania Pathway Survey (BIPAS). From November 2012 to January 2013, bipolar patients from 26 mental health facilities in China were enrolled in current study. The clinical features were compared between mania patients of different subtypes, including hypomania (groupⅠ), mania without psychotic symptoms (groupⅡ), mania with psychotic symptoms (group Ⅲ) and mixed state (group Ⅳ). Results·There was significant difference in the percentage of clinical symptoms between different subtype bipolar patients with first mania episode, especially the mania and anxiety related symptoms. Group Ⅰ, Ⅲ , Ⅳ were further compared with groupⅡ, which was considered as the typical bipolar disorder. The results showed that the mania related symptoms was significantly higher in group Ⅱ, but anxiety related symptoms was significantly higher in group Ⅰ, Ⅲ, Ⅳ. Moreover, Logistic regression analysis revealed that more eloquent or humor and unusually restless could be in favor of the diagnosis of hypomania; younger and mania or hypomania as first episode might be in favor of the diagnosis of mania with psychotic symptoms; older, national minorities and unusually restless could be in favor of the diagnosis of mixed state. Conclusion·The clinical features between different subtype bipolar patients with first mania episode are various, and analysis of the clinical features can contribute to early identification of bipolar disorder.

Multi-layer perceptron (MLP) neural network belongs to multi-layer feedforward neural network, and has the ability and characteristics of high intelligence. It can realize the complex nonlinear mapping by its own learning through the network. Bipolar disorder is a serious mental illness with high recurrence rate, high self-harm rate and high suicide rate. Most of the onset of the bipolar disorder starts with depressive episode, which can be easily misdiagnosed as unipolar depression and lead to a delayed treatment so as to influence the prognosis. The early identifica- tion of bipolar disorder is of great importance for patients with bipolar disorder. Due to the fact that the process of early identification of bipolar disorder is nonlinear, we in this paper discuss the MLP neural network application in early identification of bipolar disorder. This study covered 250 cases, including 143 cases with recurrent depression and 107 cases with bipolar disorder, and clinical features were statistically analyzed between the two groups. A total of 42 variables with significant differences were screened as the input variables of the neural network. Part of the samples were randomly selected as the learning sample, and the other as the test sample. By choosing different neu- ral network structures, all results of the identification of bipolar disorder were relatively good, which showed that MLP neural network could be used in the early identification of bipolar disorder.
Bipolar Disorder ; diagnosis ; Humans ; Neural Networks (Computer)

Objective To compare the cost-effectiveness and cost-utility of venlafaxine and mirtazapine in patients with treatment-resistant major depression (TRD).Methods One hundred and five patients with TRD were enrolled in this study and grouped into venlafaxine treatment (n=50) and mirtazapine treatment (n=55) based on the double-blind randomization scheme generated by computer.The treatment costs of antidepressants during 8 weeks were calculated,the rates of clinical response and remission were taken as treatment effectiveness,and the quality-adjusted life years (QALYs) as treatment utility.The descriptive analysis and nonparametric test were used to compare the cost-effectiveness and cost-utility of different groups.Results During 8 weeks,the treatment cost of antidepressant was ￥ 1 396.44 for venlafaxine and ￥ 1 206.90 mirtazapine,and the difference between two groups was ￥ 189.54.The cost-effectiveness ratios between venlafaxine and mirtazapine were very close (differed ￥ 0.06 for remission rate and ￥ 1.08 for response rate respectively).There was no significant difference for cost-utility ratios between two groups (physical functioning Z=-0.15,P＞0.05 ; mental health Z=-0.54,P＞0.05).Conclusion Both cost-effectiveness and cost-utility of venlafaxine in patients with TRD are close between venlafaxine and mirtazapine.