Nikolai Gogols account of sleep paralysis in the tale ``The Portrait
روایت نیکولای گوگول از فلج خواب در داستان "پرتره"-2021
Several classical writers had an impressive power of observation and often depicted medical conditions in their works long before medical literature did. Sleep paralysis is a common and frightening experience, in particular when occurring for the first time. Therefore, it is not surprising that it has been frequently described in the classical literature, eg by Dostoevsky, Kafka, Dickens, and Maupassant. In Nikolai Gogols tale “The portrait” (1833) we could recognize an excellent description of a sleep paralysis, in which several components of this condition were depicted including motor paralysis, visual and auditory hallucinations, and autonomic manifestations. To the best of our knowledge, this account is the earliest description of a sleep paralysis in non-medical literature. © 2021 Elsevier B.V. All rights reserve
keywords: فلج خواب | گوگل | "پرتره" | توهمات | خواب | Sleep paralysis | Gogol | “The portrait” | Hallucinations | Sleep
Why is personality tied to sleep quality? A biometric analysis of twins
چرا شخصیت با کیفیت خواب ارتباط دارد؟ تجزیه و تحلیل بیومتریک دوقلوها-2021
Despite consistent links between personality traits and poor sleep, little is known about genetic and environmental influences that may produce them. This study examined how much genetic background and environmental experiences contributed to phenotypic linkages between personality and subjective sleep quality. Seven hundred and thirty-four twin pairs from the Minnesota Study of Twin Aging and Development rated their sleep quality and provided personality reports. Bi-variate analyses revealed that genetic factors accounted for the majority of observed associations between subjective sleep quality and traits, but also that non-shared environmental experience played a role that varied across traits. The findings strongly implicate genotype in tying subjective sleep quality to personality variation, alongside non- shared environmental influences, and suggest indicate influences unique to individual traits.© 2020 Elsevier Inc. All rights reserved.
Keywords: Sleep | Personality | Insomnia | Genetic | Development
Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice
آیا داده های غیر انتخابی ترجیحات اقتصادی را نشان می دهد؟ شواهدی از داده های بیومتریک و انتخاب طرح جبران-2021
We investigate the feasibility of inferring economic choices from simple biometric non- choice data. We employ a machine learning approach to assess whether biometric data acquired during sleep, naturally occurring daily chores and participation in an experiment can reveal preferences for competitive and team-based compensation schemes. We ﬁnd that biometric data acquired using wearable devices enable equally accurate out-of- sample prediction for compensation-scheme choice as gender and performance. Our results demonstrate the feasibility of inferring economic choices from simple biometric data without observing past decisions. However, we ﬁnd that biometric data recorded in naturally occurring environments during daily chores and sleep add little value to out-of- sample predictions.© 2021 Elsevier B.V. All rights reserved.
Keywords: Compensation schemes | Competition | Team | Experiment | Gender | Heart rate variability | Non-choice data
Diagnosis of obstructive sleep apnea with prediction of flow characteristics according to airway morphology automatically extracted from medical images: Computational fluid dynamics and artificial intelligence approach
تشخیص آپنه انسدادی خواب با پیش بینی ویژگی های جریان با توجه به مورفولوژی راه های هوایی به طور خودکار از تصاویر پزشکی استخراج می شود: دینامیک سیالات محاسباتی و رویکرد هوش مصنوعی-2021
Background: Obstructive sleep apnea syndrome (OSAS) is being observed in an increasing number of cases. It can be diagnosed using several methods such as polysomnography. Objectives: To overcome the challenges of time and cost faced by conventional diagnostic methods, this paper proposes computational fluid dynamics (CFD) and machine-learning approaches that are derived from the upper-airway morphology with automatic segmentation using deep learning.
Method: We adopted a 3D UNet deep-learning model to perform medical image segmentation. 3D UNet prevents the feature-extraction loss that may occur by concatenating layers and extracts the anteroposterior coordination and width of the airway morphology. To create flow characteristics of the upper airway training data, we analyzed the changes in flow characteristics according to the upper-airway morphology using CFD. A multivariate Gaussian process regression (MVGPR) model was used to train the flow characteristic values. The trained MVGPR enables the prompt prediction of the aerodynamic features of the upper airway without simulation. Unlike conventional regression methods, MVGPR can be trained by considering the correlation between the flow characteristics. As a diagnostic step, a support vector machine (SVM) with predicted aerodynamic and biometric features was used in this study to classify patients as healthy or suffering from moderate OSAS. SVM is beneficial as it is easy to learn even with a small dataset, and it can diagnose various flow characteristics as factors while enhancing the feature via the kernel function. As the patient dataset is small, the Monte Carlo cross-validation was used to validate the trained model. Furthermore, to overcome the imbalanced data problem, the oversampling method was applied.
Result: The segmented upper-airway results of the high-resolution and low-resolution models present overall average dice coefficients of 0.76±0.041 and 0.74±0.052, respectively. Furthermore, the classification accuracy, sensitivity, specificity, and F1-score of the diagnosis algorithm were 81.5%, 89.3%, 86.2%, and 87.6%, respectively.
Conclusion: The convenience and accuracy of sleep apnea diagnosis are improved using deep learning and machine learning. Further, the proposed method can aid clinicians in making appropriate decisions to evaluate the possible applications of OSAS.
Keywords: Obstructive sleep apnea syndrome | Auto-segmentation | Upper-airway morphology | Computational fluid dynamics
Vision-related quality of life of Chinese children undergoing orthokeratology treatment compared to single vision spectacles
کیفیت زندگی مرتبط با بینایی کودکان چینی تحت درمان ارتوکراتولوژی در مقایسه با عینک های بینایی مجرد-2021
Objective: To measure and compare the vision-related quality of life between Chinese children wearing orthokeratology (ortho-k) lenses and single vision spectacles, to understand acceptance of ortho-k treatment by children in China.
Methods: Subjects of Chinese origin, with myopia of -5.00 to -0.75 D, astigmatism < 1.50 D were recruited. All subjects had been wearing optical correction – ortho-k lenses or single vision spectacles (SVS), for the past 12–18 months and were aged between 8–12 years. The Pediatric Refractive Error Profile (PREP) questionnaire, translated to Chinese, was used to evaluate the perceptions of children wearing spectacles in overall vision, near vision, far vision, symptoms, appearance, satisfaction, activities, academic performance, handling of optical corrections, and peer perceptions. PREP questions, rephrased to address the same issues for ortho-k subjects who did not wear spectacles in the daytime, were used for ortho-k wearers (PREP-OK). The mean score of all items was calculated as the overall score. For ortho-k wearers, four additional questions on experience and frequency of symptoms: experiencing difficulty in falling asleep, ocular discomfort, itchy/burning/dry eyes, and foreign body sensation during ortho-k lens wear at night were asked and reported separately.
Results: Forty subjects (20 ortho-k, 20 SVS) completed the study. Overall vision, far vision, appearance, satisfaction, activities, and peer perception scores in the ortho-k group were significantly better than the SVS group (all P < 0.05). Handling of optical correction score in the ortho-k group was significantly worse than the SVS group (P = 0.04). No significant differences in near vision, symptoms in the daytime and academic performance were found between two groups (P > 0.05). With respect to symptoms during ortho-k lens wear at night, none of the subjects reported difficulty in falling asleep, but 30–40 % of subjects reported occasional ocular discomfort, itchy/burning/dry eyes, and foreign body sensation after lens insertion.
Conclusion: Although ortho-k may induce some ocular discomfort with lens wear during the night, these were infrequent and the benefits from ortho-k can compensate for the discomfort, leading to better vision-related quality of life in Chinese children, compared with those wearing SVS.
Keywords: Orthokeratology | Spectacles | Symptoms | Vision-related quality of life | Children
A different sleep apnea classification system with neural network based on the acceleration signals
یک سیستم طبقه بندی sleep apnea متفاوت با شبکه عصبی مبتنی بر سیگنال های شتاب-2020
Background and objective: The apnea syndrome is characterized by an abnormal breath pause or reduction in the airflow during sleep. It is reported in the literature that it affects 2% of middle-aged women and 4% of middle-aged men, approximately. This study has vital importance, especially for the elderly, the disabled, and pediatric sleep apnea patients. Methods: In this study, a new diagnostic method is developed to detect the apnea event by using a microelectromechanical system (MEMS) based acceleration sensor. It records the value of acceleration by measuring the movements of the diaphragm in three axes during the respiratory. The measurements are carried out simultaneously, a medical spirometer (Fukuda Sangyo), to test the validity of measurement results. An artificial neural network model was designed to determine the apnea event. For the number of neurons in the hidden layer, 1-3-5-10-18-20-25 values were tried, and the network with three hidden neurons giving the most suitable result was selected. In the designed ANN, three layers were formed that three neurons in the hidden layer, the two neurons at the input, and two neurons at the output layer. Results: A study group was formed of 5 patients (having different characteristics (age, height, and body weight)). The patients in the study group have sleep apnea (SA) in different grades. Several 12.723 acceleration data (ACC) in the XYZ-axis from 5 different patients are recorded for apnea event training and detection. The measured accelerometer (ACC) data from one of the patients (called H1) are used to train an ANN. During the training phase, MSE is used to calculate the fitness value of the apnea event. Then Apnea event is detected successfully for the other patients by using ANN trained only with H1’s ACC data. Conclusions: The sleep apnea event detection system is presented by using ANN from directly acceleration values. Measurements are performed by the MEMS-based accelerometer and Industrial Spirometer simultaneously. A total of 12723 acceleration data is measured from 5 different patients. The best result in 7000 iterations was reached (the number of iterations was tried up to 10.000 with 1000 steps). 605 data of only H1 measurements are used to train ANN, and then all data used to check the performance of the ANN as well as H2, H3, H4, and H5 measurement results. MSE performance benchmark shows us that trained ANN successfully detects apnea events. One of the contributions of this study to literature is that only ACC data are used in the ANN training step. After training for one patient, the ANN system can monitor the apnea event situation on-line for others.
Keywords: Sleep apnea | Acceleration sensor | Acceleration data | Artificial neural network | Medical decision making
Eyes wide open: A systematic review of the association between insomnia and aggression in forensic contexts
چشمان کاملا باز: بررسی سیستماتیک ارتباط بین بی خوابی و پرخاشگری در زمینه های پزشکی قانونی-2020
Sleep quality has been highlighted as a significant predictor of violent behavior through lifespan and across pathologies and a causal link has also been suggested. Despite the high prevalence of insomnia and its potential impact as a modifiable risk factor for aggressive behavior, a comprehensive synthesis of the literature is lacking. We aimed to systematically review the published works exploring the role of sleep in aggressive behaviors, especially focusing on forensic contexts. We performed a systematic review searching the electronic databases PubMed and Scopus through December 2020 and selected articles that compared sleep of offenders and controls and articles that studied the association between sleep and aggression. Ten articles were selected: 2 compared sleep in offenders and controls and 8 studied the association between sleep and aggression. Offenders showed worse sleep features than control both objectively and subjectively measured. Sleep quality was associated with aggression, but sleep quantity was less studied. Sleep seems to have a prominent role in aggressive behaviors but studies concerning this topic are few; samples and methods were highly heterogeneous and most studies were cross-sectional. Future studies are needed to clarify the association between sleep disturbances and aggression, adopting a more systematized approach. Sleep assessment and treatment and might be particularly useful, especially in high-risk populations.
Keywords: Aggression | Sleep | Offenders | Forensic
Examining the factors associated with impulsivity in forensic populations: A systematic review
بررسی عوامل مرتبط با تکانشگری در جمعیت پزشکی قانونی: یک بررسی منظم-2020
Background: Elevated levels of impulsivity are considered a significant risk factor for violent behaviour within forensic populations but our knowledge of the causes of impulsivity in this group remains limited. This review collates and critically evaluates existing research examining factors associated with impulsive behaviour in forensic populations. Method: A systematic review of the current literature was conducted. The electronic databases PsycINFO, MEDLINE, EMBASE, and ProQuest Criminal Justice and Social Sciences were searched. Methodological quality assessment of eligible articles was completed prior to a narrative synthesis of findings. Results: Nine studies were included for review. Identified studies were rated as either of “adequate” or “good” quality. Studies were limited in their use of prospective, longitudinal methodological designs to assess the relationship between study variables and impulsive behaviour. Factors associated with increased impulsivity included traumatic brain injury, substance or alcohol misuse, traumatic experiences and difficulty sleeping. Conclusions: There remains little evidence regarding the underlying factors associated with impulsivity in forensic groups or, whether these might differ from those in the wider population; a question that will require further research. Those factors associated with impulsivity in forensic populations thus far; trauma, head injury, alcohol and substance misuse and poor sleep quality, provide the opportunity for more targeted screening for, and treatment of, impulsivity.
Keywords: Impulsivity | Forensic | Traumatic brain injury | Substances | Alcohol | Trauma | Sleep
Cell assemblies, sequences and temporal coding in the hippocampus
مونتاژهای سلولی ، توالی ها و برنامه نویسی موقتی در هیپوکامپ-2020
Like social networks, neurons in the brain are organized in neuronal ensembles that constrain and at the same time enrich the role and temporal precision of activity of individual neurons. Changes in coordinated firing across cortical neurons as well as selective changes in timing and sequential order across neurons that are important for encoding of novel information have collectively been known as ensemble temporal coding. Here we review recent findings on the role of online and offline temporal coding within sequential cell assemblies from the rodent hippocampus thought be important for memory encoding and consolidation and for spatial navigation. We propose that temporal coding in the rodent hippocampus represented as plasticity in replay activity relies primarily on subtle and selective changes in coordinated firing within the microstructure of individual cell assembly organization during sleep.
Revisiting the value of polysomnographic data in insomnia: more than meets the eye
بازنگری ارزش داده سندرم آپنه در بی خوابی: بیش از ملاقات چشم-2020
Background: Polysomnography (PSG) is not recommended as a diagnostic tool in insomnia. However, this consensual approach might be tempered in the light of two ongoing transformations in sleep research: big data and artificial intelligence (AI). Method: We analyzed the PSG of 347 patients with chronic insomnia, including 59 with Sleep State Misperception (SSM) and 288 without (INS). 89 good sleepers (GS) were used as controls. PSGs were compared regarding: (1) macroscopic indexes derived from the hypnogram, (2) mesoscopic indexes extracted from the electroencephalographic (EEG) spectrum, (3) sleep microstructure (slow waves, spindles). We used supervised algorithms to differentiate patients from GS. Results: Macroscopic features illustrate the insomnia conundrum, with SSM patients displaying similar sleep metrics as GS, whereas INS patients show a deteriorated sleep. However, both SSM and INS patients showed marked differences in EEG spectral components (meso) compared to GS, with reduced power in the delta band and increased power in the theta/alpha, sigma and beta bands. INS and SSM patients showed decreased spectral slope in NREM. INS and SSM patients also differed from GS in sleep microstructure with fewer and slower slow waves and more and faster sleep spindles. Importantly, SSM and INS patients were almost indistinguishable at the meso and micro levels. Accordingly, unsupervised classifiers can reliably categorize insomnia patients and GS (Cohens k ¼ 0.87) but fail to tease apart SSM and INS patients when restricting classifiers to micro and meso features (k¼0.004). Conclusion: AI analyses of PSG recordings can help moving insomnia diagnosis beyond subjective complaints and shed light on the physiological substrate of insomnia.
Keywords: Artificial intelligence | Machine learning | Insomnia | Polysomnography | REM | NREM sleep