Article

Overnight pulse oximetry for sleep-disordered breathing in adults - A review

Walter Reed National Military Medical Center, Washington, Washington, D.C., United States
Chest (Impact Factor: 7.48). 09/2001; 120(2):625-33. DOI: 10.1378/chest.120.2.625
Source: PubMed

ABSTRACT

Pulse oximetry is a well-established tool routinely used in many settings of modern medicine to determine a patient's arterial oxygen saturation and heart rate. The decreasing size of pulse oximeters over recent years has broadened their spectrum of use. For diagnosis and treatment of sleep-disordered breathing, overnight pulse oximetry helps determine the severity of disease and is used as an economical means to detect sleep apnea. In this article, we outline the clinical utility and economical benefit of overnight pulse oximetry in sleep and breathing disorders in adults and highlight the controversies regarding its limitations as presented in published studies.

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Available from: Arn Eliasson, Jan 02, 2014
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    • "In this regard the most widely used approaches relied mostly on the application of different cut-offs over the computed number of oxygen saturation dips (oxygen desaturations) per hour of time, i.e. so-called Oxygen Desaturation Index (ODI). Other applications based on the cumulative time spent below certain saturation value can be found [5]. Actually a problem with these approaches has to do with the absence of a consensus to establish the appropriate thresholds. "
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    ABSTRACT: Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine, and the costs associated to its manual diagnosis. The increment and heterogeneity of the different techniques, however, makes somewhat difficult to adequately follow recent developments. In this paper an overview within the area of computer-assisted diagnosis of SAHS has been performed. This overview of the different methods is presented together with a critical discussion of the current state-of-the-art.
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    • "Time spent below 90% Netzer et al. [16] Expert systems Daniels et al. [17] Delta-index Lévy et al. [18], Olson et al. [19], and Magalang et al. [20] Spectral analysis Zamarrón et al. [21] [22], Hua and Yu [23], and Morillo et al. [24] "
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    ABSTRACT: Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine and the costs associated with its manual diagnosis. The increment and heterogeneity of the different techniques, however, make it somewhat difficult to adequately follow the recent developments. A literature review within the area of computer-assisted diagnosis of SAHS has been performed comprising the last 15 years of research in the field. Screening approaches, methods for the detection and classification of respiratory events, comprehensive diagnostic systems, and an outline of current commercial approaches are reviewed. An overview of the different methods is presented together with validation analysis and critical discussion of the current state of the art.
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    • "PSG is treated as the gold standard for the diagnosis of OSA; however, it has several limitations, such as technical expertise is required and timely access is restricted [11]. Thus, home pulse oximetry has been proposed as a valuable and effective tool for screening patients with OSA; nonetheless, it's efficacy in OSA diagnosis has been debated for several years [12]. Recently, a comprehensive evaluation of representative oxyhemoglobin indices for predicting severity of OSA was reported [13]. "
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    ABSTRACT: Obstructive sleep apnea (OSA) is a general sleep disorder and is a significant cause of motor vehicle crashes and chronic diseases. The severity of the respiratory events is measured by the frequency and duration of apneas and hypopneas per hour of sleep, namely apnea-hypopnea index (AHI), using polysomnography (PSG). Suspected patients can be classified as normal (AHI<5), mild (5≤AHI<15), moderate (15≤AHI<30), and severe (AHI≥30). Although PSG is treated as the gold standard for the diagnosis of OSA, its shortcoming includes technical expertise is required and timely access is restricted. Thus, home pulse oximetry has been proposed as a valuable and effective tool for screening patients with OSA. Support vector machine (SVM) is believed to be more efficient than neural network and traditional statistical-based classifiers. Nonetheless, it is critical to determine suitable parameters to increase classification performance. Furthermore, an ensemble of SVM classifiers use multiple models to obtain better predictive accuracy and are more stable than models consist of a single model. Genetic algorithm (GA), on the other hand, is able to find optimal solution within an acceptable time, and is faster than dynamic programming with exhaustive searching strategy. By taking the advantage of GA in quickly selecting the salient features and adjusting SVM parameters, it was combined with ensemble SVM to design a clinical decision support system (CDSS) for the diagnosis of patients with severe OSA, and then followed by PSG to further discriminate normal mild and moderate patients. The results show that ensemble SVM classifiers demonstrate better diagnosing performance than models consisting of a single SVM model and logistic regression analysis. Additionally, the oximetry/PSG diagnostic scheme was shown to have higher cost-effectiveness in the diagnosis of OSA patients with an average cost ratio of 0.66 and an average waiting time ratio of 0.40 compared to the traditional scheme with PSG examination only.
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