Polysomnography

About: Polysomnography is a research topic. Over the lifetime, 19527 publications have been published within this topic receiving 858718 citations. The topic is also known as: PSG & polysomnogram.

Self-efficacy in sleep apnea: instrument development and patient perceptions of obstructive sleep apnea risk, treatment benefit, and volition to use continuous positive airway pressure.

Abstract: Study Objectives: The purpose of this study was to evaluate the SelfEfficacy Measure for Sleep Apnea (SEMSA) designed to assess adherence-related cognitions. Design: Subjects completed the questionnaire prior to the initiation of continuous positive airway pressure (CPAP) treatment. Test-retest reliability of the instrument was evaluated by having a subset of subjects complete the SEMSA a second time at home, 1 week later, returning the questionnaire by mail. Patients: 213 subjects with newly diagnosed obstructive sleep apnea were recruited from the clinic populations of 2 sleep disorders centers. Measurements and Results: Content validity was confirmed by a panel of expert judges. Confirmatory factor analysis validated the 3 a priori subscales: risk perception, outcome expectancies, and treatment self-efficacy. The internal consistency of the total instrument was 0.92. Test-retest reliability coefficients (N=20) were estimated to be 0.68, P=0.001, for Perceived Risk; 0.77, P<<0.0001, for Outcome Expectancies; and 0.71, P=0.0005, for the Treatment Self-Efficacy subscale. Subject responses indicated that approximately half of the subjects did not perceive problems with concentration, sexual performance, sleepy driving, or an accident as related to sleep apnea. More than 60% of the subjects acknowledged most of the benefits of CPAP presented to them, but only 53% associated CPAP use with enhanced sexual performance. Frequently identified barriers to treatment use were nasal stuffiness, claustrophobia, and disturbing bed partner sleep. Conclusion: These findings indicate that the SEMSA has strong psychometric properties and has the potential for identifying patient perceptions

A Selective Review of Maternal Sleep Characteristics in the Postpartum Period

Abstract: Objective To determine the current knowledge of postpartum womens' sleep patterns, sleep disturbances, consequences of sleep disturbances, and known strategies for prevention in order to provide best practice recommendations for health care providers. Data Sources A literature search from 1969 through February 2008 was conducted using the CINHL, Index of Allied Health Literature, Ovid, PsycINFO, and PubMed electronic databases in addition to reference lists from selected articles and other key references. Search terms included sleep, postpartum, sleep deprivation, and sleep disturbance. Study Selection A critical review of all relevant articles from the data sources was conducted with attention to the needs of postpartum womens' sleep and implications for health care providers. Data Extraction Literature was reviewed and organized into groups with similar characteristics. Data Synthesis An integrative review of the literature summarized the current state of research related to sleep alterations in postpartum women. Conclusions Postpartum women experience altered sleep patterns that may lead to sleep disturbances. The most common reasons for sleep disturbances are related to newborn sleep and feeding patterns. Although present, the relationships among sleep disturbance, fatigue, and depression in postpartum women lack clarity due to their ambiguous definitions and the variety of the studies conducted. Providers should encourage prenatal education that assists the couple in developing strategies for decreasing postpartum sleep deprivation. Alterations of in-hospital care and home care should be incorporated to improve the new family's sleep patterns.

A Decision Rule for Diagnostic Testing in Obstructive Sleep Apnea

Abstract: Obstructive sleep apnea (OSA) is traditionally diagnosed using overnight polysomnography. Decision rules may provide an alternative to polysomnography. A consecutive series of patients referred to a tertiary sleep center underwent prospective evaluation with the upper airway physical examination protocol, followed by determination of the respiratory disturbance index using a portable monitor. Seventy-five patients were evaluated with the upper airway physical examination protocol. Historic predictors included age, snoring, witnessed apneas, and hypertension. Physical examination–based predictors included body mass index, neck circumference, mandibular protrusion, thyro–rami distance, sterno–mental distance, sterno–mental displacement, thyro–mental displacement, cricomental space, pharyngeal grade, Sampsoon-Young classification, and over-bite. A decision rule was developed using three predictors: a cricomental space of 1.5 cm or less, a pharyngeal grade of more than II, and the presence of overbite. In pat...

Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting

Abstract: Sleep scoring is one of the most important methods for diagnosis in psychiatry and neurology. Sleep staging is a time consuming and difficult task conducted by sleep specialists. The purposes of this work are to automatic score the sleep stages and to help to sleep physicians on sleep stage scoring. In this work, a novel data preprocessing method called k-means clustering based feature weighting (KMCFW) has been proposed and combined with k-NN (k-nearest neighbor) and decision tree classifiers to classify the EEG (electroencephalogram) sleep into six sleep stages including awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3, REM, and non-sleep (movement time). First of all, frequency domain features belonging to sleep EEG signal have been extracted using Welch spectral analysis method and composed 129 features from EEG signal relating each sleep stages. In order to decrease the features, the statistical features comprising minimum value, maximum value, standard deviation, and mean value have been used and then reduced from 129 to 4 features. In the second phase, the sleep stages dataset with four features has been weighted by means of k-means clustering based feature weighting. Finally, the weighted sleep stages have been automatically classified into six sleep stages using k-NN and C4.5 decision tree classifier. In the classification of sleep stages, the k values of 10, 20, 30, 40, 50, and 60 in k-NN classifier have been used and compared with each other. In the experimental results, while sleep stages has been classified with 55.88% success rate using k-NN classifier (for k value of 40), the weighted sleep stages with KMCFW has been recognized with 82.15% success rate k-NN classifier (for k value of 40). And also, we have investigated the relevance between sleep stages and frequency domain features belonging to EEG signal. These results have demonstrated that proposed weighting method have a considerable impact on automatic determining of sleep stages. This system could be used as an online system in the automatic scoring of sleep stages and helps to sleep physicians in the sleep scoring process.

Development and Preliminary Validation of Heart Rate and Breathing Rate Detection Using a Passive, Ballistocardiography-Based Sleep Monitoring System

Abstract: Techniques such as ballistocardiography (BCG) that can provide noninvasive long-term physiological monitoring have gained interest due to a growing recognition of adverse effects from poor sleep and sleep disorders. The noninvasive analysis of physiological signals (NAPS) system is a BCG-based monitoring system developed to measure heart rate, breathing rate, and musculoskeletal movement that shows promise as a general sleep analysis tool. Overnight sleep studies were conducted on 40 healthy subjects during a clinical trial at the University of Virginia. The NAPS system's measures of heart rate and breathing rate were compared to ECG, pulse oximetry, and respiratory inductance plethysmography (RIP). The subjects were split into a training dataset and a validation dataset, maintaining similar demographics in each set. The NAPS system accurately detected heart rate, averaged over the prescribed 30-s epochs, to within less than 2.72 beats per minute of ECG, and accurately detected breathing rate, averaged over the same epochs, to within 2.10 breaths per minute of RIP bands used in polysomnography.