Polysomnografi ved utredning av søvnlidelser

Engstrøm, Morten; Rugland, Eyvind; Heier, Mona Skard

doi:10.4045/tidsskr.12.0172

Cited by 14 publications

(3 citation statements)

References 16 publications

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“…Sleep stage classification is also called sleep stage scoring. Multiple biosignal-based polysomnography (PSG) for artificial sleep staging is the classical standard, which can be used to diagnose typical sleep disorders such as sleep apnea, episodic sleep sickness, and sleepwalking [16]. However, the expensive cost of PSG has prompted researchers to find an alternative solution to sleep staging, and single-channel EEG sleep staging may bring costs down.…”

Section: Related Workmentioning

confidence: 99%

RLSF: Multimodal Sleep Improvement Based Reinforcement Learning

Che

Tao

et al. 2023

IEEE Access

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As informatization 3.0 accelerates the pace of people's life and work, people's happiness index and physical and mental health have become the focus of attention and research in sociology, psychology and medicine. Currently, neurological diseases represented by insomnia have become common chronic diseases. However, existing insomnia treatment methods mainly focus on drug therapy and EEG-based expert intervention, ignoring the individual variability of insomnia patients, the high cost of expert intervention, and the privacy of the user's treatment environment. Therefore, aiming at the effect of white noise lite on sleep quality, this paper proposes a time-frequency domain correlation multimodal sleep enhancement framework based on reinforcement learning (RLSF), which is a closed-loop feedback sleep improvement framework that includes hardware and software. Specifically, the individual sleep state is fed for learning through EEG sensors' input, and the agent is gradually trained to suit the sleep habit. This paper provides a reinforcement learning environment in which different agents can be deployed easily; then, we propose the Deep Net Sleep Improvement agent (DNSI agent) and the Time and Frequency-based Lightweight Sleep Improvement agent (TFLSI agent) for RLSF. Finally, the substantial experiments compare DNSI and TFLSI agent performance, and the results indicate that these two agents both have decision-making ability, and three volunteers' Pittsburgh Sleep Quality Index significantly reduces by 3-7 points within two months and the average time to sleep is reduced by 131.4 seconds.

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Section: Related Workmentioning

confidence: 99%

RLSF: Multimodal Sleep Improvement Based Reinforcement Learning

Che

Tao

et al. 2023

IEEE Access

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“…The gold standard for sleep evaluation is polysomnography (25–29), but few studies using this have been performed in CH patients (11,23). The aim of the present study was to investigate whether CPAP could be helpful in CCH patients.…”

Section: Introductionmentioning

confidence: 99%

Continuous positive airway pressure in cluster headache: A randomized, placebo-controlled, triple-blind, crossover study

et al. 2023

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Background Oxygen inhalation aborts cluster headache attacks, and case reports show the effect of continuous positive airway pressure. The aim of this study was to investigate the prophylactic effect of continuous positive airway pressure in chronic cluster headache. Methods This was a randomized placebo-controlled triple-blind crossover study using active and sham continuous positive airway pressure treatment for chronic cluster headache. Patients entered a one month’s baseline period before randomly being assigned to two months’ active continuous positive airway pressure treatment followed by a four weeks’ washout period and two months’ sham continuous positive airway pressure or vice versa. Primary outcome measure was number of cluster headache attacks/week. Results Of the 30 included participants (12 males, median age 49.5 years, min-max 20–66 years), 25 completed both treatment/sham cycles (two discontinued, three lost to follow-up). The median number of cluster headache attacks per week was reduced from 8.25 (0.75–89.75) attacks to 6.25 (0–56.00) attacks for active continuous positive airway pressure and to 7.50 (0.50–43.75) attacks for sham continuous positive airway pressure, but there was no difference in active versus sham (p = 0.904). One patient had a serious adverse event during active treatment, none occurred during sham treatment. Conclusions Continuous positive airway pressure treatment did not reduce the number of cluster headache attacks compared to sham treatment in chronic cluster headache patients. Trial registration Clinicaltrials.gov NCT03397563

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“…In previous studies, the diagnosis and evaluation criteria of OSAS were apnea hypopnea index (AHI) and minimum oxygen saturation (LsaO 2 ). AHI reflected the number of apnea times during night sleep, while the minimum oxygen saturation reflected the most serious hypoxic event [8]. Patients usually need plenty of time to complete this test, which made it difficult to know whether the patients had OSAS.…”

Section: Introductionmentioning

confidence: 99%

Association between Cardiopulmonary Exercise Test and severity of obstructive sleep apnea syndrome

Wang,

Zhang,

Jiang

et al. 2023

IJDDP

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Review Association between Cardiopulmonary Exercise Test and Severity of Obstructive Sleep Apnea Syndrome Jianyu Wang 1,2, * , Shishi Zhang 2, Luying Jiang 3, Chunran Zhang 3, and Houjuan Zuo 2 1 Tianyou Hospital, Wuhan University of Science and TechnologyWuhan430064China 2 Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhan430030China 3 The 3rd Department of Cardiology, The First Affiliated Hospital of The Medical College, Shihezi UniversityShihezi832008China * Correspondence: Wangjianyu1003@163.com Received: 17 April 2023 Accepted: 6 June 2023 Published: 27 December 2023 Abstract: Obstructive sleep apnea syndrome (OSAS) is becoming widespread, especially in people with obesity. As it is usually measured by polysomnography (PSG), the role of cardiopulmonary exercise test (CPET), a new exercise capacity test, has not attracted enough attention in OSAS research. In this article, we explore the relationship between CPET results and patients with OSAS. 68 individuals were recruited and divided into three groups: negative/mild group (n = 22, apnea hypopnea index (AHI) < 15), moderate group (n = 22, 15 ≤ AHI < 30) and severe group (n = 24, AHI ≥ 30). Clinical parameters, cardiopulmonary exercise test (CPET) indexes, and apnea hypopnea index (AHI) were compared among the three groups. A multivariate analysis was carried out to assess which factors determine the index of AHI. The ANOVA analyses were used to evaluate the difference among the three groups. Receiver operating characteristic analyses were chosen to detect the prediction efficiency of the CPET index for AHI. The predictive power of using the CPET index (VO2/kg peak) combined with the body mass index (BMI) of patients in the detection of AHI was significantly better when compared with using the CPET index only. There was a notable correlation between VO2 and the index of AHI (r = 0.249, P = 0.04). Also, the CPET data (VO2/kg peak) combined with the BMI of patients has powerful predictive value for the severity of OSAS in patients. It is expected to be a promising way to predict the severity of OSAS in the future. Our study provides a new strategy for predicting whether a patient is in the early phase OSAS.

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Polysomnografi ved utredning av søvnlidelser

Cited by 14 publications

References 16 publications

RLSF: Multimodal Sleep Improvement Based Reinforcement Learning

RLSF: Multimodal Sleep Improvement Based Reinforcement Learning

Continuous positive airway pressure in cluster headache: A randomized, placebo-controlled, triple-blind, crossover study

Association between Cardiopulmonary Exercise Test and severity of obstructive sleep apnea syndrome

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