Sleep paralysis in college students

Lifetime prevalence and characteristics of sleep paralysis in Italian university students population

Sleep paralysis (SP) is a REM-related parasomnia, characterized by the inability to perform voluntary movements. It is a relatively widespread phenomenon in the general population and, although usually not dangerous, it is experienced with intense fear. The current study aims to evaluate the lifetime prevalence and characteristics of SP in the Italian student population. The study was conducted online, through an online battery of questionnaires. We used the Unusual Sleep Experience Questionnaire to investigate the prevalence of the disorder and the typical characteristics, metacognitive beliefs on the episodes, and previous distress factors. We also collected information about anxiety and depression symptomatology, sleep quality, and circadian preferences. Four hundred and thirty-two participants (333 F, 22.8 ± 2.57 y) took part in the study and 37.5 % of them reported having experienced at least one SP episode in their lifetime. On a physiological level, the most common features were the inability to speak followed by a tingling sensation and the inability to open the eyes, consistent with REM muscle atonia. Cognitive features during episodes include the perception of a presence in the room, followed by the fear of dying. Participants who reported SP had higher anxiety and worse sleep quality, and were more associated with evening chronotypes compared to non-SP responders. Our results show that SP is generally widespread in Italian students, in line with the prevalence reported in previous studies. Further studies could investigate the effects of suggested therapies to decrease the number of episodes of the disorder, especially in those who experience it recurrently.

All-night spectral and microstate EEG analysis in patients with recurrent isolated sleep paralysis

The pathophysiology of recurrent isolated sleep paralysis (RISP) has yet to be fully clarified. Very little research has been performed on electroencephalographic (EEG) signatures outside RISP episodes. This study aimed to investigate whether sleep is disturbed even without the occurrence of a RISP episode and in a stage different than conventional REM sleep. 17 RISP patients and 17 control subjects underwent two consecutive full-night video-polysomnography recordings. Spectral analysis was performed on all sleep stages in the delta, theta, and alpha band. EEG microstate (MS) analysis was performed on the NREM 3 phase due to the overall high correlation of subject template maps with canonical templates. Spectral analysis showed a significantly higher power of theta band activity in REM and NREM 2 sleep stages in RISP patients. The observed rise was also apparent in other sleep stages. Conversely, alpha power showed a downward trend in RISP patients' deep sleep. MS maps similar to canonical topographies were obtained indicating the preservation of prototypical EEG generators in RISP patients. RISP patients showed significant differences in the temporal dynamics of MS, expressed by different transitions between MS C and D and between MS A and B. Both spectral analysis and MS characteristics showed abnormalities in the sleep of non-episodic RISP subjects. Our findings suggest that in order to understand the neurobiological background of RISP, there is a need to extend the analyzes beyond REM-related processes and highlight the value of EEG microstate dynamics as promising functional biomarkers of RISP.

Prevalence and Clinical Characteristics of Sleeping Paralysis: A Systematic Review and Meta-Analysis

Sleep paralysis (SP) is a mixed state of consciousness and sleep, combining features of rapid eye movement (REM) sleep with those of wakefulness. The exact cause of SP is unknown, and its prevalence varies among the studies. We aim to identify SP's global prevalence, the affected population's characteristics, and the SP's clinical picture. We searched three databases (PubMed, Scopus, and Web of Science (WoS)) using a unique search strategy to identify eligible studies. All observational studies identifying the prevalence or frequency of sleeping paralysis were included. No exclusions are made based on country, race, or questionnaire. The analysis was performed using the latest version of R software (R Core Team, Vienna, Austria). The analysis included 76 studies from 25 countries with 167,133 participants. The global prevalence of SP was 30% (95% CI (22%, 39%)). There were similar frequencies of isolated SP and SP (33%, 95% CI (26%, 42%), I2 = 97%, P <0.01; 31%, 95% CI (21%, 43%), I2 = 100%, P = 0, respectively). A subgroup analysis showed that the majority of those who experienced SP were psychiatric patients (35%, 95% CI (20%, 55%), I2 = 96%, P <0.01). The prevalence among non-psychiatric patients was among students (34%, 95% CI (23%, 47%), I2 = 100%, P = 0). Auditory and visual hallucinations were reported in 24.25% of patients. Around 4% had only visual hallucinations. Meta-regression showed no association between the frequency of SP and sex. Publication bias was detected among the included studies through visual inspection of funnel plot asymmetry. Our findings revealed that 30% of the population suffered from SP, especially psychiatric patients and students. The majority of SP cases lacked associated hallucinations, while a noteworthy proportion experienced combined visual and auditory hallucinations.

The prevalence of sleep paralysis in medical students in Buenos Aires, Argentina

Objectives

The objectives of this study were to determine the prevalence of sleep paralysis (SP) in medical students from the University of Buenos Aires (UBA).

Materials and Methods

An ad hoc questionnaire based on the diagnosis of SP and a demographic survey was electronically presented to students of Internal Medicine at the School of Medicine of the UBA. The respondents answered both questionnaires using Google Forms®.

Results

The prevalence of SP was 40.7% (95% CI 33.5-47.8). A higher percentage of the respondents (76%) reported experiencing SP-related anxiety. An association between self-perceived quality of sleep and the incidence of SP was found (χ2: 12.712, P = 0.002). The highest frequency was hypnopompic SP (55.55%), and the highest percentage (55.4%) suffered from SP less than once every 6 months. Most respondents (59.5%) reported having started with SP symptoms after 18 years of age, and the highest percentage (66.2%) had exacerbated their symptoms at college. The frequency of the Incubus phenomenon was 14.5% (95% CI 6.2-23). Most respondents (70.8%) denied the association of SP with religious or paranormal beliefs.

Conclusion

SP is highly prevalent in medical students and is associated with poor sleep habits and perceived poor sleep quality. Clinicians should be aware of this parasomnia to avoid a misdiagnosis of psychosis and inform sufferers of the nature of SP.

Detection of Sleep Paralysis by using IoT Based Device and Its Relationship Between Sleep Paralysis And Sleep Quality

When a person wakes up in the middle of the night, they are paralyzed. Despite the fact that most episodes are associated with extreme terror and some might cause clinically significant suffering, little is understood about the experience. This study will analyze existing research on the relationship between sleep paralyses and sleep in general. Many studies have connected poor sleep quality to an increased risk of sleep paralysis. Awake yet unable to act, sleep paralysis occurs. This might happen between awake and sleeping. The problem is approached in three steps: Data collection, data storage, calculation and machine learning prediction of sleep paralysis. The data came from the Smart Device. The dataset has several (independent) and dependent variables (Outcome). This device has been put to the test. Each exam has its own set of features and predicted outcomes. To assess the system's validity, we executed a posture recognition accuracy test. The device was hidden on top of the bed. The controller is in charge of measurement and data collection. Experiments were conducted by collecting pressure data from a patient lying down. The person acted out his sleeping positions on a mat for a while. Machine learning has been used to predict sleep paralysis. By comparing sleep postures to the outcome, we were able to show the link between sleep qualities and sleep paralysis. Machine learning approaches have been used to predict sleep paralysis. Comparing sleeping positions with the results showed the link between sleep quality and sleep paralysis. Sleep paralysis correlates with poor sleep quality. The Random Forest model has the highest accuracy of 91.9 percent in predicting sleep paralysis in the given dataset. SVM with Linear Kernel was 80.49 percent accurate, RBF was 42.68 percent, and Polynomial was 47.56 percent. The accuracy of logistic regression was 76.83 percent. KNN had a dismal performance of 60.98%. Decision Trees and Gradient Boosting both fared well at 85.37 percent.