Accuracy of oximetry for detection of respiratory disturbances in sleep apnea syndrome

Paediatric sleep diagnostics in the 21st century: the era of "sleep-omics"?

Paediatric sleep diagnostics is performed using complex multichannel tests in specialised centres, limiting access and availability and resulting in delayed diagnosis and management. Such investigations are often challenging due to patient size (prematurity), tolerability, and compliance with "gold standard" equipment. Children with sensory/behavioural issues, at increased risk of sleep disordered breathing (SDB), often find standard diagnostic equipment difficult.SDB can have implications for a child both in terms of physical health and neurocognitive development. Potential sequelae of untreated SDB includes failure to thrive, cardiopulmonary disease, impaired learning and behavioural issues. Prompt and accurate diagnosis of SDB is important to facilitate early intervention and improve outcomes.The current gold-standard diagnostic test for SDB is polysomnography (PSG), which is expensive, requiring the interpretation of a highly specialised physiologist. PSG is not feasible in low-income countries or outwith specialist sleep centres. During the coronavirus disease 2019 pandemic, efforts were made to improve remote monitoring and diagnostics in paediatric sleep medicine, resulting in a paradigm shift in SDB technology with a focus on automated diagnosis harnessing artificial intelligence (AI). AI enables interrogation of large datasets, setting the scene for an era of "sleep-omics", characterising the endotypic and phenotypic bedrock of SDB by drawing on genetic, lifestyle and demographic information. The National Institute for Health and Care Excellence recently announced a programme for the development of automated home-testing devices for SDB. Scorer-independent scalable diagnostic approaches for paediatric SDB have potential to improve diagnostic accuracy, accessibility and patient tolerability; reduce health inequalities; and yield downstream economic and environmental benefits.

A novel, simple, and accurate pulse oximetry indicator for screening adult obstructive sleep apnea

OBJECTIVE

The objective of the study was to develop a multiparametric oximetry indicator (IMp-SpO2) to diagnose obstructive sleep apnea in adults.

MATERIAL AND METHOD

This was an observational, retrospective study of diagnostic accuracy. We included adults who had had a diagnostic polysomnography with few artifacts and a total sleep time of at least 180 min in the sleep laboratory. Obstructive sleep apnea (OSA) was defined as an apnea-hypopnea index (AHI) ≥ 5. The database was randomly divided into an experimental (Exp-G) and validation (Val-G) group. The program calculated several parameters of oxygen saturation variability (Par-VarSpO2): (a) oxygen desaturation index (ODI ≥ 3, 4%) and (b) 90, 95, and 97.5 percentiles of both the number of oxygen desaturations ≥ 3 and 4% (P90-97.5 OD3/4 W5-60) and SpO2 standard deviations in moving windows from 5 to 60 min (P90-P97.5 SDSpO2 W5-10). Area under the ROC curve (AUC-ROC), sensitivity, specificity, positive/negative likelihood ratios, and accuracy were calculated.

RESULTS

Of 1141 adults included in the study, experimental (571) and validation group (570) were similar (women 47% vs 45%, BMI 27.5 kg/m² vs 27.2 kg/m², and AHI 11.7 vs 12, p NS). The IMp-SpO2 developed in the experimental group consisted of a combination of 10 parameters of oxygen saturation variability. The presence of at least one IMp-SpO2 variable had a high diagnostic performance for OSA (sensitivity/specificity/accuracy: Exp-G: 92.8/94/93.2%; Val-G: 93/95.2/93.7%). The IMp-SpO2 AUC-ROC was higher (Exp-G 0.934, Val-G 0.941) than most of the Par-VarSpO2 (0.898-0.929, p < 0.05).

CONCLUSION

The IMp-SpO2 showed a > 90% accuracy for OSA diagnosis in adults.

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.

Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation

Background

Sleep apnea is a respiratory disorder characterized by frequent breathing cessation during sleep. Sleep apnea severity is determined by the apnea-hypopnea index (AHI), which is the hourly rate of respiratory events. In positional sleep apnea, the AHI is higher in the supine sleeping position than it is in other sleeping positions. Positional therapy is a behavioral strategy (eg, wearing an item to encourage sleeping toward the lateral position) to treat positional apnea. The gold standard of diagnosing sleep apnea and whether or not it is positional is polysomnography; however, this test is inconvenient, expensive, and has a long waiting list.

Objective

The objective of this study was to develop and evaluate a noncontact method to estimate sleep apnea severity and to distinguish positional versus nonpositional sleep apnea.

Methods

A noncontact deep-learning algorithm was developed to analyze infrared video of sleep for estimating AHI and to distinguish patients with positional vs nonpositional sleep apnea. Specifically, a 3D convolutional neural network (CNN) architecture was used to process movements extracted by optical flow to detect respiratory events. Positional sleep apnea patients were subsequently identified by combining the AHI information provided by the 3D-CNN model with the sleeping position (supine vs lateral) detected via a previously developed CNN model.

Results

The algorithm was validated on data of 41 participants, including 26 men and 15 women with a mean age of 53 (SD 13) years, BMI of 30 (SD 7), AHI of 27 (SD 31) events/hour, and sleep duration of 5 (SD 1) hours; 20 participants had positional sleep apnea, 15 participants had nonpositional sleep apnea, and the positional status could not be discriminated for the remaining 6 participants. AHI values estimated by the 3D-CNN model correlated strongly and significantly with the gold standard (Spearman correlation coefficient 0.79, P<.001). Individuals with positional sleep apnea (based on an AHI threshold of 15) were identified with 83% accuracy and an F1-score of 86%.

Conclusions

This study demonstrates the possibility of using a camera-based method for developing an accessible and easy-to-use device for screening sleep apnea at home, which can be provided in the form of a tablet or smartphone app.

Night to night variability of pulse oximetry features in children at home and at the hospital

Objective. Investigation of the night-to-night (NtN) variability of pulse oximetry features in children with suspicion of Sleep Apnea.Approach. Following ethics approval and informed consent, 75 children referred to British Columbia Children's Hospital for overnight PSG were recorded on three consecutive nights, including one at the hospital simultaneously with polysomnography and 2 nights at home. During all three nights, a smartphone-based pulse oximeter sensor was used to record overnight pulse oximetry (SpO2 and photoplethysmogram). Features characterizing SpO2 dynamics and heart rate were derived. The NtN variability of these features over the three different nights was investigated using linear mixed models.Main results. Overall most pulse oximetry features (e.g. the oxygen desaturation index) showed no NtN variability. One of the exceptions is for the signal quality, which was significantly lower during at home measurements compared to measurements in the hospital.Significance. At home pulse oximetry screening shows an increasing predictive value to investigate obstructive sleep apnea (OSA) severity. Hospital recordings affect subjects normal sleep and OSA severity and recordings may vary between nights at home. Before establishing the role of home monitoring as a diagnostic test for OSA, we must first determine their NtN variability. Most pulse oximetry features showed no significant NtN variability and could therefore be used in future at-home testing to create a reliable and consistent OSA screening tool. A single night recording at home should be able to characterize pulse oximetry features in children.

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.

Increased heart rate with sleep disordered breathing in hypertrophic cardiomyopathy

BACKGROUND

Current guidelines recommend medications with rate control properties for symptomatic patients with hypertrophic cardiomyopathy (HCM) based on the rationale that lowering heart rate (HR) improves their symptoms. Whether sleep disordered breathing (SDB) is associated with increased HR in HCM patients is not known.

METHOD

We diagnosed uncontrolled SDB (oxygen desaturation index ≥5) in consecutive echocardiographically confirmed HCM patients seen at Mayo Clinic, Rochester, and analyzed their HR as recorded by a 24-h Holter monitor. We compared mean, minimum, maximum HR between those with vs without SDB. In a pilot subanalysis of HCM patients with SDB who also underwent subsequent diagnostic polysomnography (PSG), we analyzed RR interval changes coinciding with obstructive sleep apnea and hypopnea episodes.

RESULTS

Of the 230 HCM patients included in this study (age 54 ± 16 years; 138 male; LVOT pressure gradient at rest 45 ± 39 mmHg), 115 (50%) patients had SDB. HCM patients with SDB were recorded to have higher mean HR (71 vs. 67 bpm; p = .002, adjusted p = .001), and this difference was most pronounced during night hours of 10 PM to 5 AM (61 vs. 67 bpm; p < .001). In the pilot analysis of the available PSG data, the release of obstructive sleep apneas and hypopneas coincided with fluctuation of HR.

CONCLUSIONS

SDB is independently associated with higher mean HR in patients with HCM, and this difference is most significant during sleep. Treatment of SDB, which is readily available, should be tested as a complementary modality to the currently recommended pharmacotherapy aimed at lowering HR in patients with symptomatic HCM.

ApEn for assessing hypoxemia severity in obstructive sleep apnea hypopnea syndrome patients

OBJECTIVE

A new index, approximate entropy (ApEn) of oxygen saturation, was used to assess the severity of hypoxemia in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS), determine the correlation with other parameters, and explore its clinical value.

METHODS

A retrospective analysis was performed on 1200 patients with OSAHS and snorers (normal control). All subjects underwent sleep apnea monitoring for 6 h. Subjects were divided into four subgroups by apnea-hypopnea index (AHI): normal control (AHI < 5), mild OSAHS (5 ≤ AHI < 15), moderate OSAHS (15 ≤ AHI < 30), and severe OSAHS 104 (AHI ≥ 30). ApEn was initially compared among the subgroups. Then a correlation analysis of AHI with ApEn and a correlation analysis of ApEn with oxygen desaturation index (ODI), lowest oxygen saturation (LO2), and T < 90% were performed. (2) The AHI was used as the gold standard, and an attempt was performed to determine the value of ApEn to assess the severity of hypoxemia in OSAHS.

RESULTS

Among the 1200 subjects, 822 subjects were men (72%) and mean age was 53.2 ± 15.2 years (range 24-95 years). The ApEn in each group was significantly different (P <0.001), and the ApEn synchronously increased with AHI. Furthermore, a significant difference in ApEn was found among the groups (P <0.001). In addition, ApEn had a good correlation with ODI, LO2, and T <90%. According to the ROC analysis results, the boundary value of ApEn to judge OSAHS patients with mild, moderate, and severe hypoxia was 16.72, 17.84, and 20.06, respectively.

CONCLUSION

ApEn synchronously increased with the AHI and had a good correlation with AHI, ODI, LO2, and T <90%. These findings suggest that ApEn may have clinical value for assessing hypoxia severity in OSAHS patients.

Obstructive Sleep Apnea in Patients With Fibrotic Interstitial Lung Disease and COPD

STUDY OBJECTIVES

Despite the clinical and prognostic significance of obstructive sleep apnea (OSA) in chronic respiratory diseases (CRDs), there have been few studies about the possible predictors of OSA and the effect of OSA on quality of life in patients with CRDs. The objectives were to identify physiological and clinical parameters that predict the occurrence and severity of OSA and to investigate the effect of OSA on quality of life in patients with CRDs.

METHODS

Seventy-three patients with chronic obstructive pulmonary disease (COPD) and 77 patients with fibrotic interstitial lung disease (ILD) underwent overnight polysomnography (PSG) and pulmonary function testing and completed clinical questionnaires. The oximetry tracing was interpreted blindly with respect to the PSG results.

RESULTS

The prevalence of OSA was 44% and 62% in COPD and ILD, respectively. The COPD assessment test item scores related to sleep quality and daily vitality were worse among patients with OSA than among patients without OSA. The STOP-BANG questionnaire (cutoff point ≥ 3) and oxygen desaturation index from the oximetry recording (oxygen desaturation index (ODI) were associated with OSA in CRDs. The STOP-BANG questionnaire with a cutoff point ≥ 3 or 6 had the highest sensitivity and specificity, respectively, in detecting OSA in CRDs. ODI had the best accuracy in identifying OSA and was independently associated with the apnea-hypopnea index in CRDs.

CONCLUSIONS

We found OSA to be common and associated with worse sleep quality and less daily vitality in patients with advanced CRDs. The STOP-BANG questionnaire with different cutoff points may help rule in or rule out OSA. Overnight oximetry can be used as a screening tool for OSA and can assist the clinical evaluation of OSA in patients with CRDs.

Validation of an apnea and hypopnea detection algorithm implemented in implantable cardioverter defibrillators. The AIRLESS study

Diagnosis of sleep apnea (SA) using simple tools has the potential to improve the efficacy of cardiac implants in the prevention of cardiac arrhythmias. The aim of the present study was to validate a transthoracic impedance sensor for SA diagnosis in patients with cardiac implants. We compared the apnea-hypopnea index (AHI) obtained from polysomnography (AHI_PSG) with the AHI obtained from autoscoring algorithms of the ApneaScan implantable impedance respiration sensor (AHI_AS) three months after implantation of cardioverter-defibrillator (ICD) or cardiac resynchronization therapy-defibrillator (CRT-D) devices. Twenty-five patients with indications for implantation of ICD or CRT-D (INCEPTA; Boston Scientific) (24 men, 59.9 ± 14.4 years; LVEF 30.3 ± 6.4%; body mass index 25.9 ± 4.2 kg/m²) were included. Mean AHI-_PSG was 21.9 ± 19.1 events/hr. A significant correlation was found between AHI_PSG and AHI_AS especially for the most severe SA (Spearman correlation: 0.71, p < 0.001). Intraclass Correlation Coefficient (was in the expected range: 0.67, 95% CI: 0.39–0.84. The mean bias was 5.4 events per hour (mean AHI: 23.3 ± 14.6 versus 29.7 ± 13.7 for AHI-_PSG and AHI-_AS, respectively). An optimal cutoff value for the AHI_AS at 30 events/h was obtained from the Receiver Operator Characteristic (ROC) curve analysis, which yielded a sensitivity of 100%, a specificity of 80%, PPV = 67%, NPV = 100%. Using an advanced algorithm for autoscoring of transthoracic impedance included in ICDs is reliable to identify SA and has the potential to improve the management of patients with cardiac implants.

Pediatric pulse oximetry-based OSA screening at different thresholds of the apnea-hypopnea index with an expression of uncertainty for inconclusive classifications

BACKGROUND

Assessments of pediatric obstructive sleep apnea (OSA) are underutilized across Canada due to a lack of resources. Polysomnography (PSG) measures OSA severity through the average number of apnea/hypopnea events per hour (AHI), but is resource intensive and requires a specialized sleep laboratory, which results in long waitlists and delays in OSA detection. Prompt diagnosis and treatment of OSA are crucial for children, as untreated OSA is linked to behavioral deficits, growth failure, and negative cardiovascular consequences. We aim to assess the performance of a portable pediatric OSA screening tool at different AHI cut-offs using overnight smartphone-based pulse oximetry.

MATERIAL AND METHODS

Following ethics approval and informed consent, children referred to British Columbia Children's Hospital for overnight PSG were recruited for two studies including 160 and 75 children, respectively. An additional smartphone-based pulse oximeter sensor was used in both studies to record overnight pulse oximetry [SpO₂ and photoplethysmogram (PPG)] alongside the PSG. Features characterizing SpO₂ dynamics and heart rate variability from pulse peak intervals of the PPG signal were derived from pulse oximetry recordings. Three multivariate logistic regression screening models, targeted at three different levels of OSA severity (AHI ≥ 1, 5, and 10), were developed using stepwise-selection of features using the Bayesian information criterion (BIC). The "Gray Zone" approach was also implemented for different tolerance values to allow for more precise detection of children with inconclusive classification results.

RESULTS

The optimal diagnostic tolerance values defining the "Gray Zone" borders (15, 10, and 5, respectively) were selected to develop the final models to screen for children at AHI cut-offs of 1, 5, and 10. The final models evaluated through cross-validation showed good accuracy (75%, 82% and 89%), sensitivity (80%, 85% and 82%) and specificity (65%, 79% and 91%) values for detecting children with AHI ≥ 1, AHI ≥ 5 and AHI ≥ 10. The percentage of children classified as inconclusive was 28%, 38% and 16% for models detecting AHI ≥ 1, AHI ≥ 5, and AHI ≥ 10, respectively.

CONCLUSIONS

The proposed pulse oximetry-based OSA screening tool at different AHI cut-offs may assist clinicians in identifying children at different OSA severity levels. Using this tool at home prior to PSG can help with optimizing the limited resources for PSG screening. Further validation with larger and more heterogeneous datasets is required before introducing in clinical practice.

Effect of Lifestyle Modification Using a Smartphone Application on Obesity With Obstructive Sleep Apnea: A Short-term, Randomized Controlled Study

Objectives

To investigate the short-term effects of a lifestyle modification intervention based on a mobile application (app) linked to a hospital electronic medical record (EMR) system on weight reduction and obstructive sleep apnea (OSA).

Methods

We prospectively enrolled adults (aged >20 years) with witnessed snoring or sleep apnea from a sleep clinic. The patients were randomized into the app user (n=24) and control (n=23) groups. The mobile app was designed to collect daily lifestyle data by wearing a wrist activity tracker and reporting dietary intake. A summary of the lifestyle data was displayed on the hospital EMR and was reviewed. In the control group, the lifestyle modification was performed as per usual practice. All participants underwent peripheral arterial tonometry (WatchPAT) and body mass index (BMI) measurements at baseline and after 4 weeks of follow-up.

Results

Age and BMI did not differ significantly between the two groups. While we observed a significant decrease in the BMI of both groups, the decrease was greater in the app user group (P <0.001). Apnea-hypopnea index, respiratory distress index, and oxygenation distress index did not change significantly in both groups. However, the proportion of sleep spent snoring at >45 dB was significantly improved in the app user group alone (P =0.014). In either group, among the participants with successful weight reduction, the apnea-hypopnea index was significantly reduced after 4 weeks (P =0.015). Multiple regression analyses showed that a reduction in the apnea-hypopnea index was significantly associated with BMI.

Conclusion

Although a short-term lifestyle modification approach using a mobile app was more effective in achieving weight reduction, improvement in OSA was not so significant. Long-term efficacy of this mobile app should be evaluated in the future studies.

Percutaneous perfusion monitoring for the detection of hemodialysis induced cardiovascular injury

INTRODUCTION

The safe delivery of hemodialysis (HD) faces dual challenges; the accurate detection of systemic circulatory stress producing cardiovascular (CV) injury, and the ability to enable effective preemptive intervention for such injury. We performed a pilot study to examine the capability of a new noninvasive, real-time monitoring system to detect the deleterious effects of HD on CV stability.

METHODS

Eight patients were evaluated with echocardiography prior to the initiation of HD and again at peak HD stress. Continuous CV physiologic monitoring was performed throughout using oximeter-based pulse waveform analysis (CVInsight^® Monitoring System, Intelomed, Inc., Warrendale, PA, USA). Longitudinal strain (LS) values for 12 left ventricular segments were generated using speckle-tracking software (EchoPac, GE), to assess the presence of HD-induced regional wall motion abnormalities (RWMA), indicative of myocardial stunning.

FINDINGS

A reduction in pulse strength (PS) of ≥40% detected by CVI was associated with the development of RWMA (P = 0.005). This reduction occurred in 6/8 patients, all of whom exhibited myocardial stunning. Two patients had no significant reduction in PS nor evidence of myocardial stunning. In subjects with cardiac stunning, the decrease in PS was evident early during HD, 11.49 ± 10 minutes into HD treatment, prior to the detection of RWMA, which were assessed at peak HD stress, mean 210 ± 16.43 minutes into HD treatment.

DISCUSSION

Percutaneous perfusion monitoring, using pulse wave analysis, appears to be useful in identifying circulatory stress during HD and predicting the development of HD-induced myocardial stunning with a lead time long enough to consider timely intervention.

An innovative intermittent hypoxia model for cell cultures allowing fast Po2 oscillations with minimal gas consumption

Performing hypoxia-reoxygenation cycles in cell culture with a cycle duration accurately reflecting what occurs in obstructive sleep apnea (OSA) patients is a difficult but crucial technical challenge. Our goal was to develop a novel device to expose multiple cell culture dishes to intermittent hypoxia (IH) cycles relevant to OSA with limited gas consumption. With gas flows as low as 200 ml/min, our combination of plate holders with gas-permeable cultureware generates rapid normoxia-hypoxia cycles. Cycles alternating 1 min at 20% O2 followed by 1 min at 2% O2 resulted in Po2 values ranging from 124 to 44 mmHg. Extending hypoxic and normoxic phases to 10 min allowed Po2 variations from 120 to 25 mmHg. The volume of culture medium or the presence of cells only modestly affected the Po2 variations. In contrast, the nadir of the hypoxia phase increased when measured at different heights above the membrane. We validated the physiological relevance of this model by showing that hypoxia inducible factor-1α expression was significantly increased by IH exposure in human aortic endothelial cells, murine breast carcinoma (4T1) cells as well as in a blood-brain barrier model (2.5-, 1.5-, and 6-fold increases, respectively). In conclusion, we have established a new device to perform rapid intermittent hypoxia cycles in cell cultures, with minimal gas consumption and the possibility to expose several culture dishes simultaneously. This device will allow functional studies of the consequences of IH and deciphering of the molecular biology of IH at the cellular level using oxygen cycles that are clinically relevant to OSA.

Identifying individual sleep apnea/hypoapnea epochs using smartphone-based pulse oximetry

Sleep apnea, characterized by frequent pauses in breathing during sleep, poses a serious threat to the healthy growth and development of children. Polysomnography (PSG), the gold standard for sleep apnea diagnosis, is resource intensive and confined to sleep laboratories, thus reducing its accessibility. Pulse oximetry alone, providing blood oxygen saturation (SpO2) and blood volume changes in tissue (PPG), has the potential to identify children with sleep apnea. Thus, we aim to develop a tool for at-home sleep apnea screening that provides a detailed and automated 30 sec epoch-by-epoch sleep apnea analysis. We propose to extract features characterizing pulse oximetry (SpO2 and pulse rate variability [PRV], a surrogate measure of heart rate variability) to create a multivariate logistic regression model that identifies epochs containing apnea/hypoapnea events. Overnight pulse oximetry was collected using a smartphone-based pulse oximeter, simultaneously with standard PSG from 160 children at the British Columbia Children's hospital. The sleep technician manually scored all apnea/hypoapnea events during the PSG study. Based on these scores we labeled each epoch as containing or not containing apnea/hypoapnea. We randomly divided the subjects into training data (40%), used to develop the model applying the LASSO method, and testing data (60%), used to validate the model. The developed model was assessed epoch-by-epoch for each subject. The test dataset had a median area under the receiver operating characteristic (ROC) curve of 81%; the model provided a median accuracy of 74% sensitivity of 75%, and specificity of 73% when using a risk threshold similar to the percentage of apnea/hypopnea epochs. Thus, providing a detailed epoch-by-epoch analysis with at-home pulse oximetry alone is feasible with accuracy, sensitivity and specificity values above 73% However, the performance might decrease when analyzing subjects with a low number of apnea/hypoapnea events.

Obstructive Sleep Apnea Screening Using a Piezo-Electric Sensor

In this study, we propose a novel method for obstructive sleep apnea (OSA) detection using a piezo-electric sensor. OSA is a relatively common sleep disorder. However, more than 80% of OSA patients remain undiagnosed. We investigated the feasibility of OSA assessment using a single-channel physiological signal to simplify the OSA screening. We detected both snoring and heartbeat information by using a piezo-electric sensor, and snoring index (SI) and features based on pulse rate variability (PRV) analysis were extracted from the filtered piezo-electric sensor signal. A support vector machine (SVM) was used as a classifier to detect OSA events. The performance of the proposed method was evaluated on 45 patients from mild, moderate, and severe OSA groups. The method achieved a mean sensitivity, specificity, and accuracy of 72.5%, 74.2%, and 71.5%; 85.8%, 80.5%, and 80.0%; and 70.3%, 77.1%, and 71.9% for the mild, moderate, and severe groups, respectively. Finally, these results not only show the feasibility of OSA detection using a piezo-electric sensor, but also illustrate its usefulness for monitoring sleep and diagnosing OSA.

A Brief Review of Non-invasive Monitoring of Respiratory Condition for Extubated Patients with or at Risk for Obstructive Sleep Apnea after Surgery

Obstructive sleep apnea (OSA) is one of the important risk factors contributing to postoperative airway complications. OSA alters the respiratory physiology and increases the sensitivity of muscle tone of the upper airway after surgery to residual anesthetic medication. In addition, the prevalence of OSA was reported to be much higher among surgical patients than the general population. Therefore, appropriate monitoring to detect early respiratory impairment in postoperative extubated patients with possible OSA is challenging. Based on the comprehensive clinical observation, several equipment have been used for monitoring the respiratory conditions of OSA patients after surgery, including the continuous pulse oximetry, capnography, photoplethysmography (PPG), and respiratory volume monitor (RVM). To date, there has been no consensus on the most suitable device as a recommended standard of care. In this review, we describe the advantages and disadvantages of some possible monitoring strategies under certain clinical conditions. According to the literature, the continuous pulse oximetry, with its high sensitivity, is still the most widely used device. It is also cost-effective and convenient to use but has low specificity and does not reflect ventilation. Capnography is the most widely used device for detection of hypoventilation, but it may not provide reliable data for extubated patients. Even normal capnography cannot exclude the existence of hypoxia. PPG shows the state of both ventilation and oxygenation, but its sensitivity needs further improvement. RVM provides real-time detection of hypoventilation, quantitative precise demonstration of respiratory rate, tidal volume, and MV for extubated patients, but no reflection of oxygenation. Altogether, the sole use of any of these devices is not ideal for monitoring of extubated patients with or at risk for OSA after surgery. However, we expect that the combined use of continuous pulse oximetry and RVM may be promising for these patients due to their complementary function, which need further study.

A Brief Review of Non-invasive Monitoring of Respiratory Condition for Extubated Patients with or at Risk for Obstructive Sleep Apnea after Surgery

Obstructive sleep apnea (OSA) is one of the important risk factors contributing to postoperative airway complications. OSA alters the respiratory physiology and increases the sensitivity of muscle tone of the upper airway after surgery to residual anesthetic medication. In addition, the prevalence of OSA was reported to be much higher among surgical patients than the general population. Therefore, appropriate monitoring to detect early respiratory impairment in postoperative extubated patients with possible OSA is challenging. Based on the comprehensive clinical observation, several equipment have been used for monitoring the respiratory conditions of OSA patients after surgery, including the continuous pulse oximetry, capnography, photoplethysmography (PPG), and respiratory volume monitor (RVM). To date, there has been no consensus on the most suitable device as a recommended standard of care. In this review, we describe the advantages and disadvantages of some possible monitoring strategies under certain clinical conditions. According to the literature, the continuous pulse oximetry, with its high sensitivity, is still the most widely used device. It is also cost-effective and convenient to use but has low specificity and does not reflect ventilation. Capnography is the most widely used device for detection of hypoventilation, but it may not provide reliable data for extubated patients. Even normal capnography cannot exclude the existence of hypoxia. PPG shows the state of both ventilation and oxygenation, but its sensitivity needs further improvement. RVM provides real-time detection of hypoventilation, quantitative precise demonstration of respiratory rate, tidal volume, and MV for extubated patients, but no reflection of oxygenation. Altogether, the sole use of any of these devices is not ideal for monitoring of extubated patients with or at risk for OSA after surgery. However, we expect that the combined use of continuous pulse oximetry and RVM may be promising for these patients due to their complementary function, which need further study.

During economic crisis can sleep questionnaires improve the value of oximetry for assessing sleep apnea?

BACKGROUND

The diagnosis of obstructive sleep apnea/hypopnea syndrome (OSAHS) is essential but polysomnography (PSG) is expensive and time consuming. Oximetry has been used as a less expensive indicator of OSAHS. The aim of the study was to evaluate the clinical utility of the combination of oximetry with four different questionnaires: Stop, Stop Bang (S-B), Berlin questionnaire (BQ), Epworth Sleepiness Scale (ESS) in order to identify patients at risk for OSAHS compared with in-laboratory PSG.

METHODS

Patients visiting a sleep clinic were prospectively studied. They completed Stop, S-B, BQ and ESS. Home oximetry and in laboratory PSG were performed within 3-20 days.

RESULTS

A total of 204 patients were included in the study (77.5% males, mean age 51.8±13.8 years, BMI 32.8±6.2 kg/m², SaO₂% awake 95.7±2). S-B had the highest sensitivity (Se) (97.5%) and negative predictive value (NPV) (62.5%) but the lowest specificity (Sp) (9%), whereas ESS had the best Sp (75%) and positive predictive values (PPV) (81.4%). The predictive values of questionnaires improved as the severity of OSAHS worsened. The predictive values of oximetry were high for severe but low for mild and moderate OSAHS. For that oximetry was combined with different sleep questionnaires in different OSAHS severity groups, but with no improvement in the predictive values.

CONCLUSIONS

Oximetry may be used as a tool for identifying severe OSAHS. For mild and moderate disease the combination of questionnaires did not improve the diagnostic accuracy and especially for symptomatic patients with negative results, the need of PSG is essential.

Pulse oximetry recorded from the Phone Oximeter for detection of obstructive sleep apnea events with and without oxygen desaturation in children

Obstructive sleep apnea (OSA) disrupts normal ventilation during sleep and can lead to serious health problems in children if left untreated. Polysomnography, the gold standard for OSA diagnosis, is resource intensive and requires a specialized laboratory. Thus, we proposed to use the Phone Oximeter™, a portable device integrating pulse oximetry with a smartphone, to detect OSA events. As a proportion of OSA events occur without oxygen desaturation (defined as SpO2 decreases ≥ 3%), we suggest combining SpO2 and pulse rate variability (PRV) analysis to identify all OSA events and provide a more detailed sleep analysis. We recruited 160 children and recorded pulse oximetry consisting of SpO2 and plethysmography (PPG) using the Phone Oximeter™, alongside standard polysomnography. A sleep technician visually scored all OSA events with and without oxygen desaturation from polysomnography. We divided pulse oximetry signals into 1-min signal segments and extracted several features from SpO2 and PPG analysis in the time and frequency domain. Segments with OSA, especially the ones with oxygen desaturation, presented greater SpO2 variability and modulation reflected in the spectral domain than segments without OSA. Segments with OSA also showed higher heart rate and sympathetic activity through the PRV analysis relative to segments without OSA. PRV analysis was more sensitive than SpO2 analysis for identification of OSA events without oxygen desaturation. Combining SpO2 and PRV analysis enhanced OSA event detection through a multiple logistic regression model. The area under the ROC curve increased from 81% to 87%. Thus, the Phone Oximeter™ might be useful to monitor sleep and identify OSA events with and without oxygen desaturation at home.

Respiratory rate and pulse oximetry derived information as predictors of hospital admission in young children in Bangladesh: a prospective observational study

OBJECTIVE

Hypoxaemia is a strong predictor of mortality in children. Early detection of deteriorating condition is vital to timely intervention. We hypothesise that measures of pulse oximetry dynamics may identify children requiring hospitalisation. Our aim was to develop a predictive tool using only objective data derived from pulse oximetry and observed respiratory rate to identify children at increased risk of hospital admission.

SETTING

Tertiary-level hospital emergency department in Bangladesh.

PARTICIPANTS

Children under 5 years (n=3374) presenting at the facility (October 2012-April 2013) without documented chronic diseases were recruited. 1-minute segments of pulse oximetry (photoplethysmogram (PPG), blood oxygen saturation (SpO2) and heart rate (HR)) and respiratory rate were collected with a mobile app.

PRIMARY OUTCOME

The need for hospitalisation based on expert physician review and follow-up.

METHODS

Pulse rate variability (PRV) using pulse peak intervals of the PPG signal and features extracted from the SpO2 signal, all derived from pulse oximetry recordings, were studied. A univariate age-adjusted logistic regression was applied to evaluate differences between admitted and non-admitted children. A multivariate logistic regression model was developed using a stepwise selection of predictors and was internally validated using bootstrapping.

RESULTS

Children admitted to hospital showed significantly (p<0.01) decreased PRV and higher SpO2 variability compared to non-admitted children. The strongest predictors of hospitalisation were reduced PRV-power in the low frequency band (OR associated with a 0.01 unit increase, 0.93; 95% CI 0.89 to 0.98), greater time spent below an SpO2 of 98% and 94% (OR associated with 10 s increase, 1.4; 95% CI 1.3 to 1.4 and 1.5; 95% CI 1.4 to 1.6, respectively), high respiratory rate, high HR, low SpO2, young age and male sex. These variables provided a bootstrap-corrected AUC of the receiver operating characteristic of 0.76.

CONCLUSIONS

Objective measurements, easily obtained using a mobile device in low-resource settings, can predict the need for hospitalisation. External validation will be required before clinical adoption.

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.

Prevalence of Sleep Disordered Breathing among Patients with Nocturia at a Urology Clinic

OBJECTIVE

We assessed the prevalence of sleep disordered breathing (SDB) and characteristics among patients who visited a urology clinic complaining of nocturia (URO group) and those who visited a sleep apnea (SA) clinic complaining of excessive daytime sleepiness (EDS) (SA group). Additionally, we evaluated the effects of continuous positive airway pressure (CPAP) therapy in the URO group patients with nocturia and SDB resistant to conventional therapy for nocturia.

METHODS

Questionnaires were used to assess EDS, nocturia and lower urinary tract symptoms in 34 URO group patients and 49 age-matched SA group patients. We also compared these factors in the male patients in both groups and the male and female patients in the SA group. Significant SDB was diagnosed as a 3% oxygen desaturation index (3%ODI) on pulse oximeter of >5/h. The treatment response was analyzed in six URO group patients treated with CPAP after not responding to the conventional medical treatment.

RESULTS

SDB was found in 91.8% of the SA group patients and 70.6% of the URO group patients. The level of EDS and lower urinary tract symptoms were similar in both groups. The SA group showed higher 3%ODI values, while the frequency of urination during bedtime was higher in the URO group. The frequency of nocturnal urination was reduced after CPAP in the subjects resistant to conventional therapy.

CONCLUSION

SDB is as prevalent in patients who visit a urology clinic complaining of nocturia as in those who visit a sleep apnea clinic. Patients who complains of nocturia must be assessed for SDB before starting therapy for nocturia.

Decreased exercise capacity and sleep-disordered breathing in patients with hypertrophic cardiomyopathy

BACKGROUND

Mechanisms of decreased exercise capacity in patients with hypertrophic cardiomyopathy (HCM) are not well understood. Sleep-disordered breathing (SDB) is a highly prevalent but treatable disorder in patients with HCM. The role of comorbid SDB in the attenuated exercise capacity in HCM has not been studied previously.

METHODS

Overnight oximetry, cardiopulmonary exercise testing, and echocardiographic studies were performed in consecutive patients with HCM seen at the Mayo Clinic. SDB was considered present if the oxygen desaturation index (number of ≥ 4% desaturations/h) was ≥ 10. Peak oxygen consumption (VO2 peak) (the most reproducible and prognostic measure of cardiovascular fitness) was then correlated with the presence and severity of SDB.

RESULTS

A total of 198 patients with HCM were studied (age, 53 ± 16 years; 122 men), of whom 32% met the criteria for the SDB diagnosis. Patients with SDB had decreased VO2 peak compared with those without SDB (16 mL O2/kg/min vs 21 mL O2/kg/min, P < .001). SDB remained significantly associated with VO2 peak after accounting for confounding clinical variables (P < .001) including age, sex, BMI, atrial fibrillation, and coronary artery disease.

CONCLUSIONS

In patients with HCM, the presence of SDB is associated with decreased VO2 peak. SDB may represent an important and potentially modifiable contributor to impaired exercise tolerance in this unique population.

Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review

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.

Exaggerated blood pressure variability in patients with pneumoconiosis: a pilot study

BACKGROUND

Chronic hypoxia is a risk factor for cardiovascular disease, but its association with blood pressure (BP) remains unclear. We performed this study to clarify the hypothesis that chronic hypoxia is associated with abnormal BP variability in patients with pneumoconiosis.

METHODS

We recruited 19 patients with pneumoconiosis and 30 age- and BP level-matched control subjects. We used simultaneous pulse oximetry and ambulatory BP monitoring for all subjects. We evaluated their BP levels and variability by determining the SD and coefficient of variation (CV) of the BP data. The dipper pattern was defined as nocturnal BP fall ≥10%; the nondipper pattern was defined as nocturnal BP fall ≥0% but <10%; the riser pattern was defined as nocturnal BP fall <0%. Pearson and Spearman correlation coefficients were used to calculate the correlations between parameters.

RESULTS

In patients with pneumoconiosis, the daytime systolic BP (SBP) level was lower, the CV in 24-hour SBP (P < 0.05) and diastolic BP (P < 0.001) were higher than that in control subjects. And although not statistically significant (P = 0.13), the odds ratio of riser pattern was 3.73 in patients with pneumoconiosis, and their nighttime pulse rate was significantly higher (P < 0.05) than that in control subjects. The median daytime pulse oximetry oxygen saturation was inversely associated with mean (r = -0.30; P < 0.01) and SD (r = -0.38; P < 0.001) in daytime SBP. The median nighttime pulse oximetry oxygen saturation was inversely associated (r = -0.55; P < 0.001) and the mean nighttime pulse rate was associated (r = 0.51; P < 0.001) with CV in nighttime SBP. Partial pressure of oxygen was inversely associated with CV in daytime SBP (r = -0.24; P < 0.05).

CONCLUSIONS

Exaggerated BP variability was seen in patients with pneumoconiosis, and the measures of hypoxia were associated with large fluctuations in ambulatory BP. Chronic and intermittent hypoxia could be the contributing factors of these findings.

CLINICAL TRIAL REGISTRATION

Clinical Trial Number UMIN000000894 (University Hospital Medical Information Network Clinical Trials Registry website).

Vibration signals of snoring as a simple severity predictor for obstructive sleep apnea

BACKGROUND AND AIM

Polysomnography (PSG), which involves simultaneous monitoring of various physiological monitors, is the current comprehensive tool for diagnosing obstructive sleep apnea (OSA). We aimed at validating vibrating signals of snoring as a single physiological parameter for screening and evaluating severity of OSA.

METHODS

Totally, 111 subjects from the sleep center of a tertiary referral center were categorized into four groups according to the apnea hypopnea index (AHI) obtained from PSG: simple snoring group (5 > AHI, healthy subjects, n = 11), mild OSA group (5 ≤ AHI < 15, n = 11), moderate OSA group (15 ≤ AHI < 30, n = 30) and severe OSA group (AHI ≥ 30, n = 59). Anthropometric parameters and sleep efficiency of all subjects were compared. Frequencies of amplitude changes of vibrating signals on anterior neck during sleep were analyzed to acquire a snoring burst index (SBI) using a novel algorithm. Data were compared with AHI and index of arterial oxygen saturation (Δ Index).

RESULTS

There were no significant differences in age and sleep efficiency among all groups. Bland-Altman analysis showed better agreement between SBI and AHI (r = 0.906, P < 0.001) than Δ Index and AHI (r = 0.859, P < 0.001). Additionally, receiver operating characteristic (ROC) showed substantially stronger sensitivity and specificity of SBI in distinguishing between patients with moderate and severe OSA compared with Δ Index (sensitivity: 81.4% vs 66.4%; specificity: 96.7% vs 86.7%, for SBI and Δ Index, respectively).

CONCLUSION

SBI may serve as a portable tool for screening patients and assessing OSA severity in a non-hospital setting.

Emerging risk factors and prevention of perioperative pulmonary complications

Modern surgery is faced with the emergence of newer "risk factors" and the challenges associated with identifying and managing these risks in the perioperative period. Obstructive sleep apnea and obesity hypoventilation syndrome pose unique challenges in the perioperative setting. Recent studies have identified some of the specific risks arising from caring for such patients in the surgical setting. While all possible postoperative complications are not yet fully established or understood, the prevention and management of these complications pose even greater challenges. Pulmonary hypertension with its changing epidemiology and novel management strategies is another new disease for the surgeon and the anesthesiologist in the noncardiac surgical setting. Traditionally most such patients were not considered surgical candidates for any required elective surgery. Our review discusses these disease entities which are often undiagnosed before elective noncardiac surgery.

Probabilistic neural network approach for the detection of SAHS from overnight pulse oximetry

Diagnosis of sleep apnea hypopnoea syndrome (SAHS) depends on the apnea-hypopnea index determined by the standard in-laboratory overnight polysomnography (PSG). PSG is a costly, labor intensive and, at times, inaccessible approach. Because of the high demand, the need for timely diagnosis and the associated costs, novel methods for SAHS detection are required. In this study, a novel multivariate system is proposed for SAHS detection from the analysis of overnight blood oxygen saturation (SpO2). 115 subjects with SAHS suspicion were studied. A starting set of 17 time domain, stochastic, frequency-domain and nonlinear features were initially computed from SpO2 recordings. Sequential forward feature selection and a probabilistic neural network with leave-one-out cross-validation were applied. Oxygen desaturations below a 4 % threshold within 30 s (ODI430), restorations of 4 % within 10 s (RES4), median value (Sat50), SD1 Poincaré descriptor and the relative power in the 0.013-0.067 Hz frequency band (PSD15/75) formed the optimum features subset. 92.4 % sensitivity and 95.9 % specificity were achieved. Results significantly outperformed the univariate and multivariate approaches reported in literature. The outcome is a simple cost-effective tool that could be used as an alternative or supplementary method in a domiciliary approach to early diagnosis of SAHS.

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.

Interrelationships between body mass, oxygen desaturation, and apnea-hypopnea indices in a sleep clinic population

STUDY OBJECTIVES

To investigate the relationship between oxygen desaturation index (ODI), body mass index (BMI), and apnea-hypopnea index (AHI) in a large sleep clinic population.

DESIGN

Retrospective observational.

SETTING

Sleep disorders clinic.

PATIENTS OR PARTICIPANTS

11,448 individuals undergoing diagnostic polysomnography (PSG) at a sleep disorders clinic.

MEASUREMENTS AND RESULTS

Polysomnography were scored using Chicago criteria. ODI at 2%, 3%, and 4% threshold levels were derived. The study population was subdivided into BMI categories in steps of 5 kg/m(2). Mean ODI and the accuracy of ODI for detecting an AHI ≥ 15 (moderate-severe OSA) or ≥ 30 (severe OSA) were examined by BMI category, using the area under the curve (AUC) of receiver operator characteristic (ROC) curves for the 3 ODI thresholds. Based on AUC, ODI-3% performed best overall, achieving a significantly higher AUC than ODI-2% and ODI-4% for the diagnosis of moderate-severe OSA, and a higher AUC than ODI-2% for the diagnosis of severe OSA. When examining the effect of BMI, ODI-3% achieved a significantly higher AUC than ODI-2% in all BMI categories, and ODI-4% in non-obese subjects. The sensitivity of ODI for detecting OSA increased with BMI, while specificity decreased.

CONCLUSIONS

ODI-3% performed best overall, and when combined with appropriate clinical assessment, could be considered as an initial diagnostic test for OSA. OSA is more frequently associated with oxygen desaturation in obese subjects. BMI influences the accuracy of ODI for the diagnosis of OSA, and ODI should not be used in isolation as a test for OSA in subjects with a BMI below 25kg/m(2).

Feature selection from nocturnal oximetry using genetic algorithms to assist in obstructive sleep apnoea diagnosis

Nocturnal pulse oximetry (NPO) has demonstrated to be a powerful tool to help in obstructive sleep apnoea (OSA) detection. However, additional analysis is needed to use NPO alone as an alternative to nocturnal polysomnography (NPSG), which is the gold standard for a definitive diagnosis. In the present study, we exhaustively analysed a database of blood oxygen saturation (SpO(2)) recordings (80 OSA-negative and 160 OSA-positive) to obtain further knowledge on the usefulness of NPO. Population set was randomly divided into training and test sets. A feature extraction stage was carried out: 16 features (time and frequency statistics and spectral and nonlinear features) were computed. A genetic algorithm (GA) approach was applied in the feature selection stage. Our methodology achieved 87.5% accuracy (90.6% sensitivity and 81.3% specificity) in the test set using a logistic regression (LR) classifier with a reduced number of complementary features (3 time domain statistics, 1 frequency domain statistic, 1 conventional spectral feature and 1 nonlinear feature) automatically selected by means of GAs. Our results improved diagnostic performance achieved with conventional oximetric indexes commonly used by physicians. We concluded that GAs could be an effective and robust tool to search for essential oximetric features that could enhance NPO in the context of OSA diagnosis.