Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 428
Journal of Biomedical Engineering ; (6): 458-464, 2023.
Article in Chinese | WPRIM | ID: wpr-981563


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
Journal of Biomedical Engineering ; (6): 286-294, 2023.
Article in Chinese | WPRIM | ID: wpr-981541


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.

Humans , Sleep Stages , Algorithms , Sleep , Wavelet Analysis , Electroencephalography/methods , Machine Learning
Journal of Biomedical Engineering ; (6): 280-285, 2023.
Article in Chinese | WPRIM | ID: wpr-981540


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.

Humans , Random Forest , Bayes Theorem , Sleep Stages , Sleep , Electroencephalography/methods
Journal of Biomedical Engineering ; (6): 35-43, 2023.
Article in Chinese | WPRIM | ID: wpr-970671


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
Journal of Biomedical Engineering ; (6): 27-34, 2023.
Article in Chinese | WPRIM | ID: wpr-970670


In clinical, manually scoring by technician is the major method for sleep arousal detection. This method is time-consuming and subjective. This study aimed to achieve an end-to-end sleep-arousal events detection by constructing a convolutional neural network based on multi-scale convolutional layers and self-attention mechanism, and using 1 min single-channel electroencephalogram (EEG) signals as its input. Compared with the performance of the baseline model, the results of the proposed method showed that the mean area under the precision-recall curve and area under the receiver operating characteristic were both improved by 7%. Furthermore, we also compared the effects of single modality and multi-modality on the performance of the proposed model. The results revealed the power of single-channel EEG signals in automatic sleep arousal detection. However, the simple combination of multi-modality signals may be counterproductive to the improvement of model performance. Finally, we also explored the scalability of the proposed model and transferred the model into the automated sleep staging task in the same dataset. The average accuracy of 73% also suggested the power of the proposed method in task transferring. This study provides a potential solution for the development of portable sleep monitoring and paves a way for the automatic sleep data analysis using the transfer learning method.

Sleep , Sleep Stages , Arousal , Data Analysis , Electroencephalography
Rev. Méd. Clín. Condes ; 32(5): 527-534, sept.-oct. 2021. tab, ilus, graf
Article in Spanish | LILACS | ID: biblio-1526020


El desarrollo de la medicina del sueño ha experimentado notables avances por contribuciones provenientes tanto de las ciencias básicas como de los estudios clínicos, destacando una relación positiva entre la preservación de un sueño normal y un amplio espectro de beneficios en diferentes indicadores de salud individual y de la población. Un adecuado conocimiento de los postulados y mecanismos fisiológicos del sueño actualmente más aceptados a escala molecular, celular y sistémica, permiten desarrollar conceptos objetivos que otorgan mayor solidez a la evaluación del sueño. La etapificación del sueño, su arquitectura, variables de continuidad del mismo, asícomo el índice de microdespertares, entre otros, tienen una aplicación clínica directa: se pueden describir y utilizar rangos normales de parámetros polisomnográficos con sus características a lo largo de la edad, y variantes cronotípicas individuales. De este modo, se espera seguir avanzando tanto en el temprano y correcto diagnóstico como en una mejor toma de decisiones médicas.Muy probablemente, debido a la función integradora del sueño, es que este juega un rol tan crucial en la salud, avalado por un cuerpo de evidencia que muestra un importante impacto beneficioso de un sueño sano en la calidad de vida, morbilidad y la prevención primaria de enfermedades muy variadas

The development of sleep medicine has experienced notable advances due to contributions from both basic science and clinical studies, highlighting a positive relationship between the preservation of normal sleep and a wide spectrum of benefits in different indicators of individual and population health.An adequate knowledge of the currently more accepted physiological postulates and mechanisms of sleep, on a molecular, cellular and systemic scale, allows the development of objective concepts that give greater solidity to sleep assessment. Sleep staging, architecture, and continuity variables such as the microarousal index, among others, have direct clinical applications: normal ranges of polysomnographic parameters can be described and used with their characteristics throughout age and individual chronotype variants. In this way, it is further advances are expected both in early and correct diagnosis and in better medical treatments.Evidence supports the crucial role sleep plays in overall health. Most likely due to its integrative function, healthy sleep has an important beneficial impact on quality of life, morbidity and primary prevention of a wide variety of diseases

Humans , Quality of Life , Sleep/physiology , Sleep Stages , Circadian Rhythm , Sleep Quality , Sleep Duration
Journal of Biomedical Engineering ; (6): 241-248, 2021.
Article in Chinese | WPRIM | ID: wpr-879271


Sleep stage classification is a necessary fundamental method for the diagnosis of sleep diseases, which has attracted extensive attention in recent years. Traditional methods for sleep stage classification, such as manual marking methods and machine learning algorithms, have the limitations of low efficiency and defective generalization. Recently, deep neural networks have shown improved results by the capability of learning complex pattern in the sleep data. However, these models ignore the intra-temporal sequential information and the correlation among all channels in each segment of the sleep data. To solve these problems, a hybrid attention temporal sequential network model is proposed in this paper, choosing recurrent neural network to replace traditional convolutional neural network, and extracting temporal features of polysomnography from the perspective of time. Furthermore, intra-temporal attention mechanism and channel attention mechanism are adopted to achieve the fusion of the intra-temporal representation and the fusion of channel-correlated representation. And then, based on recurrent neural network and inter-temporal attention mechanism, this model further realized the fusion of inter-temporal contextual representation. Finally, the end-to-end automatic sleep stage classification is accomplished according to the above hybrid representation. This paper evaluates the proposed model based on two public benchmark sleep datasets downloaded from open-source website, which include a number of polysomnography. Experimental results show that the proposed model could achieve better performance compared with ten state-of-the-art baselines. The overall accuracy of sleep stage classification could reach 0.801, 0.801 and 0.717, respectively. Meanwhile, the macro average F1-scores of the proposed model could reach 0.752, 0.728 and 0.700. All experimental results could demonstrate the effectiveness of the proposed model.

Electroencephalography , Neural Networks, Computer , Polysomnography , Sleep , Sleep Stages
Chinese Journal of Otorhinolaryngology Head and Neck Surgery ; (12): 1256-1262, 2021.
Article in Chinese | WPRIM | ID: wpr-942610


Objective: To investigate theaccuracy of artificial intelligence sleep staging model in patients with habitual snoring and obstructive sleep apnea hypopnea syndrome (OSAHS) based on single-channel EEG collected from different locations of the head. Methods: The clinical data of 114 adults with habitual snoring and OSAHS who visited to the Sleep Medicine Center of Beijing Tongren Hospital from September 2020 to March of 2021 were analyzed retrospectively, including 93 males and 21 females, aging from 20 to 64 years old. Eighty-five adults with OSAHS and 29 subjects with habitual snoring were included. Sleep staging analysis was performed on the single lead EEG signals of different locations (FP2-M1, C4-M1, F3-M2, ROG-M1, O1-M2) using the deep learning segmentation model trained by previous data. Manual scoring results were used as the gold standard to analyze the consistency rate of results and the influence of different categories of disease. Results: EEG data in 124 747 30-second epochs were taken as the testing dataset. The model accuracy of distinguishing wake/sleep was 92.3%,92.6%,93.5%,89.2% and 83.0% respectively,based on EEG channel Fp2-M1, C4-M1, F3-M2, REOG-M1 or O1-M2. The mode accuracy of distinguishing wake/REM/NREM and wake/REM/N1-2/SWS , was 84.7% and 80.1% respectively based on channel Fp2-M1, which located in forehead skin. The AHI calculated based on total sleep time derived from the model and gold standard were 13.6[4.30,42.5] and 14.2[4.8,42.7], respectively (Z=-2.477, P=0.013), and the kappa coefficient was 0.977. Conclusions: The autonomic sleep staging via a deep neural network model based on forehead single-channel EEG (Fp2-M1) has a good consistency in the identification sleep stage in a population with habitual snoring and OSAHS with different categories. The AHI calculated based on this model has high consistency with manual scoring.

Adult , Female , Humans , Male , Middle Aged , Young Adult , Artificial Intelligence , Electroencephalography , Neural Networks, Computer , Retrospective Studies , Sleep , Sleep Stages
Rev. bras. neurol ; 56(2): 35-44, abr.-jun. 2020. ilus
Article in English | LILACS | ID: biblio-1102915


Dreaming is the result of the mental activity of rapid eye movement (REM) sleep stage, and less commonly of non-REM sleep. Dreams offer unique insights into the patients' brains, minds, and emotions. Based on neurophysiological and neuroimaging studies, the biological core of dreaming stands on some brain areas activated or inactivated. Dream abnormalities in neurological disorders include a reduction / cessation of dreaming, an increase in dream frequency, changes in dream contents and accompaniments, and the occurrence of dreamlike experiences (hallucinations) mainly during the wake-sleep/sleep-wake transitions. Dream changes can be associated with several neurological conditions, and the unfolding of biological knowledge about dream experiences can also have significance in clinical practice. Regarding the dream importance in clinical neurological management, the aim of this paper encompasses a summary of sleep stages, dreams neurobiology including brain areas involved in the dreams, memory, and dreams, besides Dreams in the aging people and neurodegenerative disorders.

Sonhar é o resultado da atividade mental do estágio do sono de movimento rápido dos olhos (REM) e, menos comumente, do sono não-REM. Os sonhos oferecem informações únicas sobre o cérebro, a mente e as emoções dos pacientes. Com base em estudos neurofisiológicos e de neuroimagem, o núcleo biológico do sonho está em algumas áreas do cérebro ativadas ou inativadas. As anormalidades do sonho nos distúrbios neurológicos incluem uma redução / cessação do sonho, um aumento na frequência do sonho, alterações nos conteúdos e acompanhamentos do sonho e a ocorrência de experiências semelhantes ao sonho (alucinações), principalmente durante as transições de vigília-sono / sono-vigília. As mudanças do sonho podem estar associadas a várias condições neurológicas, e o desenvolvimento do conhecimento biológico sobre as experiências do sonho também pode ter significado na prática clínica. Com relação à importância do sonho no manejo neurológico clínico, o objetivo deste artigo é resumir os estágios do sono, a neurobiologia dos sonhos, incluindo as áreas do cérebro envolvidas nos sonhos, a memória e os sonhos, além dos sonhos nos idosos e nos distúrbios neurodegenerativos.

Humans , Child , Adult , Sleep/physiology , Sleep, REM/physiology , Sleep Stages , Dreams/physiology , Polysomnography/methods , REM Sleep Behavior Disorder , Memory , Narcolepsy
Rev. ciênc. méd., (Campinas) ; 29: 204711, jan.-dez. 2020. ilus, tab
Article in Portuguese | LILACS | ID: biblio-1118459


O estudo pretendeu avaliar a fisiopatologia correlacionada, as diferenças de sexo e as comorbidades associadas à síndrome da apneia e hipopneia obstrutiva do sono. Trata-se de uma revisão de literatura realizada a partir dos dados obtidos pesquisas com as palavras-chaves "Síndrome de Apneia e Hipopneia do Sono", "Fisiopatologia", "Fatores de Risco", "Comorbidades e Sexo" nas plataformas digitais SciELO, PubMed, DESC Bireme e Google Acadêmico no período de 2008 a 2018. O sono é dividido em sono Rapid Eye Movement e sono Non-Rapid Eye Movement. A síndrome da apneia e hipopneia do sono é observada pelo ronco e caracterizada pela obstrução total (apneia) ou parcial (hipopneia) das vias aéreas superiores, que leva ao colapso e à dessaturação da oxi-hemoglobina e, consequentemente, causa hipóxia. Os índices de apneia e hipopneia são diagnosticados pela polissonografia e classificam o distúrbio em leve, moderado ou grave. A síndrome da apneia e hipopneia do sono apresenta-se frequentemente associada à obesidade e a doenças cardiovasculares, sendo principalmente observada em homens. A síndrome é considerada um problema de saúde pública mundial e envolve uma equipe multidisciplinar para o tratamento farmacológico ou não farmacológico. Dentre as principais comorbidades verificadas estão obesidade, hipertensão, arritmias e diabetes Mellitus tipo 2.

To evaluate correlated pathophysiologies, sex differences, and comorbidities associated with Obstructive Sleep Apnea-Hypopnea Syndrome. This is a literature review based on data obtained in SciELO, PubMed, DESC Bireme, and Google Scholar in the period between 2008 and 2018, using the following keywords: "Sleep Apnea and Hypopnea Syndrome", "Pathophysiology", "Risk Factors", "Comorbidities and Sex". Sleep is divided into Rapid Eye Movement sleep and Non- Rapid Eye Movement sleep. Obstructive Sleep Apnea-Hypopnea Syndrome is characterized by the total (apnea) or partial (hypopnea) obstruction of the upper airways, which results in snoring and leads to the collapse and desaturation of oxyhemoglobin, causing hypoxia. Apnea and hypopnea indexes are diagnosed by polysomnography and classified as mild, moderate, and severe. ApneaHypopnea Syndrome is often associated with obesity and cardiovascular diseases, and it is mainly observed in men. Apnea-Hypopnea Syndrome is a global public health problem, involving a multidisciplinary team for pharmacological or non-pharmacological treatment. Among the main comorbidities observed are: obesity, hypertension, arrhythmias, and type 2 diabetes Mellitus

Sleep Stages , Cardiovascular Diseases , Comorbidity , Sleep Apnea, Obstructive , Diabetes Mellitus , Obesity
Rev. bras. neurol ; 56(1): 5-10, jan.-mar. 2020. ilus
Article in English | LILACS | ID: biblio-1095921


Sleep occupies roughly one-third of human lives, yet it is still not entirely scientifically clear about its purpose or function. However, the latest research achievement concluded that sleeping has much more effect on the brain than formerly believed. Much of these studies are about the effects of sleep deprivation, and the glymphatic pathway initially identified in the rodent brain. In this paper, it is presented some of the theories about sleep functions, besides a review of some physiologic function of sleep. Now, it is accepted that sleep is involved with cleaning the brain toxins, physical restoration, information processing and recall, regulation, besides strengthening the immune system. Sleep implies in a neuronal activity markedly different along with its phases. It is regulated by two parallel mechanisms, homeostatic and circadian. Besides, the sleep-waking cycle involves diverse brain circuits and neurotransmitters and their interaction is explained using a flip-flop model. Several theories may help clarify the reasons human beings spend an important part of their lives sleeping such as those of Inactivity, Energy Conservation, Restorative, and Brain Plasticity. Recently, it was emphasized the importance of the glymphatic system that is a waste clearence system that acts mainly during sleep support efficient removal of soluble proteins and metabolites from the central nervous system. Indeed, sleep meet the needs of higher brain functions along with basic vital processes.

O sono ocupa cerca de um terço da vida humana, mas ainda não é totalmente claro cientificamente o seu propósito ou função. No entanto, a mais recente pesquisa concluiu que dormir tem muito mais efeito no cérebro do que se pensava anteriormente. Muitos desses estudos são sobre os efeitos da privação do sono e o sistema glinfático inicialmente identificada no cérebro de roedores. Neste artigo, são apresentadas algumas das teorias sobre as funções do sono, além de uma revisão de algumas funções fisiológicas do sono. Agora, aceita-se que o sono esteja envolvido com a limpeza de toxinas cerebrais, restauração física, processamento e memorização de informações, regulação do humor, além de fortalecer o sistema imunológico. O sono implica em uma atividade neuronal marcadamente diferente ao longo de suas fases. É regulado por dois mecanismos paralelos, homeostático e circadiano. Além disso, o ciclo de vigília envolve diversos circuitos cerebrais e neurotransmissores e sua interação é explicada por meio de um modelo de flip-flop. Várias teorias podem ajudar a esclarecer as razões pelas quais o ser humano passa uma parte importante de suas vidas dormindo, como as de inatividade, conservação de energia, restauração e plasticidade cerebral. Recentemente, enfatizou-se a importância do sistema glinfático agir principalmente durante o sono, que é um sistema de eliminação de resíduos para apoiar a remoção eficiente de proteínas e metabólitos solúveis do sistema nervoso central. De fato, o sono atende às necessidades de funções cerebrais superiores, juntamente com processos vitais básicos.

Humans , Sleep/physiology , Sleep Stages , Sleep Hygiene/physiology , Sleep, REM , Executive Function/physiology , Memory
Rev. bras. neurol ; 56(1): 11-18, jan.-mar. 2020. ilus, graf, tab
Article in English | LILACS | ID: biblio-1095930


The sleep-wake cycle that is circadian rhythm may have different patterns according to sex, environment and genetics determinants. This chronological cycle type, chronotype, may be populational expressed by the extremes, early or later going to bed and waking up, in a continuum. The first, the Morning-type individuals (the lark) and the later, the Evening types (the owl). Between the two extremes, there is the majority of these chronotypes ­ the intermediate ones. These patterns may be classified according to the questionnaires such as Horne and Ostberg Morningness/ Eveningness Questionnaire (MEQ) and the Munich Chrono Type Questionnaire (MCTQ). The rural population tends to be Morning-type, as well as children and younger women, more than men. The Morning person tends to be more healthy than the Evening ones who are more prone to diseases, as depression and metabolic syndrome. This basic knowledge may be helpful to patient's counseling and management: to avoid mismatch of circadian physiology and social duties / sleep. This circadian desynchrony can increase the risk of diseases, consequently there is a need to chrono-medicine into current treatment strategies.

O ciclo sono-vigília, que é um ritmo circadiano, pode ter padrões diferentes de acordo com os determinantes sexuais, ambientais e genéticos. Esse tipo de ciclo cronológico, cronótipo, pode ser expresso em termos populacionais pelos extremos, indo cedo ou mais tarde para a cama ou saindo dela, em um continuum. O primeiro, os indivíduos do tipo Manhã (a cotovia) e o posterior, os tipos da Tarde (a coruja). Entre os dois extremos, há a maioria desses cronotipos - os intermediários. Esses padrões podem ser classificados de acordo com questionários como o Horne e Ostberg Morningness/Eveningness Questionnaire (MEQ) e o Munich Chrono Type Questionnaire (MCTQ). A população rural tende a ser do tipo matutino, assim como crianças e mulheres mais jovens, mais que os homens.A pessoa da manhã tende a ser mais saudável do que as da noite, mais propensa a doenças, como depressão e síndrome metabólica. Esse conhecimento básico pode ser útil para o aconselhamento e tratamento dos pacientes: para evitar incompatibilidade entre a fisiologia circadiana e os deveres sociais / sono. Essa dessincronia circadiana pode aumentar o risco de doenças, consequentemente, é necessário a cronomedicina nas atuais estratégias de tratamento.

Humans , Male , Female , Adolescent , Adult , Middle Aged , Sleep/physiology , Biological Clocks , Circadian Rhythm/physiology , Sleep Stages , Sex Factors , Surveys and Questionnaires , Actigraphy , Circadian Clocks/physiology
Journal of Korean Neuropsychiatric Association ; : 25-28, 2020.
Article in Korean | WPRIM | ID: wpr-811245


Narcolepsy is a chronic neurological sleep disorder caused by hypocretin neuron loss, resulting in excessive daytime sleepiness, disturbed nocturnal sleep, and intrusions of aspects of rapid eye movement sleep in wakefulness, such as cataplexy, sleep paralysis, and hypnopompic/hypnagogic hallucinations. Narcolepsy disrupts the maintenance and orderly occurrence of the wake and sleep stages. Cataplexy is a highly specific symptom of narcolepsy, but many other symptoms can be observed in a variety of sleep disorders. The diagnosis of narcolepsy type 1 requires a history of excessive daytime sleepiness and one of the following : 1) a low cerebrospinal fluid hypocretin-1 level or 2) cataplexy and a positive multiple sleep latency test result. The diagnosis of narcolepsy type 2 requires a history of excessive daytime sleepiness and a positive mean sleep-latency test result. The mean sleep-latency test must be preceded by nighttime polysomnography to exclude other sleep disorders and to document adequate sleep. The mean sleep-latency test result can be falsely positive in other sleep disorders, such as shift work, sleep apnea, or sleep deprivation, and it is influenced by age, sex, and puberty. Modafinil and armodafinil can reduce the excessive daytime sleepiness without many of the side effects associated with older stimulants. Although there is no cure for narcolepsy, the treatments are often effective and include both behavioral and pharmacologic approaches.

Adolescent , Humans , Cataplexy , Cerebrospinal Fluid , Diagnosis , Disorders of Excessive Somnolence , Hallucinations , Narcolepsy , Neurons , Orexins , Polysomnography , Puberty , Sleep Apnea Syndromes , Sleep Deprivation , Sleep Paralysis , Sleep Stages , Sleep Wake Disorders , Sleep, REM , Wakefulness
Arq. neuropsiquiatr ; 77(9): 609-616, Sept. 2019. tab, graf
Article in English | LILACS | ID: biblio-1038752


ABSTRACT Obstructive sleep apnea (OSA) occurs in up to 66% of Parkinson's disease (PD) patients, higher than in the general population. Although it is more prevalent, the relationship between OSA and PD remains controversial, with some studies confirming and others denying the relationship of OSA with some risk factors and symptoms in patients with PD. Objective: To determine the factors associated with OSA in PD patients com DP. Methods: A cross-sectional study was performed with 88 consecutive patients with PD from the outpatient clinic. Participants underwent clinical interviews with neurologists and a psychiatrist, assessment using standardized scales (Epworth Sleepiness Scale, Parkinson's Disease Questionnaire, Pittsburgh Sleep Quality Index and, for individuals with a diagnosis of restless legs syndrome/Willis-Ekbom disease, the International Restless Legs Syndrome Rating Scale), and video-polysomnography. Results: Individuals with PD and OSA were older and had less insomnia than those with PD without OSA. Regarding the polysomnographic variables, we observed a lower percentage of stage N3 sleep, a higher arousal index, and a higher oxygen desaturation index in those individuals with OSA, relative to those without OSA. In the multivariate analysis, only the percentage of stage N3 sleep and the oxygen desaturation index were significantly different. Besides this, most of the PD patients with OSA had a correlation with sleeping in the supine position (58% of OSA individuals). Conclusion: The PD patients showed a high prevalence of OSA, with the supine position exerting a significant influence on the OSA in these patients, and some factors that are associated with OSA in the general population did not seem to have a greater impact on PD patients.

RESUMO A Apneia Obstrutiva do Sono (AOS) chega a acometer até 66% dos pacientes com doença de Parkinson (DP), prevalência maior, portanto, que a da população geral. Embora seja mais prevalente, a relação entre AOS e DP permanence controversa, com trabalhos confirmando e outros afastando a relação de AOS com alguns fatores de risco e sintomas em pacientes com DP. Objetivo: Determinar quais fatores estão relacionados à AOS em pacientes com DP. Métodos: Estudo transversal, observacional, realizado com 88 pacientes com DP, provenientes do ambulatório de hospital público. Os pacientes foram submetidos à entrevista clínica com neurologista e psiquiatra, à aplicação de escalas padronizadas (escala de sonolência de Epworth, questionário de qualidade de vida da DP, índice de qualidade de sono de Pittsburgh e, para os indivíduos com diagnóstico de Síndrome das Pernas Inquietas, a escala internacional de graduação da SPI), e vídeo-polissonografia. Resultados: Indivíduos com DP e AOS apresentaram maior idade e menor prevalência de insônia crônica que os indivíduos com DP, sem AOS. Em relação às variáveis polissonográficas, observamos uma baixa proporção de sono N3, elevado índice de microdespertares e maior índice de desaturações nos indivíduos com AOS, em comparação ao grupo sem AOS. Na análise multivariada, apenas a porcentagem de sono N3 e o índice de dessaturação permaneceu significativo. Além disso, a maior parte dos pacientes tem relação com a posição supina (58% dos pacientes com AOS). Conclusão: Pacientes com DP apresentaram prevalência elevada de AOS, a posição supina exerceu influência importante na AOS destes pacientes e alguns fatores que estão associados à AOS na população geral não mostraram impacto significativo nos pacientes com DP.

Humans , Male , Female , Middle Aged , Aged , Parkinson Disease/complications , Parkinson Disease/epidemiology , Sleep Apnea, Obstructive/complications , Sleep Apnea, Obstructive/epidemiology , Quality of Life , Sleep Stages/physiology , Time Factors , Brazil/epidemiology , Cross-Sectional Studies , Multivariate Analysis , Surveys and Questionnaires , Risk Factors , Supine Position/physiology , Polysomnography , Statistics, Nonparametric
Biomedical Engineering Letters ; (4): 73-85, 2019.
Article in English | WPRIM | ID: wpr-763004


With progress in sensors and communication technologies, the range of sleep monitoring is extending from professional clinics into our usual home environments. Information from conventional overnight polysomnographic recordings can be derived from much simpler devices and methods. The gold standard of sleep monitoring is laboratory polysomnography, which classifi es brain states based mainly on EEGs. Single-channel EEGs have been used for sleep stage scoring with accuracies of 84.9%. Actigraphy can estimate sleep effi ciency with an accuracy of 86.0%. Sleep scoring based on respiratory dynamics provides accuracies of 89.2% and 70.9% for identifying sleep stages and sleep effi ciency, respectively, and a correlation coeffi cient of 0.94 for apnea–hypopnea detection. Modulation of autonomic balance during the sleep stages are well recognized and widely used for simpler sleep scoring and sleep parameter estimation. This modulation can be recorded by several types of cardiovascular measurements, including ECG, PPG, BCG, and PAT, and the results showed accuracies up to 96.5% and 92.5% for sleep effi ciency and OSA severity detection, respectively. Instead of using recordings for the entire night, less than 5 min ECG recordings have used for sleep effi ciency and AHI estimation and resulted in high correlations of 0.94 and 0.99, respectively. These methods are based on their own models that relate sleep dynamics with a limited number of biological signals. Parameters representing sleep quality and disturbed breathing are estimated with high accuracies that are close to the results obtained by polysomnography. These unconstrained technologies, making sleep monitoring easier and simpler, will enhance qualities of life by expanding the range of ubiquitous healthcare.

Actigraphy , Brain , Delivery of Health Care , Electrocardiography , Electroencephalography , Mycobacterium bovis , Polysomnography , Respiration , Sleep Stages
International Neurourology Journal ; : 249-256, 2019.
Article in English | WPRIM | ID: wpr-764118


PURPOSE: To determine if self-administered transcutaneous tibial nerve stimulation (TTNS) is a feasible treatment option for neurogenic bladder among people with spinal cord injury (SCI) who utilize intermittent catheterization for bladder management. METHODS: Four-week observational trial in chronic SCI subjects performing intermittent catheterization with incontinence episodes using TTNS at home daily for 30 minutes. Those using anticholinergic bladder medications were given a weaning schedule to begin at week 2. Primary outcomes were compliance and satisfaction. Secondary outcomes included change in bladder medications, efficacy based on bladder diary, adverse events, and incontinence quality of life (I-QoL) survey.

Humans , Appointments and Schedules , Catheterization , Catheters , Compliance , Mouth , Quality of Life , Sleep Stages , Spinal Cord Injuries , Spinal Cord , Tibial Nerve , Transcutaneous Electric Nerve Stimulation , Urinary Bladder , Urinary Bladder, Neurogenic , Urodynamics , Weaning
Clinical and Experimental Otorhinolaryngology ; : 72-78, 2019.
Article in English | WPRIM | ID: wpr-739228


OBJECTIVES: To develop a simple algorithm for prescreening of obstructive sleep apnea (OSA) on the basis of respiratorysounds recorded during polysomnography during all sleep stages between sleep onset and offset. METHODS: Patients who underwent attended, in-laboratory, full-night polysomnography were included. For all patients, audiorecordings were performed with an air-conduction microphone during polysomnography. Analyses included allsleep stages (i.e., N1, N2, N3, rapid eye movement, and waking). After noise reduction preprocessing, data were segmentedinto 5-s windows and sound features were extracted. Prediction models were established and validated with10-fold cross-validation by using simple logistic regression. Binary classifications were separately conducted for threedifferent threshold criteria at apnea hypopnea index (AHI) of 5, 15, or 30. Prediction model characteristics, includingaccuracy, sensitivity, specificity, positive predictive value (precision), negative predictive value, and area under thecurve (AUC) of the receiver operating characteristic were computed. RESULTS: A total of 116 subjects were included; their mean age, body mass index, and AHI were 50.4 years, 25.5 kg/m2, and23.0/hr, respectively. A total of 508 sound features were extracted from respiratory sounds recorded throughoutsleep. Accuracies of binary classifiers at AHIs of 5, 15, and 30 were 82.7%, 84.4%, and 85.3%, respectively. Predictionperformances for the classifiers at AHIs of 5, 15, and 30 were AUC, 0.83, 0.901, and 0.91; sensitivity, 87.5%,81.6%, and 60%; and specificity, 67.8%, 87.5%, and 94.1%. Respective precision values of the classifiers were89.5%, 87.5%, and 78.2% for AHIs of 5, 15, and 30. CONCLUSION: This study showed that our binary classifier predicted patients with AHI of ≥15 with sensitivity and specificityof >80% by using respiratory sounds during sleep. Since our prediction model included all sleep stage data, algorithmsbased on respiratory sounds may have a high value for prescreening OSA with mobile devices.

Humans , Apnea , Area Under Curve , Body Mass Index , Classification , Logistic Models , Machine Learning , Noise , Polysomnography , Respiratory Sounds , ROC Curve , Sensitivity and Specificity , Sleep Apnea, Obstructive , Sleep Stages , Sleep, REM
Journal of Korean Academy of Community Health Nursing ; : 79-89, 2019.
Article in Korean | WPRIM | ID: wpr-739087


PURPOSE: The purpose of this paper is to determine effects of auricular acupressure on knee pain, range of motion, and sleep in the elderly with knee osteoarthritis. METHODS: This is an experimental study designed with randomization and single-blind in a placebo-control approach. The subjects included the elderly with knee osteoarthritis who were using an elderly welfare facility. In each of the groups, 28 subjects were assigned. For the experimental group, auricular acupressure was applied to five pressure points related to the pain caused by osteoarthritis and sleep. In the case of the placebo-control group, auricular acupressure was applied to other five points than the former. The intervention lasted eight weeks. In order to examine intervention effects of auricular acupressure, joint pain, Pressure Pain Thresholds (PPTs), and extension and flexion range of motion (ROM) were measured weekly. As for the pre- and post-examinations, pain, sleep quality, and the time-by-sleep stage of the patients with knee osteoarthritis were measured. RESULTS: The VAS scores in the experimental group with auricular acupressure significantly decreased through time (p<.001) and WOMAC also significantly decreased (p<.01) compared with the placebo-control group. However, there were no significant differences in PPTs. The flexion (p<.01) and extension (p<.001) ROMs measured for eight weeks improved over time. Meanwhile, sleep quality improved significantly after the intervention termination (p<.01), but there was no significant difference found in the time-by-sleep stage. CONCLUSION: Auricular acupressure applied for eight weeks was found to be effective in reducing joint pain, improving knee ROM, and improving sleep quality in patients with degenerative knee arthritis.

Aged , Humans , Acupressure , Arthralgia , Arthritis , Joints , Knee , Osteoarthritis , Osteoarthritis, Knee , Pain Threshold , Random Allocation , Range of Motion, Articular , Sleep Stages
Journal of Sleep Medicine ; : 56-60, 2019.
Article in English | WPRIM | ID: wpr-766231


A close relationship has emerged between obstructive sleep apnea (OSA) and cardiac arrhythmia. However, transient sinus arrest or atrioventricular (AV) conduction disturbance during rapid eye movement (REM) sleep was rarely reported. This sleep stage specific arrhythmia has been referred to as REM sleep-related bradyarrhythmia syndrome. The differential diagnosis between OSA-related arrhythmia and REM sleep-related bradyarrhythmia syndrome is important in determining the treatment strategy for the underlying disease and its complication, especially in patient with a history of OSA. Here, we report a case with both REM sleep-related AV block and severe OSA, whose REM sleep-related AV block was not improved with continuous positive airway pressure treatment.

Humans , Arrhythmias, Cardiac , Atrioventricular Block , Bradycardia , Continuous Positive Airway Pressure , Diagnosis, Differential , Sleep Apnea, Obstructive , Sleep Stages , Sleep, REM