Study Objectives:In some cases, it is difficult to correctly nosologically classify daytime sleepiness. Clinical manifestations may be non-specific, on the basis of objective measures it is possibl...

Sleep disorders are diagnosed in sleep laboratories by polysomnography, a multi-parameter examination that monitors biological signals during sleep. The subsequent evaluation of the obtained records is very time-consuming. The goal of this study was to create an automatic system for evaluation of the airflow and SpO${}_2$ channels of polysomnography records, through the use of machine learning techniques and a large database, for apnea and desaturation detection (which is unusual in other studies). To that end, a convolutional neural network (CNN) was designed using hyperparameter optimization. It was then trained and tested for apnea and desaturation. The proposed CNN was compared with the commonly used k-nearest neighbors (k-NN) method. The classifiers were designed based on nasal airflow and blood oxygen saturation signals. The final neural network accuracy for apnea detection reached 84%, and that for desaturation detection was 74%, while the k-NN classifier reached accuracies of 83% and 64% for apnea detection and desaturation detection, respectively.

Functional magnetic resonance imaging (fMRI) techniques and electroencephalography (EEG) were used to investigate sleep with a focus on impaired arousal mechanisms in disorders of arousal (DOAs). With a prevalence of 2–4% in adults, DOAs are significant disorders that are currently gaining attention among physicians. The paper describes a simultaneous EEG and fMRI experiment conducted in adult individuals with DOAs ($n=10$). Both EEG and fMRI data were validated by reproducing well established EEG and fMRI associations. A method for identification of both brain functional areas and EEG rhythms associated with DOAs in shallow sleep was designed. Significant differences between patients and controls were found in delta, theta, and alpha bands during awakening epochs. General linear models of the blood-oxygen-level-dependent signal have shown the secondary visual cortex and dorsal posterior cingulate cortex to be associated with alpha spectral power fluctuations, and the precuneus with delta spectral power fluctuations, specifically in patients and not in controls. Future EEG–fMRI sleep studies should also consider subject comfort as an important aspect in the experimental design.

Objectives: Pregnancy is often associated with reduced sleep quality and an increase in sleep disorders, such as restless leg syndrome, obstructive sleep apnea, and insomnia. There are few studies investigating the prevalence of parasomnias in pregnancy, although they may be expected to be a significant problem, as disturbed sleep in this time period in addition to these sleep disorders may trigger parasomnia episodes. Methods: We conducted a survey using an online questionnaire focusing on a comparison of the prevalence of parasomnias in three time periods: 3 months before pregnancy, during pregnancy, and 3 months after delivery. We also inquired about psychiatric and neurological comorbidities, current anxiety and depression symptoms, and pregnancy complications. Results: A total of 325 women (mean age 30.3 ± 5.3 years) participated in the online survey. The overall number of reported parasomnias increased during pregnancy compared to the 3 months before pregnancy (p < 0.001) and decreased after childbirth (p < 0.001). Specifically, we found a significant increase in sleepwalking (p = 0.02) and night terrors (p < 0.001), as well as in vivid dreams (p < 0.001) and nightmares (p < 0.001) during pregnancy. A similar significant increase during pregnancy was reported for head explosion (p < 0.011). In contrast, the number of episodes of sleep paralysis increased after delivery (p = 0.008). At the individual level, an increase in the severity/frequency of individual parasomnia episodes was also observed during pregnancy. Participants whose vivid dreams/nightmares persisted after delivery had higher BDI-II and STAI-T scores. Our data also suggest a significant impact of migraines and other chronic pain, as well as complications during pregnancy, on the presence of parasomnia episodes in our cohort. Conclusions: We have shown that the prevalence of parasomnias increases during pregnancy and needs to be targeted, especially by non-pharmacological approaches. At the same time, it is necessary to inquire about psychiatric and neurological comorbidities and keep in mind that more sleep disorders may be experienced by mothers who have medical complications during pregnancy.

Introduction: Huntington’s disease (HD) is often on the margin of standard medical practice due to its low prevalence, the lack of causal treatment, and the typically long premanifest window prior to the onset of the symptoms, which contrasts with the long-lasting burden that the disease causes in affected families. Methods: To capture these socio-psychological aspects of HD and map the experiences of affected individuals, persons at risk of HD, and caregivers, we created a questionnaire using a qualitative research approach. The questionnaire containing 16 questions was conducted online for a period of three months through patient associations in Slovakia and their infrastructures. Results: In total, we received 30 responses. The survey results, in particular, indicate insufficient counselling by physicians with explicitly missing information about the possibility of preimplantation genetic diagnostic. There was also a necessity to improve comprehensive social and health care in the later stages of the disease, raise awareness of the disease in the general health community, and provide more information on ongoing clinical trials. Conclusion: The psychosocial effects, as well as the burden, can be mitigated by comprehensive genetic counselling as well as reproductive and financial guidelines and subsequent therapeutic programs to actively support patients, caregivers, children, and adolescents growing up in affected families, preferably with the help of local HD community association. Limitations: We have used online data collection to reach a wider HD community, but at the same time, we are aware that the quality of the data we would obtain through face-to-face interviews would be considerably better. Therefore, future studies need to be conducted to obtain more detailed information.