Effect of Continuous Positive Airway Pressure on Symptoms and Prevalence of Insomnia in Patients With Obstructive Sleep Apnea: A Longitudinal Study

Objective: Obstructive sleep apnea (OSA) and insomnia are the two most common sleep disorders. Continuous positive airway pressure (CPAP) is considered first-line treatment for OSA. In the present study, we assess the effect of CPAP on symptoms and prevalence of insomnia in patients with OSA. We hypothesized a decrease in insomnia symptoms from CPAP initiation to follow-up, and that this decrease would depend on CPAP adherence. Materials and methods: The sample included 442 patients diagnosed with OSA [mean age 54.9 years (SD = 12.1), 74.4% males] who started treatment with CPAP at a university hospital. OSA was diagnosed according to standard respiratory polygraphy. Mean apnea-hypopnea index (AHI) was 30.1 (SD = 21.1) at baseline. Insomnia was assessed prior to CPAP treatment (baseline) and at follow-up after a median of 19.9 weeks (range 6–52 weeks) with the Bergen Insomnia Scale (BIS). CPAP adherence was defined as an average use of ≥ 4 h per night, whereas non-adherence was defined as < 4 h per night. Results: There was a significant decrease in BIS scores from baseline (mean = 18.8, SD = 9.8) to follow-up (mean = 12.9, SD = 9.9), p < 0.001. Cohen’s d(0.65) indicated a moderate effect size. The reduction in BIS scores was depending on CPAP adherence (interaction effect F(1,440) = 12.4, p < 0.001), with larger reduction in the adherent group than in the non-adherent group. The proportion of patients with chronic insomnia was significantly reduced from 51.1% at baseline to 33.0% at follow-up (p < 0.001). Conclusion: Overall, there was a significant reduction in insomnia symptoms from baseline to follow-up. The improvement was significant in both adherence groups, but the degree of improvement was larger among patients who were adherent to CPAP. Furthermore, there was a significant reduction in the prevalence of chronic insomnia at follow-up compared to baseline. This suggests that CPAP effectively reduces both the presence of insomnia and the severity of insomnia symptoms in some patients with OSA.publishedVersio

Regularized linear discriminant analysis of EEG features in dementia patients

The present study explores if EEG spectral parameters can discriminate between healthy elderly controls (HC), Alzheimer’s disease (AD) and vascular dementia (VaD) using. We considered EEG data recorded during normal clinical routine with 114 healthy controls (HC), 114 AD, and 114 VaD patients. The spectral features extracted from the EEG were the absolute delta power, decay from lower to higher frequencies, amplitude, center and dispersion of the alpha power and baseline power of the entire frequency spectrum. For discrimination, we submitted these EEG features to regularized linear discriminant analysis algorithm with a 10-fold cross-validation. To check the consistency of the results obtained by our classifiers, we applied bootstrap statistics. Four binary classifiers were used to discriminate HC from AD, HC from VaD, AD from VaD, and HC from dementia patients (AD or VaD). For each model, we measured the discrimination performance using the area under curve (AUC) and the accuracy of the cross-validation (cv-ACC). We applied this procedure using two different sets of predictors. The first set considered all the features extracted from the 22 channels. For the second set of features, we automatically rejected features poorly correlated with their labels. Fairly good results were obtained when discriminating HC from dementia patients with AD or VaD (AUC = 0.84). We also obtained AUC = 0.74 for discrimination of AD from HC, AUC = 0.77 for discrimination of VaD from HC, and finally AUC = 0.61 for discrimination of AD from VaD. Our models were able to separate HC from dementia patients, and also and to discriminate AD from VaD above chance. Our results suggest that these features may be relevant for the clinical assessment of patients with dementia

Visual EEG reviewing times with SCORE EEG

Objective: Visual EEG analysis is the gold standard for clinical EEG interpretation and analysis, but there is no published data on how long it takes to review and report an EEG in clinical routine. Estimates of reporting times may inform workforce planning and automation initiatives for EEG. The SCORE standard has recently been adopted to standardize clinical EEG reporting, but concern has been expressed about the time spent reporting. Methods: Elapsed times were extracted from 5889 standard and sleep-deprived EEGs reported between 2015 and 2017 reported using the SCORE EEG software. Results: The median review time for standard EEG was 12.5 min, and for sleep deprived EEG 20.9 min. A normal standard EEG had a median review time of 8.3 min. Abnormal EEGs took longer than normal EEGs to review, and had more variable review times. 99% of EEGs were reported within 24 h of end of recording. Review times declined by 25% during the study period. Conclusion: Standard and sleep-deprived EEG review and reporting times with SCORE EEG are reasonable, increasing with increasing EEG complexity and decreasing with experience. EEG reports can be provided within 24 h. Significance: Clinical standard and sleep-deprived EEG reporting with SCORE EEG has acceptable reporting times. Keywords: EEG reporting, EEG review, EEG workload, EEG review time, SCORE EE

Effect of Continuous Positive Airway Pressure on Symptoms and Prevalence of Insomnia in Patients With Obstructive Sleep Apnea: A Longitudinal Study

Objective: Obstructive sleep apnea (OSA) and insomnia are the two most common sleep disorders. Continuous positive airway pressure (CPAP) is considered first-line treatment for OSA. In the present study, we assess the effect of CPAP on symptoms and prevalence of insomnia in patients with OSA. We hypothesized a decrease in insomnia symptoms from CPAP initiation to follow-up, and that this decrease would depend on CPAP adherence. Materials and methods: The sample included 442 patients diagnosed with OSA [mean age 54.9 years (SD = 12.1), 74.4% males] who started treatment with CPAP at a university hospital. OSA was diagnosed according to standard respiratory polygraphy. Mean apnea-hypopnea index (AHI) was 30.1 (SD = 21.1) at baseline. Insomnia was assessed prior to CPAP treatment (baseline) and at follow-up after a median of 19.9 weeks (range 6–52 weeks) with the Bergen Insomnia Scale (BIS). CPAP adherence was defined as an average use of ≥ 4 h per night, whereas non-adherence was defined as < 4 h per night. Results: There was a significant decrease in BIS scores from baseline (mean = 18.8, SD = 9.8) to follow-up (mean = 12.9, SD = 9.9), p < 0.001. Cohen’s d(0.65) indicated a moderate effect size. The reduction in BIS scores was depending on CPAP adherence (interaction effect F(1,440) = 12.4, p < 0.001), with larger reduction in the adherent group than in the non-adherent group. The proportion of patients with chronic insomnia was significantly reduced from 51.1% at baseline to 33.0% at follow-up (p < 0.001). Conclusion: Overall, there was a significant reduction in insomnia symptoms from baseline to follow-up. The improvement was significant in both adherence groups, but the degree of improvement was larger among patients who were adherent to CPAP. Furthermore, there was a significant reduction in the prevalence of chronic insomnia at follow-up compared to baseline. This suggests that CPAP effectively reduces both the presence of insomnia and the severity of insomnia symptoms in some patients with OSA

Prevalence of parasomnias in patients with obstructive sleep apnea. A registry-based cross-sectional study

Objective: To assess the prevalence of parasomnias in relation to presence and severity of obstructive sleep apnea (OSA). We hypothesized higher parasomnia prevalence with higher OSA severity. Methods: The sample comprised 4,372 patients referred to a Norwegian university hospital with suspicion of OSA (mean age 49.1 years, 69.8% males). OSA was diagnosed and categorized by standard respiratory polygraphy (type 3 portable monitor). The patients completed a comprehensive questionnaire prior to the sleep study, including questions about different parasomnias during the last 3 months. Pearson chi-square tests explored differences according to the presence and severity of OSA. Furthermore, logistic regression analyses with the parasomnias as dependent variables and OSA severity as predictor were conducted (adjusted for sex, age, marital status, smoking, and alcohol consumption). Results: In all, 34.7% had apnea-hypopnea index (AHI) <5 (no OSA), 32.5% had AHI 5-14.9 (mild OSA), 17.4% had AHI 15-29.9 (moderate OSA), and 15.3% had AHI ≥30 (severe OSA). The overall prevalence of parasomnias was 3.3% (sleepwalking), 2.5% (sleep-related violence), 3.1% (sexual acts during sleep), 1.7% (sleep-related eating), and 43.8% (nightmares). The overall parasomnia prevalence was highest in the no OSA group. In the chi-square analyses, including all OSA groups, the prevalence of sleep-related violence and nightmares were inversely associated with OSA severity, whereas none of the other parasomnias were significantly associated with OSA severity. In adjusted logistic regression analyses the odds of sleepwalking was significantly higher in severe compared to mild OSA (OR = 2.0, 95% CI = 1.12–3.55). The other parasomnias, including sleep-related violence and nightmares, were not associated with OSA presence or severity when adjusting for sex and age. Conclusions: We found no increase in parasomnias in patients with OSA compared to those not having OSA. With the exception of sleepwalking, the parasomnias were not associated with OSA severity

Regularized linear discriminant analysis of EEG features in dementia patients

The present study explores if EEG spectral parameters can discriminate between healthy elderly controls (HC), Alzheimer’s disease (AD) and vascular dementia (VaD) using. We considered EEG data recorded during normal clinical routine with 114 healthy controls (HC), 114 AD and 114 vascular dementia VaD patients. The spectral features extracted from the EEG were the absolute delta power, decay from lower to higher frequencies, amplitude, center and dispersion of the alpha power and baseline power of the entire frequency spectrum. For discrimination, we submitted these EEG features to regularized linear discriminant analysis algorithm with a 10 fold cross-validation. To check the consistency of the results obtained by our classifiers, we applied bootstrap statistics. Four binary classifiers were used to discriminate HC from AD, HC from VaD, AD from VaD and HC from dementia patients (AD or VaD). For each model, we measured the discrimination performance using the area under curve (AUC) and the accuracy of the cross-validation (cv-ACC). We applied this procedure using two different sets of predictors. The first set considered all the features extracted from the 22 channels. For the second set of features we automatically rejected features poorly correlated with their labels. Fairly good results were obtained when discriminating HC from dementia patients with AD or VaD (AUC=0.84). We also obtained AUC=0.74 for discrimination of AD from HC, AUC=0.77 for discrimination of VaD from HC and finally AUC=0.61 for discrimination of AD from VaD. Our models were able to separate healthy controls from dementia patients, and also and to discriminate AD from VaD above chance. Our results suggest that these features may be relevant for the clinical assessment of patients with dementia

Table_1_Prevalence of Parasomnias in Patients With Obstructive Sleep Apnea. A Registry-Based Cross-Sectional Study.DOCX

<p>Objective: To assess the prevalence of parasomnias in relation to presence and severity of obstructive sleep apnea (OSA). We hypothesized higher parasomnia prevalence with higher OSA severity.</p><p>Methods: The sample comprised 4,372 patients referred to a Norwegian university hospital with suspicion of OSA (mean age 49.1 years, 69.8% males). OSA was diagnosed and categorized by standard respiratory polygraphy (type 3 portable monitor). The patients completed a comprehensive questionnaire prior to the sleep study, including questions about different parasomnias during the last 3 months. Pearson chi-square tests explored differences according to the presence and severity of OSA. Furthermore, logistic regression analyses with the parasomnias as dependent variables and OSA severity as predictor were conducted (adjusted for sex, age, marital status, smoking, and alcohol consumption).</p><p>Results: In all, 34.7% had apnea-hypopnea index (AHI) <5 (no OSA), 32.5% had AHI 5-14.9 (mild OSA), 17.4% had AHI 15-29.9 (moderate OSA), and 15.3% had AHI ≥30 (severe OSA). The overall prevalence of parasomnias was 3.3% (sleepwalking), 2.5% (sleep-related violence), 3.1% (sexual acts during sleep), 1.7% (sleep-related eating), and 43.8% (nightmares). The overall parasomnia prevalence was highest in the no OSA group. In the chi-square analyses, including all OSA groups, the prevalence of sleep-related violence and nightmares were inversely associated with OSA severity, whereas none of the other parasomnias were significantly associated with OSA severity. In adjusted logistic regression analyses the odds of sleepwalking was significantly higher in severe compared to mild OSA (OR = 2.0, 95% CI = 1.12–3.55). The other parasomnias, including sleep-related violence and nightmares, were not associated with OSA presence or severity when adjusting for sex and age.</p><p>Conclusions: We found no increase in parasomnias in patients with OSA compared to those not having OSA. With the exception of sleepwalking, the parasomnias were not associated with OSA severity.</p