bluuebunny/crossref_metadata_2025_split huggingface dataset:
0434 Clinical Application of Computer Aided Cloud Sleep Scoring System
Authors: Lin W , Kuo P , Liu M , Li C , Lin C , Liang S 
Published: May 2020

Abstract

Introduction
According to a survey by World Sleep Society, 45% of the population suffered from sleep disorders. The best way to diagnose these patients is to use Polysomnography (PSG), recording their physiological signals throughout the night. Mostly, sleep technologists manually score sleep stages. Manual scoring is quite subjective and time-consuming. Although the technologist’s judgments are based on scoring standards of the American Academy of Sleep Medicine, fine-tuning scoring results because of different considerations in different sleep centers may be happened. In order to assess the consistency of scoring standards in sleep technologists, we tried to establish a cloud sleep scoring system and evaluate its feasibility in 4 sleep centers in southern Taiwan.

Methods
We constructed a computer-aided cloud sleep scoring system. Each sleep technologist could score the same test data of PSG online without being restricted by places and hardware equipment. After comparing scoring results of all participants, the scoring system could provide the following reports, including an overall agreement, agreement of each sleep stage and each sleep index. Besides, multi-person scoring results of each epoch with displaying physiological signals were analyzed.

Results
Seven sleep technologists from 4 hospitals in Tainan, Taiwan joined this study. Standard deviations (SDs) of each sleep stage included 2.64 in Wake stage, 6.90 in N1, 8.31 in N2, 6.87 in N3, 1.38 in REM, respectively. SDs of sleep indexes were 2.64 in sleep efficiency, 2.14 in sleep onset time, 8.35 in wake after sleep onset time, 10.03 in total sleep time, individually. The overall agreement was 89.6%. The satisfaction of this scoring system operation was 85.7%.

Conclusion
With the cloud sleep scoring system assistance, it was feasible to evaluate the scoring consistency among sleep technologists in different sleep centers.

Support
This work is supported by the Ministry of Science and Technology, Taiwan. (MOST 108-2634-F-006-012)
0452 Morlet Wavelet Transforms and Neural Networks in Polysomnography Analysis
Authors: Stretch R , Zeidler M 
Published: May 2020

Abstract

Introduction
Manually scoring polysomnograms is both time-consuming and labor-intensive. It also increases variability in care. Generating features for use as components within larger models is an important part of building highly accurate auto-scoring systems. In this study, we examined the use of time-frequency data representations in combination with a convolutional neural networks (CNN).

Methods
We used just six (6) pre-scored polysomnograms from the MrOS dataset in this analysis. Only one electroencephalography (EEG) and one electrooculography (EOG) channel were extracted from each polysomnogram and split into 30 second epochs. Visual representations of each epoch in the time-frequency domain were generated using Morlet wavelets, then divided into training and validation sets in a 4:5 distribution. We then re-trained a ResNet-50 CNN using transfer learning to classify sleep stage based on the time-frequency representations.

Results
A total of 4971 epochs were generated. Of those, 1242 epochs formed the validation set. Performance was high for identifying Stage W with an accuracy of 94.2% (295/313 epochs). However, performance for other stages was considerably lower. Stage N3 was predicted correctly in 68.0% of cases (138/203 epochs), although in 60/75 cases of misclassification the predicted class was Stage N2. Similarly, Stage N2 was predicted correctly in 62.0% of cases (183/295 epochs), and in 63/112 cases of misclassification the predicted class was Stage N3. Accuracy for Stage REM was 64.9%. Stage N1 prediction was poor (22.0% accuracy), likely due to insufficient representation in the sample (&lt; 10% of epochs).

Conclusion
This exploratory analysis of the use of time-frequency representations in conjunction with a CNN demonstrates some promise, especially with respect to prediction of Stage W using this technique. Inclusion of additional data channels and larger sample size would likely improve accuracy.

Support
RS - ASPIRE Fellowship (sponsored by the American Thoracic Association).
0455 Role of Interaction Between Anterior Insula Response to Sleep-Related Pictures and Stress Levels on Sleep Disturbance
Authors: Lee M , Lee K , Lee H , Kim N , Jeon J , Jeon S , Oh S , Kim S , Kim S , Lee Y 
Published: May 2020

Abstract

Introduction
Literature suggests that stress may play an important role in sleep disturbance. Individuals with higher stress levels often showed hyperarousal to stressful events, possibly leading to sleep disturbance. Hyperarousal is also one of features of stress-related sleep disturbance. Here, we examined the extent to which stress levels interact with neural activity in response to sleep-related information to predict sleep disturbance.

Methods
Forty eight healthy adults (26 females, age = 35.7 ± 10.5) without sleep disorders based on nocturnal polysomnography participated in this study. They were viewing sleep-related pictures (e.g., bedroom and sunset) and non-sleep related, neutral pictures (e.g., kitchen and landscape) during fMRI scanning. They also completed questionnaires assessing stress levels and sleep disturbance using Life Experience Survey (LES) and Pittsburgh Sleep Quality Index (PSQI), respectively. Activity in response to sleep-related pictures compared to neutral pictures was extracted from our region-of-interest (ROI), the anterior insula, and entered into our moderation models. The SPSS macro PROCESS (Hayes, 2013) was used to conduct moderation analyses. Given a significant correlation between age and PSQI scores, age was included as a covariate.

Results
Our moderation analyses showed that interactions between stress levels and anterior insula response to sleep-related pictures significantly predicted sleep disturbance. Simple slope analyses showed that at higher anterior insula response, higher stress levels were associated with greater sleep disturbance, but at lower anterior insula response, stress was not significantly associated with sleep disturbance. These results indicate that individuals with high levels of stress were more likely to experience sleep disturbance if they showed greater anterior insula response to sleep-related pictures (i.e., hyperarousal in response to sleep-related information).

Conclusion
The current findings suggest that interactions between stress levels and neural substrates of hyperarousal, particularly the anterior insula, may play a critical role in sleep disturbance.

Support
Brain Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (Study No.: 2016M3C7A1904338 and NRF-2018R1D1A1B07049704).
0456 Natural History of Insomnia: Sleep Reactivity Predicts New-Onset Acute Insomnia
Authors: Vargas I , Drake C , Muench A , Boyle J , Morales K , Grandner M , Ellis J , Perlis M 
Published: May 2020

Abstract

Introduction
Greater vulnerability to stress-related sleep disturbance (i.e., sleep reactivity) is a risk factor for chronic insomnia (CI). What has not been investigated is whether greater sleep reactivity, as assessed by the Ford Insomnia Response to Stress Test (FIRST), predicts the onset of acute insomnia (AI), and more, whether greater sleep reactivity predicts the transition from AI to CI.

Methods
A national cohort of 1,222 good sleeper subjects (68% female; mean age=53.2 years) were prospectively assessed to estimate the incidence of AI and CI. The FIRST was completed at baseline and sleep diaries were completed on a daily basis for a period of one year. Subjects were categorized based on their FIRST scores (high, FIRST&gt;16; low, FIRST≤16). Subjects were also grouped based on whether they developed AI (two consecutive weeks with a frequency of ≥ 3 nights per week of sleep initiation or maintenance problems) or maintained good sleep (GS; n=896). For those subjects that transitioned to AI (n=326), they were also grouped based on whether or not they developed CI (insomnia ≥ 3 nights/week for at least three months; n=23). Chi-square analyses were performed to determine if higher FIRST scores at baseline predicted the incidence of AI or CI.

Results
32.5% of subjects in the high FIRST group met criteria for AI at some point during the one-year interval, whereas 22.5% of subjects in the low FIRST group experienced AI (χ 2=15.2, p&lt;.001). In contrast, FIRST did not predict CI status (low FIRST, 8.5% CI, high FIRST, 5.6% CI; χ 2=1.1, p=.30).

Conclusion
Greater sleep reactivity predicted incident AI but not the onset of CI. While these findings suggest that sleep reactivity may be a predisposing factor for AI, data are not consistent with previous findings showing FIRST scores are predictive of the development of CI. It’s possible that the present study was underpowered to detect these differences, given that the incidence of CI was low (less than 2% of the total sample). Additional analyses are ongoing to evaluate the temporal association between stressful life events and AI in subjects with high and low FIRST scores.

Support
Perlis: NIH R01AG041783, K24AG055602
0468 A Multiple Dose, Placebo-Controlled, Randomized Double-Blind, Multicenter Study to Investigate Triprolidine in the Treatment of Temporary Sleep Disturbance
Authors: Reyner L , Miller J E , Shea T 
Published: May 2020

Abstract

Introduction
American Association of Sleep Medicine guidelines states that the primary goals of the treatment of insomnia are to improve sleep quality and related daytime function. While H1 antihistamines have sedative effects, they are associated with residual daytime sleepiness and an effective dose range for hypnotic effect has hitherto not been established. Triprolidine a first generation antihistamine used to treat allergic rhinitis and the common cold has a mean half-life of 3.2 hours. We evaluated the effect of two doses of triprolodine compared with placebo on sleep onset latency and daytime sleepiness to determine the optimum dose in subjects with temporary sleep disturbance.

Methods
Multicenter, placebo-controlled, parallel group, double blind, multiple dose, randomized study of 178 patients aged 18 years or above with a primary diagnosis of temporary sleep disturbance. Patients were randomized to one of three groups. Group 1: 2 x placebo tablets; Group 2: 1 x placebo tablet + 1 x 2.5mg triprolidine tablet; Group 3: 2 x 2.5mg triprolodine tablets, taken 20 minutes before intended sleep on three consecutive evenings. Efficacy was measured objectively using the Sleep Disturbance Index using a wrist actimeter and subjectively using the Loughborough Sleep Log and Karolinska Sleepiness Scale.

Results
Both doses were statistically significantly superior to placebo in terms of quality and duration of sleep and sleep interruptions. No hangover effects or daytime sleepiness were observed with either dose compared to placebo. Patients on the 2.5 mg dose awoke more refreshed than the 5 mg dose. No serious adverse effects observed in any group and anticholinergic events i.e. dry mouth were very low.

Conclusion
Tripolidine is effective and safe in the treatment of temporary sleep disturbance. The optimum dose is 2.5mg.

Support
The study was sponsored by Boots Healthcare International.
0491 Changes in Insomnia Symptoms are Associated with Improvements in Chronic Pain
Authors: Ito K , Kadotani H 
Published: May 2020

Abstract

Introduction
Depression and anxiety is known to associated with insomnia. Chronic pain is also well known to be associated with insomnia. However, relationship among insomnia and anxiety and depression is not well understood. Here We conducted a retrospective cohort study whether improvement insomnia could affect chronic pain release.

Methods
Patients with chronic pain suffering for more than three months, who first visited in our pain management clinic in Shiga University of Medical Science, Otsu, Japan during 09/25/2013-01/26/2017, were included in this study. Patients were asked to complete the same questionnaire at their first visit and six-month later.We designate pain release as 30% and over improvement in numerical rating scale (NRS) for pain release.We define improvement insomnia as subjects with Athene insomnia scale (AIS) ≥6 at the first visit became AIS &lt;6 six-month later.We also evaluated patients’ psychotic conditions by Questionnaires for anxiety and depression: HADS (Anxiety and depression) and PCS (Pain Catastrophizing score) for negative cognition, respectively. We conducted logistic regression analysis as for dependent variable was pain release.

Results
Characteristics data: n=47, mean age: 53.53±15.45, male rate: 45%, BMI: 22.81±3.49, NRS: 5.45±1.85, AIS: 8.94±2.73, HADSA: 6.04±2.57, HADSD: 6.06±2.64, PCS: 30.70±8.70, housemate rate: 81%, more than junior college graduated level: 53%, employment rate: 62%, annual income over 4oo million yen /year: 62%, antidepressant use; 26%, benzodiazepine use:11%.Logistic Regression odds ratio (OR) with 95% confidence intervals (95%CI) for relationship of improvement insomnia and not with chronic pain were unadjusted OR: 3.600 (95%CI: 1.007-12.865) and adjusted OR: 3.078 (95%CI 0.779-12.161) (adjust for PCS.)

Conclusion
We showed improvement in insomnia can affect pain release in the pain therapy, and PCS improvement may be also association with chronic pain release.

Support
0497 Rebound Insomnia During Discontinuation of Chronic Hypnotic Use
Authors: Koshorek G , Verkler J , Roth T , Roehrs T 
Published: May 2020

Abstract

Introduction
Rebound insomnia refers to worsened sleep relative to baseline on 1-2 nights after discontinuation of active hypnotic medication. Rebound is typically assessed using a placebo substitution. We assessed rebound in an on-going “blinded” clinical trial in which people with insomnia are instructed to discontinue their study medication (i.e., no-pill) after 6 months of nightly use.

Methods
DSM-V diagnosed people with insomnia (n=31, 26 females), aged 26-61 yrs, with a polysomnographic sleep efficiency of ≤85%, no other sleep disorders, unstable medical or psychiatric diseases or drug dependency completed the clinical trial. Participants were randomized to zolpidem XR (12.5 mg), eszopiclone (3 mg) or placebo nightly for 6 months (blinded groups A: n=11, B: n=9, C: n=11). After 6 months, over a 2-week choice period, they were given the instruction to discontinue their nightly hypnotic use with an opportunity, if necessary, to self-administer either 1, 2, or 3 capsules of their assigned medication (zolpidem XR 6.25 mg, 6.25 mg, placebo; eszopiclone 2 mg, 1 mg, placebo as capsules 1, 2 and 3 respectively; or 3 placebos). On baseline and the14 discontinuation nights, sleep was recorded and scored by actigraphy for sleep efficiency (SE), sleep latency (LAT) and wake after sleep onset (WASO).

Results
Relative to the baseline night, on the first discontinuation night there was no difference in SE, LAT, and WASO. Fifteen subjects stopped taking study medication when told to discontinue and 16 subjects took study medication on one night or more. While not differing on baseline or night 1, on night 14 the last study night the medication users had a lower SE (75.9 vs 87.7 %, p&lt;.0.004) and a longer LAT (61.5 vs 14.5 min, p&lt;0.05).

Conclusion
Difficulty discontinuing hypnotic use is not specifically related to rebound insomnia. We reported in a companion abstract those with insomnia and hyperarousal, defined by MSLT, are those with difficulty discontinuing hypnotic use and as shown here slept poorly on the last study night.

Support
NIDA, grant#: R01DA038177 awarded to Dr. Roehrs
0505 Insomnia: Entrapment of Binaural Auditory Beats on Subjects with Insomnia Symptoms
Authors: Choi H , Bang Y , Yoon I 
Published: May 2020

Abstract

Introduction
It has been reported that binaural beat stimulation, which has two different frequencies on both ears, is effective in increasing alertness, memory, reducing anxiety, and controlling mood. This study aims to clarify the brain wave entrainment effect of binaural beat and to identify the mechanism of action of the binaural beat.

Methods
Subjects with subclinical insomnia symptoms between 20 and 59 years of age were recruited from the community. Quantitative electroencephalography (EEG) was measured two times before and after the 2 weeks of binaural beat intervention period. An audio apparatus without the distortion of a sound source is set with theta (6 Hz) binaural beat. Participants used the apparatus for 30 minutes before going to bed for 2 weeks.

Results
When the music with binaural beat was played, the relative power of theta frequency increased (occipital, P=0.009). When the music only was played in the laboratory, the relative power of delta (temporal, P=0.004; parietal, P=0.005; occipital, P=0.006) and theta frequency (temporal, P=0.004; central, P=0.001; parietal, P=0.001; occipital, P=0.003) increased and the relative power of alpha decreased (frontal, P=0.008; temporal, P=0.012; central, P=0.008; parietal, P=0,004; occipital, P=0.005). After listening to music with binaural beat for two weeks, the difference of beta power before and after listening to music first in the laboratory was lower than the difference after using music-only devices (P=0.008).

Conclusion
When the binaural beat was played, the entrapment of theta wave appeared. And the music was presumed to have a nonspecific relaxation effect. After exposure to music with binaural beat for 2 weeks, beta power decreased compared to exposure to music alone. Continuous music with binaural beat exposure for 2 weeks is likely to reduce hyper-arousal state and contribute to sleep induction.

Support
None
0509 Use of Blinded Hypnotic Tapering for Hypnotic Discontinuation
Authors: Edinger J D , Walmboldt F , Holm K , Johnson R L , Simmons B , Tsai S , Morin C 
Published: May 2020

Abstract

Introduction
Many patients have difficulties achieving hypnotic discontinuation due to anxiety that arises when they knowingly reduce their hypnotic dose or withhold it entirely. This study tested a blinded tapering approach to reduce patients’ anxiety and help them discontinue their hypnotics.

Methods
The study sample included 78 (M age = 55.2 ± 12.8 yrs.; 65.4% women) users of benzodiazepine and benzodiazepine receptor agonists. Following baseline assessments, enrollees first completed 4 sessions of cognitive behavioral insomnia therapy (CBTI). Subsequently they were randomized to one of three 20-week, double-blinded tapering protocols wherein their medication dosage either remained unchanged (CTRL) or was reduced by 25% or 10% every two weeks. At the end of the 20-week period the study blind was eliminated and those who completed one of the two blinded tapering protocols entered a 3-month follow-up period, whereas CTRL participants were offered an open label taper before completing the follow-up.

Results
Among those who completed one of the blinded tapering protocols, 92.9% totally discontinued their medication use by the end of the 20-week tapering phase, whereas 77.3% in the CTRL group discontinued hypnotic use by the end of their open label tapering. At follow-up 72.1% of those who completed blinded tapering remained medication free whereas only 52% of those who underwent open-label tapering remained medication free. Comparisons at follow-up showed those who received the open-label taper continued to use hypnotics on average 2-3 nights/week compared to about 1 time every other week for the blinded taper group (p = .05). The average weekly diazepam equivalent dose of medication used by the open label tapering group was about 5 times higher than the average weekly dose used by the blind tapering group (p = .025).

Conclusion
CBTI combined with blinded hypnotic tapering is a promising treatment approach for helping hypnotic users overcome their medication dependence.

Support
National Institute of Drug Abuse, Grant # R34 DA042329-01
0520 Treating Sleep Maintenance Insomnia Means Treating Depression?
Authors: Batta B , Szakacs Z 
Published: May 2020

Abstract

Introduction
Rumination whilst awakening nightly might be a common symptom in fragmented sleep and depression as well. The problem is that the test results on BECK or MMPI DEPRESSION SCALE often do not reach the diagnostic criteria of depression on DSM-V, only the symptoms of worry, rumination and sleep maintenance problems appear as mild depressive signs. The aim of our study was to investigate the assumption to which treating everyday worries through CBT methods would affect the amount and severity of nightly rumination and the severity of depressive mood as well.

Methods
33 adult patients (M=39,55 SD=10,66) diagnosed with insomnia with the criteria of only sleep maintenance proble. Each participants filled the PSQI, the Epworth Sleepiness Scale, the Beck Depression Inventory and the Trait-Anxiety Inventory before the beginning of the CBT therapy session and 10 sessions later.

Results
Our results show a significant effect of the CBT therapy to sleep maintenance symptoms through treating everyday ruminations opposite to treating only nightly ruminations on sleeping quality (F(1, 31)= 55,358 p&lt;0,01 η2 = 0,641). The significant improvement in sleep quality only were shown in the subgroup of everyday rumination CBT group (N=17, M=7,88 SD=2,57), but not in the subgroup of nightly ruminations CBT group (N=16, M=9,87, SD=1,92).

Conclusion
Based on our preliminary findings, treating everyday worries with CBT therapy affect sleep maintenance insomnia, while treating only nightly ruminations and worries of sleeping difficulties would not improve sleeping problems and depressive mood.

Support
No