Comparison of the indices of oxyhemoglobin saturation by pulse oximetry in obstructive sleep apnea hypopnea syndrome

A Review of Novel Oximetry Parameters for the Prediction of Cardiovascular Disease in Obstructive Sleep Apnoea

Obstructive sleep apnoea (OSA) is a sleep disorder with repetitive collapse of the upper airway during sleep, which leads to intermittent hypoxic events overnight, adverse neurocognitive, metabolic complications, and ultimately an increased risk of cardiovascular disease (CVD). The standard diagnostic parameter for OSA, apnoea-hypopnoea index (AHI), is inadequate to predict CVD morbidity and mortality, because it focuses only on the frequency of apnoea and hypopnoea events, and fails to reveal other physiological information for the prediction of CVD events. Novel parameters have been introduced to compensate for the deficiencies of AHI. However, the calculation methods and criteria for these parameters are unclear, hindering their use in cross-study analysis and studies. This review aims to discuss novel parameters for predicting CVD events from oximetry signals and to summarise the corresponding computational methods.

Desaturation event scoring criteria affect the perceived severity of nocturnal hypoxic load

OBJECTIVES/BACKGROUND

Interest in using blood oxygen desaturations in the diagnostics of sleep apnea has risen in recent years. However, no standardized criteria for desaturation scoring exist which complicates the drawing of solid conclusions from literature.

PATIENTS/METHODS

We investigated how different desaturation scoring criteria affect the severity of nocturnal hypoxic load and the prediction of impaired daytime vigilance in 845 patients. Desaturations were scored based on three features: 1) minimum oxygen saturation drop during the event (2-20%, 1% interval), 2) minimum duration of the event (2-20s, 1s interval), and 3) maximum plateau duration within the event (5-60s, 5s interval), resulting in 4332 different scoring criteria. The hypoxic load was described with oxygen desaturation index (ODI), desaturation severity (DesSev), and desaturation duration (DesDur) parameters. Association between hypoxic load and impaired vigilance was investigated with covariate-adjusted area under curve (AUC) analyses by dividing patients into normal (≤5 lapses) and impaired (≥36 lapses) vigilance groups based on psychomotor vigilance task performance.

RESULTS

The severity of hypoxic load varied greatly between different scoring criteria. For example, median ODI ranged between 0.4 and 12.9 events/h, DesSev 0.01-0.23 %-point, and DesDur 0.3-9.6 %-point when the minimum transient drop criterion of 3% was used and other two features were altered. Overall, the minimum transient drop criterion had the largest effect on parameter values. All models with differently determined parameters predicted impaired vigilance moderately (AUC = 0.722-0.734).

CONCLUSIONS

Desaturation scoring criteria greatly affected the severity of hypoxic load. However, the difference in the prediction of impaired vigilance between different criteria was rather small.

Pulse oximetrySpO2signal for automated identification of sleep apnea: a review and future trends

Sleep apnea (SA) is characterized by intermittent episodes of apnea or hypopnea paused or reduced breathing, respectively each lasting at least ten seconds that occur during sleep. SA has an estimated global prevalence of 200 million and is associated with medical comorbidity, and sufferers are also more likely to sustain traffic- and work-related injury due to daytime somnolence. SA is amenable to treatment if detected early. Polysomnography (PSG) involving multi-channel signal acquisition is the reference standard for diagnosing SA but is onerous and costly. For home-based detection of SA, single-channelSpO₂signal acquisition using portable pulse oximeters is feasible. Machine (ML) and deep learning (DL) models have been developed for automated classification of SA versus no SA usingSpO₂signals alone. In this work, we review studies published between 2012 and 2022 on the use of ML and DL forSpO₂signal-based diagnosis of SA. A literature search based on PRISMA recommendations yielded 297 publications, of which 31 were selected after considering the inclusion and exclusion criteria. There were 20 ML and 11 DL models; their methods, differences, results, merits, and limitations were discussed. Many studies reported encouraging performance, which indicates the utility ofSpO₂signals in wearable devices for home-based SA detection.

Oximetry Indices in the Management of Sleep Apnea: From Overnight Minimum Saturation to the Novel Hypoxemia Measures

Obstructive sleep apnea (OSA) is a multidimensional disease often underdiagnosed due to the complexity and unavailability of its standard diagnostic method: the polysomnography. Among the alternative abbreviated tests searching for a compromise between simplicity and accurateness, oximetry is probably the most popular. The blood oxygen saturation (SpO₂) signal is characterized by a near-constant profile in healthy subjects breathing normally, while marked drops (desaturations) are linked to respiratory events. Parameterization of the desaturations has led to a great number of indices of severity assessment commonly used to assist in OSA diagnosis. In this chapter, the main methodologies used to characterize the overnight oximetry profile are reviewed, from visual inspection and simple statistics to complex measures involving signal processing and pattern recognition techniques. We focus on the individual performance of each approach, but also on the complementarity among the great amount of indices existing in the state of the art, looking for the most relevant oximetric feature subset. Finally, a quick overview of SpO₂-based deep learning applications for OSA management is carried out, where the raw oximetry signal is analyzed without previous parameterization. Our research allows us to conclude that all the methodologies (conventional, time, frequency, nonlinear, and hypoxemia-based) demonstrate high ability to provide relevant oximetric indices, but only a reduced set provide non-redundant complementary information leading to a significant performance increase. Finally, although oximetry is a robust tool, greater standardization and prospective validation of the measures derived from complex signal processing techniques are still needed to homogenize interpretation and increase generalizability.

Machine learning approaches for screening the risk of obstructive sleep apnea in the Taiwan population based on body profile

(a) Objective: Obstructive sleep apnea syndrome (OSAS) is typically diagnosed through polysomnography (PSG). However, PSG incurs high medical costs. This study developed new models for screening the risk of moderate-to-severe OSAS (apnea-hypopnea index, AHI ≥15) and severe OSAS (AHI ≥30) in various age groups and sexes by using anthropometric features in the Taiwan population.(b) Participants: Data were derived from 10,391 northern Taiwan patients who underwent PSG.(c) Methods: Patients' characteristics - namely age, sex, body mass index (BMI), neck circumference, and waist circumference - was obtained. To develop an age- and sex-independent model, various approaches - namely logistic regression, k-nearest neighbor, naive Bayes, random forest (RF), and support vector machine - were trained for four groups based on sex and age (men or women; aged <50 or ≥50 years). Dataset was separated independently (training:70%; validation: 10%; testing: 20%) and Cross-validated grid search was applied for model optimization. Models demonstrating the highest overall accuracy in validation outcomes for the four groups were used to predict the testing dataset.(d) Results: The RF models showed the highest overall accuracy. BMI was the most influential parameter in both types of OSAS severity screening models.(e) Conclusion: The established models can be applied to screen OSAS risk in the Taiwan population and those with similar craniofacial features.

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for “non-circadian rhythm” sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The “endpoint” of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.

Nocturnal oximetry in bariatric surgery patients referred to overnight in-lab polysomnography

OBJECTIVE

This study aimed to evaluate nocturnal oximetry approaches in identifying obstructive sleep apnea (OSA) among bariatric surgical candidates.

METHODS

This was a cross-sectional study involving adult bariatric patients who were undergoing in-lab polysomnography and who were previously screened with the GOAL questionnaire. OSA severity was established as any OSA, moderate/severe OSA, and severe OSA. Oximetry data were evaluated as oxygen saturation (average and nadir), oxygen desaturation index (ODI) at 3%, and proportion of time spent with oxygen saturation <90%. Associations between oximetry data and the apnea-hypopnea index (AHI) were assessed by Spearman correlation index (r), linear regression, logistic regression, and discrimination.

RESULTS

All oximetry values were significantly correlated with the AHI among 1,178 individuals, with the ODI emerging as the better parameter (r = 0.911, p < 0.001). Using linear regression, the ODI was the only predictor of the AHI (β = 0.952, p < 0.001). In the multivariate analysis, the ODI was the only independent parameter predicting OSA at all severity levels. In addition, the ODI exhibited excellent discrimination to predict OSA and displayed improved performance among individuals screened as being at high risk versus those at low risk with the GOAL instrument.

CONCLUSIONS

The ODI emerges as a valid surrogate predictor of the AHI, particularly among those screened as being at high risk for OSA.

Effects of nocturnal complete denture usage on cardiorespiratory parameters: A pilot study

STATEMENT OF PROBLEM

Sleeping without conventional complete dentures (CCDs) has been stated by some to induce negative effects on the cardiorespiratory functions of edentulous patients with obstructive sleep apnea (OSA), although others have reported the exact opposite. Therefore, a consensus on nocturnal CCD usage is lacking.

PURPOSE

The purpose of this clinical study was to assess the effects of nocturnal denture usage on cardiorespiratory stability by using pulse oximetry (PO).

MATERIALS AND METHODS

Thirty CCD wearers were enrolled in the study. The first nocturnal pulse oximetry (FNPO) recordings were made on 3 different nights while the participants were sleeping without dentures (WOD). Oxygen desaturation index (ODI) and other PO parameters of the participants, including total respiratory event (TRE), basal SpO₂ (BSpO₂), time≤88 (T88), average low SpO₂ (ALSpO₂), total pulse event (TPE), average pulse rate (APR), and heart rate variability index (HRVI), were processed and the obtained data were recorded as WOD condition values. According to the ODI scores, the OSA status of the participants was grouped as normal (ODI<5), mild (5<ODI<15), moderate (15<ODI<30), or severe (ODI>30). Complete dentures were fabricated by an experienced prosthodontist and a dental laboratory technician by following conventional procedures. At the end of the first month of the follow-up period, the second nocturnal PO recordings (SNPO) were made on 3 different nights while the participants slept wearing dentures (WID), and the data obtained were recorded as WID condition values. The comparison of mean PO values obtained from WOD and WID were analyzed with the Wilcoxon signed- rank test (α=.05).

RESULTS

Significant differences were found between WOD and WID values in terms of TRE (P=.01), ODI (P=.001), ALSpO2 (P=.006), TPE (P=.001), and HRVI (P=.001) parameters. The significance of the improvements in the WID condition increased with the severity of OSA.

CONCLUSIONS

Improvements were observed in substantial cardiorespiratory parameters such as the ODI and HRVI of the participants wearing dentures nocturnally.

Diagnostic Accuracy of Oxygen Desaturation Index for Sleep-Disordered Breathing in Patients With Diabetes

BACKGROUND

Polysomnography (PSG) is the gold standard for diagnosis of sleep-disordered breathing (SDB). But it is impractical to perform PSG in all patients with diabetes. The objective was to develop a clinically easy-to-use prediction model to diagnosis SDB in patients with diabetes.

METHODS

A total of 440 patients with diabetes were recruited and underwent overnight PSG at West China Hospital. Prediction algorithms were based on oxygen desaturation index (ODI) and other variables, including sex, age, body mass index, Epworth score, mean oxygen saturation, and total sleep time. Two phase approach was employed to derivate and validate the models.

RESULTS

ODI was strongly correlated with apnea-hypopnea index (AHI) (r_s = 0.941). In the derivation phase, the single cutoff model with ODI was selected, with area under the receiver operating characteristic curve (AUC) of 0.956 (95%CI 0.917-0.994), 0.962 (95%CI 0.943-0.981), and 0.976 (95%CI 0.956-0.996) for predicting AHI ≥5/h, ≥15/h, and ≥30/h, respectively. We identified the cutoff of ODI 5/h, 15/h, and 25/h, as having important predictive value for AHI ≥5/h, ≥15/h, and ≥30/h, respectively. In the validation phase, the AUC of ODI was 0.941 (95%CI 0.904-0.978), 0.969 (95%CI 0.969-0.991), and 0.949 (95%CI 0.915-0.983) for predicting AHI ≥5/h, ≥15/h, and ≥30/h, respectively. The sensitivity of ODI ≥5/h, ≥15/h, and ≥25/h was 92%, 90%, and 93%, respectively, while the specificity was 73%, 89%, and 85%, respectively.

CONCLUSIONS

ODI is a sensitive and specific tool to predict SDB in patients with diabetes.

Individuals With Scleroderma May Have Increased Risk of Sleep-Disordered Breathing

STUDY OBJECTIVES

Scleroderma is associated with abnormal skin thickening, interstitial lung disease, pulmonary hypertension, and abnormalities of the upper airway. These changes can cause cardiopulmonary complications, potentially including sleep-disordered breathing. The objective of this study is to examine the risk of sleep-disordered breathing in patients with scleroderma.

METHODS

We retrospectively identified patients with documented scleroderma. We abstracted data from their electronic health records, including findings from antibody tests, serial pulmonary function tests, transthoracic echocardiography, high-resolution computed tomography, and overnight forehead oximetry.

RESULTS

We identified 171 patients with scleroderma. Mean age at the time of initial consult was 56.5 years (range, 18-96 years), and 150 (86.7%) were women. Scleroderma was categorized as limited disease for 108 (62.4%), diffuse disease for 59 (34.1%), and mixed connective tissue disease for 6 (3.5%). Fifty-four patients (31.2%) had abnormal overnight forehead oximetry results, defined as an oxygen desaturation index greater than 5 or a baseline mean arterial oxygen saturation level less than 90%.

CONCLUSIONS

Cardiopulmonary complications are common in patients with scleroderma, one of which may be sleep-disordered breathing. In our cohort, approximately one-third of individuals with scleroderma had evidence of sleep-disordered breathing. Moreover, the rate of sleep-disordered breathing in our population of scleroderma patients was twice the rate of pulmonary hypertension and was approximately the same as the rate of interstitial lung disease. Future prospective studies are needed to further assess the role of sleep-disordered breathing in scleroderma clinical outcomes.

Oximetry as an Accurate Tool for Identifying Moderate to Severe Sleep Apnea in Patients With Acute Stroke

STUDY OBJECTIVES

Sleep-disordered breathing (SDB) is highly prevalent in patients with acute stroke. SDB is often underdiagnosed and associated with neurological deterioration and stroke recurrence. Polysomnography or home sleep apnea testing (HSAT) is typically used as the diagnostic modality; however, it may not be feasible to use regularly in patients with acute stroke. We investigated the predictive performance of pulse oximetry, a simpler alternative, to identify SDB.

METHODS

The records of 254 patients, who were admitted to Boston Medical Center for acute stroke and underwent HSAT, were retrospectively reviewed. Oxygen desaturation index (ODI) from pulse oximetry channel were compared to respiratory event index (REI) obtained from HSAT devices. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ODI were calculated, and different ODI cutoff values to predict SDB were proposed.

RESULTS

ODI had a strong correlation (r = .902) and agreement with REI. ODI was accurate in predicting SDB at different REI thresholds (REI ≥ 5, REI ≥ 15, and REI ≥ 30 events/h) with the area under the curve (AUC) of .965, .974, and .951, respectively. An ODI ≥ 5 events/h rules in the presence of SDB (specificity 91.7%, PPV 96.3%). An ODI ≥ 15 events/h rules in moderate to severe SDB (specificity 96.4%, PPV 95%) and an ODI < 5 events/h rules out moderate to severe SDB (sensitivity 100%, NPV 100%).

CONCLUSIONS

Nocturnal pulse oximetry has a high diagnostic accuracy in predicting moderate to severe SDB in patients with acute stroke. Oximetry can be a simple modality to rapidly recognize patients with more severe SDB and facilitate the referral to the confirmation sleep study.

The characteristics of dental occlusion in patients with moderate to severe obstructive sleep apnea in Saudi Arabia

Objectives:

To evaluate characteristics of dental occlusion among non-obese Saudi adult patients suffering from moderate to severe obstructive sleep apnea(OSA).

Methods:

Following ethical approval, a cross-sectional study was conducted at Sleep Disorders Center, King Khalid University Hospital, Riyadh, Kingdom of Saudi Arabia, between January and March 2017. Non-obese adult Saudi patients with moderate/severe OSA (apnea-hypopnea index>15) and without history of malocclusion or edentulism were included with an estimated sample size of 50. Demographic details and severity of OSA as diagnosed by polysomnography were recorded. Characteristics of dental occlusion, namely molar, canine and incisor relationship, overjet, overbite, crossbite and arch form were obtained through calibrated examiners (kappa 0.81). Descriptive statistical analysis and Chi-square test, with 95% significance level (p<0.05), were used to identify relationships between the severity of OSA and characteristics of dental occlusion.

Results:

A total of 51 patients (31 males, 20 females; mean age 49.45±10.35 years), were enrolled in the study. Severity of OSA was moderate in 17 patients and severe in 34 patients. Severe form of OSA was more among males (64.7%) and in patients with Class-II division-1 incisor relationship (94.1%). Neither the demographic characteristics, nor characteristics of dental occlusion showed statistically significant relationship with the severity of OSA.

Conclusion:

The results of the present cross-sectional study indicate that the characteristics of dental occlusion are not related to the severity of OSA among non-obese adult Saudi patients.

Diagnostic accuracy of a mathematical model to predict apnea-hypopnea index using nighttime pulse oximetry

The intent of this study is to develop a predictive model to convert an oxygen desaturation index (ODI) to an apnea-hypopnea index (AHI). This model will then be compared to actual AHI to determine its precision. One thousand four hundred and sixty-seven subjects given polysomnograms with concurrent pulse oximetry between April 14, 2010, and February 7, 2012, were divided into model development (n = 733) and verification groups (n = 734) in order to develop a predictive model of AHI using ODI. Quadratic regression was used for model development. The coefficient of determination (r(2)) between the actual AHI and the predicted AHI (PredAHI) was 0.80 (r = 0.90), which was significant at a p < 0.001. The areas under the receiver operating characteristic curve ranged from 0.96 for AHI thresholds of ≥ 10 and ≥ 15/h to 0.97 for thresholds of ≥ 5 and ≥ 30/h. The algorithm described in this paper provides a convenient and accurate way to convert ODI to a predicted AHI. This tool makes it easier for clinicians to understand oximetry data in the context of traditional measures of sleep apnea.

Validation of overnight oximetry to diagnose patients with moderate to severe obstructive sleep apnea

Background

Polysomnography (PSG) is treated as the gold standard for diagnosing obstructive sleep apnea (OSA). However, it is labor-intensive, time-consuming, and expensive. This study evaluates validity of overnight pulse oximetry as a diagnostic tool for moderate to severe OSA patients.

Methods

A total of 699 patients with possible OSA were recruited for overnight oximetry and PSG examination at the Sleep Center of a University Hospital from Jan. 2004 to Dec. 2005. By excluding 23 patients with poor oximetry recording, poor EEG signals, or respiratory artifacts resulting in a total recording time less than 3 hours; 12 patients with total sleeping time (TST) less than 1 hour, possibly because of insomnia; and 48 patients whose ages less than 20 or more than 85 years old, data of 616 patients were used for further study. By further considering 76 patients with TST < 4 h, a group of 540 patients with TST ≥ 4 h was used to study the effect of insufficient sleeping time. Alice 4 PSG recorder (Respironics Inc., USA) was used to monitor patients with suspected OSA and to record their PSG data. After statistical analysis and feature selection, models built based on support vector machine (SVM) were then used to diagnose moderate and moderate to severe OSA patients with a threshold of AHI = 30 and AHI = 15, respectively.

Results

The SVM models designed based on the oxyhemoglobin desaturation index (ODI) derived from oximetry measurements provided an accuracy of 90.42-90.55%, a sensitivity of 89.36-89.87%, a specificity of 91.08-93.05%, and an area under ROC curve (AUC) of 0.953-0.957 for the diagnosis of severe OSA patients; as well as achieved an accuracy of 87.33-87.77%, a sensitivity of 87.71-88.53%, a specificity of 86.38-86.56%, and an AUC of 0.921-0.924 for the diagnosis of moderate to severe OSA patients. The predictive outcome of ODI to diagnose severe OSA patients is better than to diagnose moderate to severe OSA patients.

Conclusions

Overnight pulse oximetry provides satisfactory diagnostic performance in detecting severe OSA patients. Home-styled oximetry may be a tool for severe OSA diagnosis.

High prevalence of sleep disordered breathing in patients with diabetic macular edema

BACKGROUND

Diabetic retinopathy is more common and severe in patients with sleep disordered breathing (SDB). This study aimed to establish whether this is also true for patients with diabetic clinically significant macular edema (CSME). It is hypothesized that SDB, through intermittent hypoxia and blood pressure oscillations, might provoke worsening of CSME.

METHODS

Patients with CSME had a home sleep study (ApneaLink; ResMed) to identify SDB. These results were compared with relevant control populations. Macular thickness was measured using optical coherence tomography, and retinal photographs were graded to assess the severity of retinopathy.

RESULTS

Eighty of 195 patients (40 men) consented, with average age of 64.7 (11.7) years, neck circumference of 40.4 (5.4) cm, body mass index of 30.2 (6.2) kg/m2, glycosylated hemoglobin (HbA1c) 7.8% (1.4%) [62 (8.0) mmol/mol], and Epworth sleepiness scale of 7.4 (4.8). Overall, 54% had an oxygen desaturation index ≥ 10, and 31% had an apnea-hypopnea index ≥ 15. This SDB prevalence is probably higher than would be expected from the available matched control data. Those with SDB were not sleepier, but they were older and more obese. No significant relationship was identified between the degree of macular thickness and the severity of SDB.

CONCLUSION

Individuals with CSME have a high prevalence of SDB. Sleep disordered breathing may contribute to the pathophysiology of CSME, but the mechanism remains unclear. Given the high prevalence, retinal specialists should perhaps consider a diagnosis of SDB in patients with CSME.

Real-time sleep apnea detection by classifier combination

To find an efficient and valid alternative of polysomnography (PSG), this paper investigates real-time sleep apnea and hypopnea syndrome (SAHS) detection based on electrocardiograph (ECG) and saturation of peripheral oxygen (SpO(2)) signals, individually and in combination. We include ten machine-learning algorithms in our classification experiment. It is shown that our proposed SpO (2) features outperform the ECG features in terms of diagnostic ability. More importantly, we propose classifier combination to further enhance the classification performance by harnessing the complementary information provided by individual classifiers. With our selected SpO(2) and ECG features, the classifier combination using AdaBoost with Decision Stump, Bagging with REPTree, and either kNN or Decision Table achieves sensitivity, specificity, and accuracy all around 82% for a minute-based real-time SAHS detection over 25 sleep-disordered-breathing suspects' full overnight recordings.

Clinical characteristics, comorbidities, and response to treatment of veterans with obstructive sleep apnea, Cincinnati Veterans Affairs Medical Center, 2005-2007

Introduction

Obstructive sleep apnea (OSA) is a common disorder that is associated with significant morbidity. Veterans may be at an elevated risk for OSA because of increased prevalence of factors associated with the development and progression of OSA. The objective of this study was to determine the clinical characteristics, comorbidities, polysomnographic findings, and response to treatment of veterans with OSA.

Methods

We performed a retrospective chart review of 596 patients undergoing polysomnography at the Cincinnati Veterans Affairs Medical Center from February 2005 through December 2007. We assessed potential correlations of clinical data with polysomnography findings and response to treatment.

Results

Polysomnography demonstrated OSA in 76% of patients; 30% had mild OSA, 23% moderate OSA, and 47% severe OSA. Increasing body mass index, neck circumference, Epworth Sleepiness Scale score, hypertension, congestive heart failure, and type 2 diabetes correlated with increasing OSA severity. Positive airway pressure treatment was initiated in 81% of veterans with OSA, but only 59% reported good adherence to this treatment method. Of the patients reporting good adherence, a greater proportion of those with severe OSA (27%) than with mild or moderate disease (0%-12%) reported an excellent response to treatment.

Conclusion

The prevalence of metabolic and cardiovascular comorbidities increased with increasing OSA severity. Only 59% of treated patients reported good adherence to treatment with positive airway pressure, and response to treatment correlated with OSA severity.

Updates on definition, consequences, and management of obstructive sleep apnea

Obstructive sleep apnea (OSA) is a breathing disorder during sleep that has implications beyond disrupted sleep. It is increasingly recognized as an independent risk factor for cardiac, neurologic, and perioperative morbidities. Yet this disorder remains undiagnosed in a substantial portion of our population. It is imperative for all physicians to remain vigilant in identifying patients with signs and symptoms consistent with OSA. This review focuses on updates in the areas of terminology and testing, complications of untreated OSA, perioperative considerations, treatment options, and new developments in this field.

Automatic detection of apnoea of prematurity

The detection of the incidents of apnoea of prematurity (AP) in preterm infants is important in the intensive care unit, but this detection is often based on simple threshold techniques, which suffer from poor specificity. Three methods for the automatic detection of AP were designed, tested and evaluated using approximately 2426 h of continuous recording from 54 neonates (μ = 44 h and σ = 7 h). The first method was based on the cumulative sum of the time series of heart rate (HR), respiratory rate (RR) and oxygen saturation (SpO(2)) along with the sum of their Shannon entropy. The performance of this method gave 94.53% sensitivity, 74.72% specificity and 77.84% accuracy. The second method was based on the correlation between the time series of HR, RR and SpO(2), which were used as inputs to an artificial neural network. This gave 81.85% sensitivity, 75.83% specificity and 76.78% accuracy. The third method utilized the derivative of the three time series and yielded a performance of 100% sensitivity, 96.19% specificity and 96.79% accuracy. Although not optimized to work in real time, the latter method has the potential for forming the basis of a real time system for the detection of incidents of AP.

Multivariate analysis of blood oxygen saturation recordings in obstructive sleep apnea diagnosis

This study focuses on the analysis of blood oxygen saturation (SaO(2)) from nocturnal pulse oximetry (NPO) to help in the diagnosis of the obstructive sleep apnea (OSA) syndrome. A population of 148 patients suspected of suffering from OSA syndrome was studied. A wide set of 16 features was used to characterize changes in the SaO(2) profile during the night. Our feature set included common statistics in the time and frequency domains, conventional spectral characteristics from the power spectral density (PSD) function, and nonlinear features. We performed feature selection by means of a step-forward logistic regression (LR) approach with leave-one-out cross-validation. Second- and fourth-order statistical moments in the time domain (M2t and M4t), the relative power in the 0.014-0.033 Hz frequency band ( P(R)), and the Lempel-Ziv complexity (LZC) were automatically selected. 92.0% sensitivity, 85.4% specificity, and 89.7% accuracy were obtained. The optimum feature set significantly improved the diagnostic ability of each feature individually. Furthermore, our results outperformed classic oximetric indexes commonly used by physicians. We conclude that simultaneous analysis in the time and frequency domains by means of statistical moments, spectral and nonlinear features could provide complementary information from NPO to improve OSA diagnosis.