Evaluating the sleep quality of obstructive sleep apnea patients after continuous positive airway pressure treatment

Update on Research and Practices in Major Sleep Disorders: Part I. Obstructive Sleep Apnea Syndrome

PURPOSE

The purpose of this first of two review articles providing an update on sleep disorders was to examine the pathophysiology, epidemiology, and treatment of obstructive sleep apnea (OSA). OSA is a common sleep disorder whose prevalence is similar to asthma. As with other sleep disorders, OSA has a broad impact on individuals, affecting their daily behaviors, cognitive abilities, and performance, and putting them at increased risk for accidents, mood disorders, cancer, cardiovascular disease, and hypertension. Thus, early recognition and management, much of which can be implemented by nurses, can reduce health and accident risks and improve daily functioning.

METHODS

This narrative review utilized medical databases such as PubMed to identify relevant English language original and systematic review articles predominantly from peer-reviewed journals from 2012 to 2018. However, as background, findings from classic articles prior to 2012 were also included.

CLINICAL RELEVANCE

OSA is a common condition with considerable impact on daily functioning and potential for accidents and serious comorbidities such as hypertension, cardiovascular disease, diabetes, and depressed mood. The impairments and comorbidities associated with OSA can be reduced through early detection, encouraging treatment, providing education about sleep and OSA, and, importantly, promoting adherence to the predominant therapy, positive airway pressure.

Comparative analysis of different characteristics of automatic sleep stages

BACKGROUND AND OBJECTIVE

With the acceleration of social rhythm and the increase of pressure, there are various sleep problems among people. Sleep staging is an important basis for the diagnosis of sleep disorders and other related diseases. The process of automatic staging of sleep is mainly divided into three core steps: data preprocessing, feature extraction, and classification. Accurate analysis of the features of sleep electroencephalogram (EEG) signals is not only helpful to improve the accuracy of sleep staging, but also help people to understand their sleep status.

METHODS

This paper focuses on the analysis of EEG features during sleep staging, and reviews many feature extraction methods and classification methods for sleep staging and sums up these algorithms used in literatures and its staging results. Besides, this paper lists a total of 22 features based on time domain, time-frequency, and nonlinear analysis methods, including kurtosis, skewness, Hjorth parameters, and standard deviations, wavelets energy; sample entropy (SampEn), fuzzy entropy, Tsallis entropy, fractal dimension (FD), complexity. The data set comes from EDF database. Wavelet transform (WT) and support vector machine (SVM) are used to achieve the sleep staging based on single-channel EEG signal. And the characteristic feature data was analyzed by ANOVA.

RESULTS

By comparison, the SampEn, fuzzy entropy, FD and complexity can achieve ideal sleep staging. The highest accuracy of sleep staging is 85.93%. The FD and complexity are simpler than the entropy value, but their accuracies are lower. Furthermore, the distribution of these methods in different sleep period is more significant than others, which is content with the results of sleep staging.

CONCLUSION

In a word, due to the non-stationary and non-linear characteristics of EEG signals, time domain and time-frequency analysis methods all have some limitations. Nonlinear analysis was more effective and practical for the analysis of sleep EEG.

Effectiveness of behavioral and psychosocial interventions for continuous positive airway pressure adherence in obstructive sleep apnea patients: a quantitative systematic review protocol

REVIEW QUESTION/OBJECTIVE

The objective of this review is to assess the effectiveness of behavioral and psychosocial interventions on adherence to continuous positive airway pressure among patients 18 years and over with obstructive sleep apnea.

Analyze the beta waves of electroencephalogram signals from young musicians and non-musicians in major scale working memory task

Electroencephalograms can record wave variations in any brain activity. Beta waves are produced when an external stimulus induces logical thinking, computation, and reasoning during consciousness. This work uses the beta wave of major scale working memory N-back tasks to analyze the differences between young musicians and non-musicians. After the feature analysis uses signal filtering, Hilbert-Huang transformation, and feature extraction methods to identify differences, k-means clustering algorithm are used to group them into different clusters. The results of feature analysis showed that beta waves significantly differ between young musicians and non-musicians from the low memory load of working memory task.

Effect of adherence on daytime sleepiness, fatigue, depression and sleep quality in the obstructive sleep apnea/hypopnea syndrome patients undertaking nasal continuous positive airway pressure therapy

OBJECTIVES

The aims of this study were to describe changes in day- and nighttime symptoms and the adherence to nasal continuous positive airway pressure (nCPAP) during the first 3-month nCPAP therapy among newly diagnosed patients with obstructive sleep apnea/hypopnea syndrome (OSAS) and to identify the effect of adherence on the changes in day- and nighttime symptoms during the first 3 months.

METHODS

Newly diagnosed OSAS patients were consecutively recruited from March to August 2013. Baseline clinical information and measures of the Epworth Sleepiness Scale (ESS), Fatigue Severity Scale (FSS), Zung's Self-Rating Depression Scale (SDS) and the Pittsburgh Sleep Quality Index (PSQI) at baseline and the end of 3rd, 6th, 9th and 12th week of therapy were collected. Twelve weeks' adherence was calculated as the average of each 3-week period. Mixed model was used to explore the effect of adherence to nCPAP therapy on ESS, FSS, SDS and PSQI in each 3-week phase.

RESULTS

Seventy-six patients completed the 12-week follow-up. The mixed-effects models showed that under the control of therapy phase adherence in the range of <4 hours per night, using nCPAP could independently improve daytime sleepiness, in terms of ESS (coefficient, [95% confidence interval] unit; -4.49 [-5.62, -3.36]). Adherence at 4-6 hours per night could independently improve all variables of day- and nighttime symptoms included in this study, namely ESS -6.69 (-7.40, -5.99), FSS -6.02 (-7.14, -4.91), SDS -2.40 (-2.95, -1.85) and PSQI -0.20 (-0.52, -0.12). Further improvement in symptoms could be achieved at ≥6 hours per night using nCPAP, which was ESS -8.35 (-9.26, -7.44), FSS -10.30 (-11.78, -8.83), SDS -4.42 (-5.15, -3.68) and PSQI -0.40 (-0.82, -0.02). The interaction between adherence level and therapy phase was not significant in day- and nighttime symptoms.

CONCLUSION

The effect of adherence on the above-mentioned symptoms is stable through the first 3 months. Under the control of therapy phase, the nCPAP therapy effectively improves day- and nighttime symptoms with ≥4 hours adherence, and the patients can achieve a further improvement with ≥6 hours adherence.