Screening of patients with obstructive sleep Apnea syndrome using C4.5 algorithm based on non linear analysis of respiratory signals during sleep

Kaimakamis, Evangelos; Bratsas, Charalampos; Sichletidis, Lazaros; Karvounis, Charalambos; Maglaveras, Nicos

doi:10.1109/iembs.2009.5334605

Cited by 15 publications

(21 citation statements)

References 15 publications

(14 reference statements)

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“…That is, changes in the respiratory profile itself could serve as a window into the state of the system. A growing literature supports the value of similar analytical approaches to the respiratory pattern as a biomarker of pathophysiologic status (Kaimakamis et al 2009; Koo et al 2010; Veiga et al 2010; Mangin et al 2011). …”

Section: Discussionmentioning

confidence: 99%

Lung and brainstem cytokine levels are associated with breathing pattern changes in a rodent model of acute lung injury

Jacono¹,

Mayer²,

Hsieh³

et al. 2011

Respiratory Physiology & Neurobiology

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Acute lung injury evokes a pulmonary inflammatory response and changes in the breathing pattern. The inflammatory response has a centrally mediated component which depends on the vagi. We hypothesize that the central inflammatory response, complimentary to the pulmonary inflammatory response, is expressed in the nuclei tractus solitarii (nTS) and that the expression of cytokines in the nTS is associated with breathing pattern changes. Adult, male Sprague-Dawley rats (n=12) received intratracheal instillation of either bleomycin (3U in 120 µl of saline) or saline (120 µl). Respiratory pattern changed by 24h. At 48 h, bronchoalveolar lavage fluid and lung tissue had increased IL-1β and TNF-α levels, but not IL-6. No changes in these cytokines were noted in serum. Immunocytochemical analysis of the brainstem indicated increased expression of IL-1β in the nTS commissural subnucleus that was localized to neurons. We conclude that breathing pattern changes in acute lung injury were associated with increased levels of IL-1β in brainstem, areas which integrate cardio-respiratory sensory input.

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Section: Discussionmentioning

confidence: 99%

Lung and brainstem cytokine levels are associated with breathing pattern changes in a rodent model of acute lung injury

Jacono¹,

Mayer²,

Hsieh³

et al. 2011

Respiratory Physiology & Neurobiology

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“…One study [14] tackled the tedious and time-consuming task of analyzing PSG records, automatizing both the detection and classification of sleep apneas, through analysis of wavelets and Bayesian neural networks. The other [19] classified patients with possible diagnosis of OSA into groups according to the severity of the disease using a decision tree producing algorithm based on nonlinear analysis of three respiratory signals instead of full PSG. However, none of the found approaches addressed only clinical and demographic variables that could be used earlier in the healthcare process flow, as they require diagnostic data from PSG.…”

Section: Introductionmentioning

confidence: 99%

“…Recommendations from the American Academy of Sleep Medicine state that OSA is present when AHI ≥ 5. It can be classified as mild (AHI: 5-15), moderate (AHI: [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], or severe (AHI ≥ 30) [6,7,13,22]. Approximately 30% of the general public is affected by a significant sleep problem, often of long standing [39].…”

Section: Introductionmentioning

confidence: 99%

A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

Ferreira-Santos

Rodrigues

2018

Int J Data Sci Anal

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In obstructive sleep apnea, respiratory effort is maintained but ventilation decreases/disappears due to upper-airway partial/total occlusion. This condition affects about 4% of men and 2% of women worldwide. This study aimed to define an auxiliary diagnostic method that can support the decision to perform polysomnography, based on risk and diagnostic factors. Our sample performed polysomnography between January and May 2015. Two Bayesian classifiers were used to build the models: Naïve Bayes and Tree Augmented Naïve Bayes, using 38 variables identified by literature review or just a selection of 6. Area under the ROC curve, sensitivity, specificity and predictive values were evaluated using leave-one-out and cross-validation techniques. From a total of 241 patients, only 194 fulfilled the inclusion criteria, 123 (63%) were male, with a mean age of 58 years, 66 (34%) patients had a normal result and 128 (66%) a diagnosis of obstructive sleep apnea. The cross-validated AUCs for each model were: NB38: 69.2%; TAN38: 69.0%; NB6: 74.6% and TAN6: 63.6%. Regarding risk matrix, female gender presented a starting rate of 8%, comparing to 20% in male gender, almost 3 times higher. The high (34%) proportion of normal results confirms the need for a pre-evaluation prior to polysomnography, making the search for a validated model to screen patients with suspicion of obstructive sleep apnea essential, especially at primary care level.

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“…reported pauses in breathing during sleep, daytime sleepiness and fatigue, frequent arousals at night and snoring at variable percentages. Dementia, neuromuscular disorders, overlap syndrome or severe cardiac problems were exclusion criteria [ 19 ]. Finally, medications that affected the sleep patterns were also exclusion criteria.…”

Section: Methodsmentioning

confidence: 99%

“…Also, dDFA_f2 and mDFA_f2 refer to the analogous parameters when the F signals were reduced to one fourth of their initial duration (achieved by keeping every fourth value of each time series from F), thus allowing for signal analysis of 80 minutes in each case. Finally dmDFA_t2 and mmDFA_t2 were introduced by taking the difference between or the mean value of mDFA_f2 and mDFA_t [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of a Decision Support System for Obstructive Sleep Apnea with Nonlinear Analysis of Respiratory Signals

et al. 2016

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IntroductionObstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder.Materials and MethodsPatients referred to a Sleep Unit (135) underwent full polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) extracted from two biosignals (airflow from a nasal cannula, thoracic movement) and one linear derived from Oxygen saturation provided input to a data mining application with contemporary classification algorithms for the creation of predictive models for AHI.ResultsA linear regression model presented a correlation coefficient of 0.77 in predicting AHI. With a cutoff value of AHI = 8, the sensitivity and specificity were 93% and 71.4% in discrimination between patients and normal subjects. The decision tree for the discrimination between patients and normal had sensitivity and specificity of 91% and 60%, respectively. Certain obtained nonlinear values correlated significantly with commonly accepted physiological parameters of people suffering from OSA.DiscussionWe developed a predictive model for the presence/severity of OSA using a simple linear equation and additional decision trees with nonlinear features extracted from 3 respiratory recordings. The accuracy of the methodology is high and the findings provide insight to the underlying pathophysiology of the syndrome.ConclusionsReliable predictions of OSA are possible using linear and nonlinear indices from only 3 respiratory signals during sleep. The proposed models could lead to a better study of the pathophysiology of OSA and facilitate initial evaluation/follow up of suspected patients OSA utilizing a practical low cost methodology.Trial RegistrationClinicalTrials.gov NCT01161381

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Screening of patients with obstructive sleep Apnea syndrome using C4.5 algorithm based on non linear analysis of respiratory signals during sleep

Abstract: It is possible to have reliable predictions of the severity of OSAS using linear and nonlinear indices from only two respiratory signals during sleep instead of performing full polysomnography. The proposed algorithm could be used for screening patients suspected to suffer from OSAS.

Cited by 15 publications

References 15 publications

Lung and brainstem cytokine levels are associated with breathing pattern changes in a rodent model of acute lung injury

Lung and brainstem cytokine levels are associated with breathing pattern changes in a rodent model of acute lung injury

A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

Evaluation of a Decision Support System for Obstructive Sleep Apnea with Nonlinear Analysis of Respiratory Signals

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