Utility of oxygen saturation and heart rate spectral analysis obtained from pulse oximetric recordings in the diagnosis of sleep apnea syndrome

Overview of a Sleep Monitoring Protocol for a Large Natural Population

A standard operating procedure for studying the sleep phenotypes in a large population cohort is proposed. It is intended for academic researchers in investigating the sleep phenotypes in conjunction with the clinical sleep disorders assessment guidelines. The protocol refers to the definitive American Academy of Sleep Medicine (AASM) manual for setting polysomnography (PSG) technical specifications, scoring of sleep and associated events, etc. On this basis, it not only provides a standardized procedure of sleep interview, sleep-relevant questionnaires, and laboratory-based PSG test, but also offers a comprehensive process of sleep data analysis, phenotype extraction, and data storage. Both the objective sleep data recorded by PSG test and subjective sleep information obtained by the sleep interview and sleep questionnaires are involved in the data acquisition procedure. Subsequently, sleep phenotypes can be characterized by observable/inconspicuous physiological patterns during sleep from PSG test or can be marked by sleeping habits like sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, daytime dysfunction, etc., from sleep interview or questionnaires derived. In addition, solutions to the problems that may be encountered during the protocol are summarized and addressed. With the protocol, it can significantly improve scientific research efficiency and reduce unnecessary workload in large population cohort studies. Moreover, it is also expected to provide a valuable reference for researchers to conduct systematic sleep research.

Combining Heart Rate Variability and Oximetry to Improve Apneic Event Screening in Non-Desaturating Patients

In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO₂, and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO₂-based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study.

Relative spectral power quantifying the distribution of intermittent hypoxemia in obstructive sleep apnea is strongly associated with hypertension

STUDY OBJECTIVES

To investigate the association between the periodicity of distribution of intermittent hypoxemia (IH) and hypertension in adults with obstructive sleep apnea (OSA) and search for an index to quantify the association.

METHODS

Samples were derived from two cross-sectional studies: The Sleep Heart Health Study (SHHS) including 3991 adults with age 64.7 ± 10.9 years; and the Chinese Changgung Sleep Health Study (CSHS) including 906 adults with age 59.5 ± 12.4 years. Spectral analysis of peripheral oxygen saturation (SpO₂) was performed and the relative spectral power (PFR) in the frequency band of 0.011-0.037 Hz (PFR_{0.011-0.037Hz}) was extracted to quantify the periodic distribution of IH. Multiple logistic regression models were used to calculate the partially and fully adjusted odd ratios for PFR_{0.011-0.037Hz}.

RESULTS

PFR_{0.011-0.037Hz} was significantly higher in the hypertension group than non-hypertension group (44.4% ± 0.3% vs. 42.1% ± 0.3%, p < 0.001 in SHHS and 57.4% ± 0.7% vs. 50.5% ± 0.8%, p < 0.001 in CSHS). In the fully adjusted model, individuals in the SHHS with PFR_{0.011-0.037Hz} in the highest quintiles had an odd ratio of 1.33 [95% confidence interval (CI) 1.06-1.67]. Similarly, the group in the CSHS with PFR_{0.011-0.037Hz} in the highest quintile had an odd ratio of 3.08 (95% CI 1.80-5.28).

CONCLUSIONS

We developed an IH distribution measure which is strongly associated with hypertension independent of multiple confounding variables. The finding suggests that the periodic distribution of sleep related upper airway obstructions is an essential hypertension characterizing feature.

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.

A model for obstructive sleep apnea detection using a multi-layer feed-forward neural network based on electrocardiogram, pulse oxygen saturation, and body mass index

PURPOSE

To develop and evaluate a model for obstructive sleep apnea (OSA) detection using an artificial neural network (ANN) based on the combined features of body mass index (BMI), electrocardiogram (ECG), and pulse oxygen saturation (SpO2).

METHODS

Polysomnography (PSG) data for 148 patients with OSA and 33 unaffected individuals were included. A multi-layer feed-forward neural network (FNN) was used based on the features obtained from ECG, SpO2, and BMI. The receiver operating characteristic (ROC) curve and the metrics of accuracy, sensitivity, and specificity were used to evaluate the performance of the overall classification. Some other machine learning methods including linear discriminant, linear Support Vector Machine (SVM), Complex Tree, RUSBoosted Trees, and Logistic Regression were also used to compare their performance with the FNN.

RESULTS

The accuracy, sensitivity, and specificity of the proposed multi-layer FNN were 97.8%, 98.6%, and 93.9%, respectively, and the area under the ROC curve was 97.0%. Compared with the other machine learning methods mentioned above, the FNN achieved the highest performance.

CONCLUSIONS

The satisfactory performance of the proposed FNN model for OSA detection indicated that it is reliable to screen potential patients with OSA using the combined channels of ECG and SpO2 and also taking into account BMI. This strategy might be a viable alternative method for OSA diagnosis.

Digital oximetry biomarkers for assessing respiratory function: standards of measurement, physiological interpretation, and clinical use

Pulse oximetry is routinely used to non-invasively monitor oxygen saturation levels. A low oxygen level in the blood means low oxygen in the tissues, which can ultimately lead to organ failure. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous oxygen saturation time series variability analysis. The primary objective of this research was to identify, implement and validate key digital oximetry biomarkers (OBMs) for the purpose of creating a standard and associated reference toolbox for continuous oximetry time series analysis. We review the sleep medicine literature to identify clinically relevant OBMs. We implement these biomarkers and demonstrate their clinical value within the context of obstructive sleep apnea (OSA) diagnosis on a total of n = 3806 individual polysomnography recordings totaling 26,686 h of continuous data. A total of 44 digital oximetry biomarkers were implemented. Reference ranges for each biomarker are provided for individuals with mild, moderate, and severe OSA and for non-OSA recordings. Linear regression analysis between biomarkers and the apnea hypopnea index (AHI) showed a high correlation, which reached [Formula: see text]. The resulting python OBM toolbox, denoted "pobm", was contributed to the open software PhysioZoo ( physiozoo.org ). Studying the variability of the continuous oxygen saturation time series using pbom may provide information on the underlying physiological control systems and enhance our understanding of the manifestations and etiology of diseases, with emphasis on respiratory diseases.

Neural network analysis of nocturnal SpO2 signal enables easy screening of sleep apnea in patients with acute cerebrovascular disease

Current diagnostics of sleep apnea relies on the time-consuming manual analysis of complex sleep registrations, which is impractical for routine screening in hospitalized patients with a high probability for sleep apnea, e.g. those experiencing acute stroke or transient ischemic attacks (TIA). To overcome this shortcoming, we aimed to develop a convolutional neural network (CNN) capable of estimating the severity of sleep apnea in acute stroke and TIA patients based solely on the nocturnal oxygen saturation (SpO₂) signal. The CNN was trained with SpO₂ signals derived from 1379 home sleep apnea tests (HSAT) of suspected sleep apnea patients and tested with SpO₂ signals of 77 acute ischemic stroke or TIA patients. The CNN's performance was tested by comparing the estimated respiratory event index (REI) and oxygen desaturation index (ODI) with manually obtained values. Median estimation errors for REI and ODI in patients with stroke or TIA were 1.45 events/hour and 0.61 events/hour, respectively. Furthermore, based on estimated REI and ODI, 77.9% and 88.3% of these patients were classified into the correct sleep apnea severity categories. The sensitivity and specificity to identify sleep apnea (REI > 5 events/hour) were 91.8% and 78.6%, respectively. Moderate-to-severe sleep apnea was detected (REI > 15 events/hour) with sensitivity of 92.3% and specificity of 96.1%. The CNN analysis of the SpO₂ signal has great potential as a simple screening tool for sleep apnea. This novel automatic method accurately detects sleep apnea in acute cerebrovascular disease patients and facilitates their referral for a differential diagnostic HSAT or polysomnography evaluation.

Influence of Chronic Obstructive Pulmonary Disease and Moderate-To-Severe Sleep Apnoea in Overnight Cardiac Autonomic Modulation: Time, Frequency and Non-Linear Analyses

Chronic obstructive pulmonary disease (COPD) is one of the most prevalent lung diseases worldwide. COPD patients show major dysfunction in cardiac autonomic modulation due to sustained hypoxaemia, which has been significantly related to higher risk of cardiovascular disease. Obstructive sleep apnoea syndrome (OSAS) is a frequent comorbidity in COPD patients. It has been found that patients suffering from both COPD and OSAS simultaneously, the so-called overlap syndrome, have notably higher morbidity and mortality. Heart rate variability (HRV) has demonstrated to be useful to assess changes in autonomic functioning in different clinical conditions. However, there is still little scientific evidence on the magnitude of changes in cardiovascular dynamics elicited by the combined effect of both respiratory diseases, particularly during sleep, when apnoeic events occur. In this regard, we hypothesised that a non-linear analysis is able to provide further insight into long-term dynamics of overnight cardiovascular modulation. Accordingly, this study is aimed at assessing the usefulness of sample entropy (SampEn) to distinguish changes in overnight pulse rate variability (PRV) recordings among three patient groups while sleeping: COPD, moderate-to-severe OSAS, and overlap syndrome. In order to achieve this goal, a population composed of 297 patients were studied: 22 with COPD alone, 213 showing moderate-to-severe OSAS, and 62 with COPD and moderate-to-severe OSAS simultaneously (COPD+OSAS). Cardiovascular dynamics were analysed using pulse rate (PR) recordings from unattended pulse oximetry carried out at patients’ home. Conventional time- and frequency- domain analyses were performed to characterise sympathetic and parasympathetic activation of the nervous system, while SampEn was applied to quantify long-term changes in irregularity. Our analyses revealed that overnight PRV recordings from COPD+OSAS patients were significantly more irregular (higher SampEn) than those from patients with COPD alone (0.267 [0.210–0.407] vs. 0.212 [0.151–0.267]; p < 0.05) due to recurrent apnoeic events during the night. Similarly, COPD + OSAS patients also showed significantly higher irregularity in PRV during the night than subjects with OSAS alone (0.267 [0.210–0.407] vs. 0.241 [0.189–0.325]; p = 0.05), which suggests that the cumulative effect of both diseases increases disorganization of pulse rate while sleeping. On the other hand, no statistical significant differences were found between COPD and COPD + OSAS patients when traditional frequency bands (LF and HF) were analysed. We conclude that SampEn is able to properly quantify changes in overnight cardiovascular dynamics of patients with overlap syndrome, which could be useful to assess cardiovascular impairment in COPD patients due to the presence of concomitant OSAS.

Assessment of automated analysis of portable oximetry as a screening test for moderate-to-severe sleep apnea in patients with chronic obstructive pulmonary disease

BACKGROUND

The coexistence of obstructive sleep apnea syndrome (OSAS) and chronic obstructive pulmonary disease (COPD) leads to increased morbidity and mortality. The development of home-based screening tests is essential to expedite diagnosis. Nevertheless, there is still very limited evidence on the effectiveness of portable monitoring to diagnose OSAS in patients with pulmonary comorbidities.

OBJECTIVE

To assess the influence of suffering from COPD in the performance of an oximetry-based screening test for moderate-to-severe OSAS, both in the hospital and at home.

METHODS

A total of 407 patients showing moderate-to-high clinical suspicion of OSAS were involved in the study. All subjects underwent (i) supervised portable oximetry simultaneously to in-hospital polysomnography (PSG) and (ii) unsupervised portable oximetry at home. A regression-based multilayer perceptron (MLP) artificial neural network (ANN) was trained to estimate the apnea-hypopnea index (AHI) from portable oximetry recordings. Two independent validation datasets were analyzed: COPD versus non-COPD.

RESULTS

The portable oximetry-based MLP ANN reached similar intra-class correlation coefficient (ICC) values between the estimated AHI and the actual AHI for the non-COPD and the COPD groups either in the hospital (non-COPD: 0.937, 0.909-0.956 CI95%; COPD: 0.936, 0.899-0.960 CI95%) and at home (non-COPD: 0.731, 0.631-0.808 CI95%; COPD: 0.788, 0.678-0.864 CI95%). Regarding the area under the receiver operating characteristics curve (AUC), no statistically significant differences (p >0.01) between COPD and non-COPD groups were found in both settings, particularly for severe OSAS (AHI ≥30 events/h): 0.97 (0.92-0.99 CI95%) non-COPD vs. 0.98 (0.92-1.0 CI95%) COPD in the hospital, and 0.87 (0.79-0.92 CI95%) non-COPD vs. 0.86 (0.75-0.93 CI95%) COPD at home.

CONCLUSION

The agreement and the diagnostic performance of the estimated AHI from automated analysis of portable oximetry were similar regardless of the presence of COPD both in-lab and at-home. Particularly, portable oximetry could be used as an abbreviated screening test for moderate-to-severe OSAS in patients with COPD.

Waist circumference and postmenopause stages as the main associated factors for sleep apnea in women: a cross-sectional population-based study

OBJECTIVE

The current study aimed to investigate stages of reproductive aging as an associated factor for obstructive sleep apnea syndrome (OSAS) among women in a representative sample of Sao Paulo, Brazil.

METHODS

Four hundred seven women underwent clinical evaluation, polysomnography, and biochemical analysis. Stages of reproductive aging were defined as premenopause, early postmenopause, and late postmenopause.

RESULTS

OSAS was more frequent in the postmenopausal groups, with 68.4% of women affected by severe OSAS belonging to the late postmenopause group. After adjustment for potential confounding factors, associated factors for OSAS, regardless of its severity, were waist circumference, modified Mallampati score IV, and both postmenopause stages. For moderate to severe OSAS and severe OSAS, we found waist circumference and both postmenopause stages to be the main factors. We carried out a receiver operating characteristic curve analysis, which demonstrated that the cutoff value for waist circumference was 87.5 cm, with a maximum of 75.7% accuracy for the classification of women as OSAS or non-OSAS.

CONCLUSIONS

OSAS is prevalent in postmenopausal women, especially in late postmenopause. This study highlights the association between waist circumference, early postmenopause and late postmenopause, and severity of OSAS. Our findings suggest that postmenopause stages may potentially exacerbate the presence of sleep disturbance and that reducing waist circumference may be an important strategy for managing OSAS in women.

A REVIEW OF ECG-BASED DIAGNOSIS SUPPORT SYSTEMS FOR OBSTRUCTIVE SLEEP APNEA

Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy.

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.

Robust spectral analysis of thoraco-abdominal motion and oxymetry in obstructive sleep apnea

The diagnosis of obstructive sleep apnea (OSA) relies on polysomnography (PSG), a multidimensional biosignal recording that is conducted in sleep laboratories. Standard PSG montage involves the use of nasal-oral airflow sensors to visualize cyclic episodes of upper airflow interruption, which are considered diagnostic of sleep apnea. Given the high-cost and discomfort associated with in-laboratory PSG, there is an emergent need for novel technology that simplifies OSA screening and diagnosis with less expensive methods. The main goal of this project was to identify novel OSA signatures based on the spectral analysis of thoraco-abdominal motion channels. Our main hypothesis was that proper spectral analysis can detect OSA cycles in adults using simultaneous recording of oxygen saturation (SaO2) and either, chest or abdominal motion. A sample study on 35 individuals was conducted with statistically significant results that suggest a strong relationship between airflow-independent signals and oxygen saturation. The impact of this new approach is that it may allow the design of more comfortable and reliable portable devices for screening, diagnosis and monitoring of OSA, functioning only with oximetry and airflow-independent (abdominal or chest) breathing sensors.

Analysis of nocturnal oxygen saturation recordings using kernel entropy to assist in sleep apnea-hypopnea diagnosis

In this study, a new entropy measure known as kernel entropy (KerEnt), which quantifies the irregularity in a series, was applied to nocturnal oxygen saturation (SaO(2)) recordings. A total of 96 subjects suspected of suffering from sleep apnea-hypopnea syndrome (SAHS) took part in the study: 32 SAHS-negative and 64 SAHS-positive subjects. Their SaO(2) signals were separately processed by means of KerEnt. Our results show that a higher degree of irregularity is associated to SAHS-positive subjects. Statistical analysis revealed significant differences between the KerEnt values of SAHS-negative and SAHS-positive groups. The diagnostic utility of this parameter was studied by means of receiver operating characteristic (ROC) analysis. A classification accuracy of 81.25% (81.25% sensitivity and 81.25% specificity) was achieved. Repeated apneas during sleep increase irregularity in SaO(2) data. This effect can be measured by KerEnt in order to detect SAHS. This non-linear measure can provide useful information for the development of alternative diagnostic techniques in order to reduce the demand for conventional polysomnography (PSG).

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.

Pulse oximetry for the detection of obstructive sleep apnea syndrome: can the memory capacity of oxygen saturation influence their diagnostic accuracy?

Objective. To assess the diagnostic ability of WristOx 3100 using its three different recording settings in patients with suspected obstructive sleep apnea syndrome (OSAS). Methods. All participants (135) performed the oximetry (three oximeters WristOx 3100) and polysomnography (PSG) simultaneously in the sleep laboratory. Both recordings were interpreted blindly. Each oximeter was set to one of three different recording settings (memory capabilities 0.25, 0.5, and 1 Hz). The software (nVision 5.1) calculated the adjusted O₂ desaturation index-mean number of O₂ desaturation per hour of analyzed recording ≥2, 3, and 4% (ADI2, 3, and 4). The ADI2, 3, and 4 cutoff points that better discriminated between subjects with or without OSAS arose from the receiver-operator characteristics (ROCs) curve analysis. OSAS was defined as a respiratory disturbance index (RDI) ≥ 5. Results. 101 patients were included (77 men, mean age 52, median RDI 22.6, median BMI 27.4 kg/m²). The area under the ROCs curves (AUC-ROCs) of ADI2, 3, and 4 with different data storage rates were similar (AUC-ROCs with data storage rates of 0.25/0.5/1 Hz: ADI2: 0.958/0.948/0.965, ADI3: 0.961/0.95/0.966, and ADI4: 0.957/0.949/0.963, P NS). Conclusions. The ability of WristOx 3100 to detect patients with OSAS was not affected by the data storage rate of the oxygen saturation signal. Both memory capacity of 0.25, 0.5, or 1 Hz showed a similar performance for the diagnosis of OSAS.

Early diagnosis of sleep related breathing disorders

Obstructive sleep apnea (OSA) being the most frequent sleep related breathing disorder results in non-restorative sleep, an increased cardiovascular morbidity and mortality as well as an elevated number of accidents. In Germany at least two million people have to be expected. If obstructive sleep apnea is diagnosed early enough then sleep may regain its restorative function, daytime performance may be improved and accident risk as well as cardiovascular risk may be normalised. This review critically evaluates anamnestic parameters, questionnaires, clinical findings and unattended recordings during sleep regarding their diagnostic accurracy in recognising OSA. There are numerous tools with insufficient results or too few data disqualifying them for screening for OSA. Promising preliminary results are published concerning neural network analysis of a high number of clinical parameters and non-linear analysis of oximetry itself or in combination with heart rate. Nasal pressure recordings can be used for risk estimation even without expertise in sleep medicine. More data is needed. Unattended portable monitoring used by qualified physicians is the gold standard procedure when screening methods for OSA are compared. It has a very high sensitivity and specificity well documented by several meta-analyses.

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.

A classification algorithm based on spectral features from nocturnal oximetry and support vector machines to assist in the diagnosis of obstructive sleep apnea

The aim of this study is to develop and evaluate an algorithm to help in the diagnosis of the obstructive sleep apnea syndrome (OSAS). Arterial oxygen saturation (SaO(2)) signals from nocturnal pulse oximetry were used to identify OSAS patients. A total of 149 SaO(2) recordings from subjects suspected of OSAS were available. The initial population was divided into a training set (74 subjects) and a test set (75 subjects) to optimize and evaluate our algorithm. Support vector machines (SVM) with Gaussian kernel were used to classify spectral features from SaO(2) signals. Several configurations of SVM were assessed by varying the regularization (C) and the kernel width (sigma) parameters. Finally, the selected SVM classifier (C = 235 and sigma = 0.4) provided an accuracy of 88.00% (84.44% sensitivity and 93.33% specificity) and an AROC of 0.921. Our results suggest that the proposed algorithm could be useful for OSAS screening.

Spectral analysis of single-channel airflow and oxygen saturation recordings in obstructive sleep apnea detection

This study investigated the usefulness of the very low spectral content of single-channel airflow recordings to help in the diagnosis of the obstructive sleep apnea (OSA) syndrome. Additionally, we evaluated whether airflow frequency components in the 0.01 - 0.10 Hz band are linked with desaturations in blood oxygen saturation (SaO(2)) recordings due to apnea events. The relationship between changes in airflow and SaO(2) was analyzed by means of the magnitude squared coherence (MSC) function. Power spectral density (PSD) was used to obtain the power spectrum of single airflow and SaO(2) signals. Peak amplitude (PA) and relative power (P(R)) were used to parameterize the power spectrum in the very low frequency band. 148 subjects suspected of suffering from OSA were studied. Significant differences (p-value ≪ 0.01) between OSA positive and OSA negative subjects were obtained from PSD and MSC features. We found a power increase in the very low frequency band of single-channel airflow linked with the periodic desaturations of OSA. Diagnostic sensitivity, specificity and accuracy of 84.0%, 85.4% and 84.5%, respectively, were reached with the peak amplitude of the airflow PSD. Thus, spectral features from the very low frequency components of single-channel airflow recordings could provide useful information to help in OSA diagnosis.

Assessment of four statistical pattern recognition techniques to assist in obstructive sleep apnoea diagnosis from nocturnal oximetry

The aim of this study is to assess the utility of traditional statistical pattern recognition techniques to help in obstructive sleep apnoea (OSA) diagnosis. Classifiers based on quadratic (QDA) and linear (LDA) discriminant analysis, K-nearest neighbours (KNN) and logistic regression (LR) were evaluated. Spectral and nonlinear input features from oxygen saturation (SaO(2)) signals were applied. A total of 187 recordings from patients suspected of suffering from OSA were available. This initial dataset was divided into training set (74 subjects) and test set (113 subjects). Twelve classification algorithms were developed by applying QDA, LDA, KNN and LR with spectral features, nonlinear features and combination of both groups. The performance of each algorithm was measured on the test set by means of classification accuracy and receiver operating characteristic (ROC) analysis. QDA, LDA and LR showed better classification capability than KNN. The classifier based on LDA with spectral features provided the best diagnostic ability with an accuracy of 87.61% (91.05% sensitivity and 82.61% specificity) and an area under the ROC curve (AROC) of 0.925. The proposed statistical pattern recognition techniques could be applied as an OSA screening tool.

Single layer network classifiers to assist in the detection of obstructive sleep apnea syndrome from oximetry data

The aim of this study is to assess the utility of single layer network classifiers to help in the diagnosis of the obstructive sleep apnea syndrome (SAOS). Oxygen saturation (SaO(2)) recordings from a total of 157 subjects suspected of suffering from OSAS were used. These were divided into a training set and a test set with 74 and 83 subjects, respectively. Four classification schemes were developed by using generalized linear models (GLM). Two GLM classifiers were built with spectral (GLM-SP) and non-linear (GLM-NL) features from SaO(2) signals, respectively. In addition, both algorithms were combined in order to improve their classification capability. The performance of two different ensemble classifiers was analyzed. The highest diagnostic accuracy was reached by the GLM-SP classifier (88%). The ensemble built from the combination of GLM-SP and GLM-NL by means of an additional GLM structure provided the best sensitivity value (87.8%). Applying spectral and non-linear features from SaO(2) data simultaneously could be useful in OSAS diagnosis.

Association of chronic obstructive pulmonary disease and obstructive sleep apnea consequences

Obstructive sleep apnea syndrome (OSAS) and chronic obstructive pulmonary disease (COPD) are two diseases that often coexist within an individual. This coexistence is known as overlap syndrome and is the result of chance rather than a pathophysiological link. Although there are claims of a very high incidence of OSAS in COPD patients, recent studies report that it is similar to the general population. Overlap patients present sleep-disordered breathing associated to upper and lower airway obstruction and a reduction in respiratory drive. These patients present unique characteristics, which set them apart from either COPD or OSAS patients. COPD and OSAS are independent risk factors for cardiovascular events and their coexistence in overlap syndrome probably increases this risk. The mechanisms underlying cardiovascular risk are still unclear, but may involve systemic inflammation, endothelial dysfunction, and tonic elevation of sympathetic neural activity. The treatment of choice for overlap syndrome in stable patients is CPAP with supplemental oxygen for correction of upper airway obstructive episodes and hypoxemia during sleep.

Spectral analysis of electroencephalogram and oximetric signals in obstructive sleep apnea diagnosis

This study assessed the hypothesis that blood oxygen saturation (SaO(2)) and electroencephalogram (EEG) recordings could provide complementary information in the diagnosis of the obstructive sleep apnea (OSA) syndrome. We studied 148 patients suspected of suffering from OSA. Classical spectral parameters based on the relative power in specified frequency bands (A(f-band)) or peak amplitudes (PA) were used to characterize the frequency content of SaO(2) and EEG recordings. Additionally, the median frequency (MF) and the spectral entropy (SE) were applied to obtain further spectral information. We applied a forward stepwise logistic regression (LR) procedure with crossvalidation leave-one-out to obtain the optimum spectral feature set. Two features from the oximetric spectral analysis (PA and MFsat) and three features from the EEG spectral analysis (A(delta), A(alpha) and SEeeg) were automatically selected. 91.0% sensitivity, 83.3% specificity and 88.5% accuracy were obtained. These results suggest that MF and SE could provide additional information to classical frequency characteristics commonly used in OSA diagnosis. Additionally, nocturnal SaO(2) and EEG recordings during the whole night could provide complementary information to help in the detection of OSA syndrome.

Validation of the WristOx 3100 oximeter for the diagnosis of sleep apnea/hypopnea syndrome

OBJECTIVE

To evaluate the diagnostic accuracy of the Nonin WristOx 3100 and its software (nVision 5.0) in patients with suspicion of sleep apnea/hypopnea syndrome (SAHS).

METHODS

All participants (168) had the oximetry and polysomnography simultaneously. The two recordings were interpreted blindly. The software calculated: adjusted O(2) desaturation index [ADI]-mean number of O(2) desaturation per hour of total recording analyzed time of > or = 2%, 3%, 4%, 5%, and 6% (ADI2, 3, 4, 5, and 6) and AT90-accumulated time at SO(2) < 90%. The ADI2, 3, 4, 5, and 6 and the AT90 cutoff points that better discriminated between subjects with or without SAHS arose from the receiver operating characteristic curve analysis. The sensitivity (S), specificity (E), and positive and negative likelihood ratio (LR+, LR-) for the different thresholds for ADI were calculated.

RESULTS

One hundred and fifty-four patients were included (119 men, mean age 51, median apnea/hypopnea index [AHI] 14, median body mass index [BMI] 28.3 kg/m(2)). The best cutoff points of ADI were: SAHS = AHI > or = 5: ADI2 > 19.3 (S 89%, E 94%, LR+ 15.5 LR- 0.11); SAHS =AHI > or = 10: ADI3 > 10.5 (S 88%, E 94%, LR+ 15 LR- 0.12); SAHS = AHI > or = 15: ADI3 > 13.4 (S 88%, E 90%, LR+ 8.9, LR- 0.14). AT90 had the lowest diagnosis accuracy. An ADI2 < or = 12.2 excluded SAHS (AHI > or = 5 and 10; S 100%, LR- 0) and ADI3 > 4.3 (AHI > or = 5 and 10) or 32 (AHI > or = 15) confirmed SAHS (E 100%).

CONCLUSIONS

A negative oximetry defined as ADI2 < or = 12.2 excluded SAHS defined as AHI > or = 5 or 10 with a sensitivity and negative likelihood ratio of 100% and 0%, respectively. Furthermore, a positive oximetry defined as an ADI3 > 32 (SAHS = AHI > or = 15) had a specificity of 100% to confirm the pathology.

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

OBJECTIVES

To comprehensively evaluate the ability and reliability of the representative previously proposed oxyhemoglobin indexes derived automatically for predicting the severity of obstructive sleep apnea hypopnea syndrome (OSAHS).

METHODS

Patients with a diagnosis of OSAHS by standard polysomnography were recruited from China Medical University Hospital Centre. There were 257 patients in the learning set and 279 patients in the validation set. The presence of OSAHS was defined as apnea-hypopnea index (AHI) > 5/h. Three kinds of oxyhemoglobin indexes, including the oxyhemoglobin desaturation index (ODI), time-domain index, and frequency-domain index, were used. Degrees of severity were AHI > 15/h and AHI > 30/h, representing moderate and severe OSAHS. A total of 28 oxyhemoglobin indexes were tested in our study.

RESULTS

Among the three kinds of indexes, ODI had a better diagnostic performance than the time-domain and frequency-domain indexes, with the results coincident in the validation set and learning set. For predicting the severity of OSAHS with AHI > 15/h or > 30/h, the ODI clinically had the higher correlation with AHI than time-domain and frequency-domain indexes, with sensitivity/specificity achieving 84.0%/84.3% in AHI > 15/h and 87.8%/96.6% in AHI > 30/h, respectively.

CONCLUSIONS

Based on the smaller SEE of the AHI, the ODI had a significantly smaller SEE than the time-domain and frequency-domain indexes. The ODI index provided a high level of diagnostic sensitivity and specificity at different degrees of OSAHS severity.

Pulse rate and pulse rate variability decrease after adenotonsillectomy for obstructive sleep apnea

BACKGROUND

Data suggest that obstructive sleep apnea syndrome (OSA) results in sympathetic stimulation, brady/tachycardia and cardiac stress. Heart rate variability, but not baseline heart rate, is known to be elevated in pediatric OSA. Our patients with moderate to severe OSA (McGill Oximetry Scores of 3 or 4) have been re-evaluated with pulse oximetry after adenotonsillectomy (T&A). We hypothesized that pulse rate (PR) and pulse rate variability (PRV) would decrease after treatment of OSA with T&A.

METHODS

This retrospective before-after study comprised pre- and post-operative oximetries and parental questionnaires of children 1-18 years old with moderate to severe OSA from September 2004 to August 2005, inclusive. We excluded patients with significant comorbidities.

RESULTS

In 25 subjects, age at surgery was 4.3 +/- 3.6 years (mean +/- SD). OSA symptoms decreased or resolved, saturation metrics improved, and parental concern about breathing during sleep decreased following T&A. PR decreased in 21 of 25 patients after T&A (mean PR from 99.7 +/- 11.2 to 90.1 +/- 10.7 bpm, P < 0.001; maximum PR from 150.6 +/- 14.5 to 137.4 +/- 15.6 bpm, P < 0.001). PRV, as measured by the standard deviation of the PR, decreased in 23 of 25 patients after T&A (from 10.3 +/- 2.1 to 8.2 +/- 1.6 bpm, [P < 0.001]). Pulse accelerations greater than 6, 7, 8 bpm also decreased post-operatively.

CONCLUSIONS

Nocturnal pulse oximetry complements clinical history to document improvement and/or resolution of moderate to severe OSA in children. Resolution of tachycardia and diminished PRV after T&A illustrate the stress that recurrent airway obstruction during sleep places on the cardiovascular system. Further work will be required to determine if PR and PRV as measured by pulse oximetry would be useful in the diagnosis and follow-up of OSA in children.

Applying neural network classifiers in the diagnosis of the obstructive sleep apnea syndrome from nocturnal pulse oximetric recordings

The aim of this study was to assess the ability of neural networks as an assistant tool for the diagnosis of the obstructive sleep apnea syndrome (OSAS). A total of 187 subjects suspected of suffering from OSAS (111 with a positive diagnosis of OSAS and 76 with a negative diagnosis of OSAS) took part in the study. The initial population was divided into training, validation and test sets for deriving and testing our neural classifiers. Our method was based on spectral and non-linear features extracted from overnight arterial oxygen saturation (SaO_(2)) recordings. A seven-element input vector was used for patient classification. We selected four spectral features from the estimated power spectral density (PSD) of SaO_(2). In addition, three input features were computed from non-linear analysis of SaO_(2). Two neural classifiers were assessed: the multilayer perceptron (MLP) network and the radial basis function (RBF) network. The RBF classifier provided the best diagnostic performance with an accuracy of 86.3% (89.9% sensitivity and 81.1% specificity).

Cross approximate entropy analysis of nocturnal oximetry signals in the diagnosis of the obstructive sleep apnea syndrome

This study is focused on the analysis of blood oxygen saturation (SaO(2)) and heart rate (HR) from nocturnal oximetry using cross approximate entropy (Cross-ApEn). We assessed its usefulness in screening obstructive sleep apnea (OSA) syndrome. We applied Cross-ApEn(m,r,N) to quantify the asynchrony between paired SaO(2) and HR records of 74 patients (44 with a positive OSA diagnosis and 30 with a negative OSA diagnosis). Cross-ApEn values were significantly lower in the OSA positive group compared with those obtained in the OSA negative group. A receiver operating characteristic (ROC) analysis showed that the best results, in terms of diagnostic accuracy, were achieved with m = 2 and r = 0.6. With these input parameters, the optimum decision threshold was found at 1.7, where we achieved 95.5% sensitivity, 73.3% specificity and 86.5% accuracy. Further analyses should be carried out with new and larger data sets to test the usefulness of our methodology prospectively.

A preliminary study on the influence of obstructive sleep apnea upon cumulative parasympathetic system activity

OBJECTIVE

Although the autonomic nervous system plays a key role in mediating cardiovascular changes during obstructive sleep apnea (OSA), parasympathetic nervous system (PNS) activity during sleep apnea has not yet been sufficiently investigated. This study is to discuss the relationship between PNS activity and OSA.

METHODS

Polysomnography recording was carried out in 76 patients (71 male and 5 female) with OSA. Cumulative PNS activity during sleep for each patient was derived from time series data of electrocardiogram (ECG) and analyzed by coarse graining spectral analysis of heart rate variability. The correlation between cumulative PNS activity and apnea-hypopnea index (AHI) was then discussed.

RESULTS

Cumulative PNS activity and PNS peaks during sleep were lowly but significantly correlated with OSA severity (r=-0.344, p<0.005; and r=-0.266, p<0.05 respectively), and a linear regression equation could be established. Furthermore, significant correlation was also observed in the adult groups and in the moderate and severe groups, but not in the juvenile and the elderly and mild groups.

CONCLUSION

These findings indicated that PNS function was obviously influenced by OSA during sleep. Cumulative PNS activity level might also serve as a useful parameter for the evaluation of OSA.