Novel mathematical processing method of nocturnal oximetry for screening patients with suspected sleep apnoea syndrome

A New Screening Tool (BAMSA) for Sleep Apnea in Male Professional Truck Drivers

Obstructive sleep apnea (OSA) is common in professional truck drivers. It is important that OSA is recognized since undiagnosed and/or untreated sleep apnea is a risk factor for sleepiness-related traffic accidents. In this study, we developed a new simple tool to screen for obstructive sleep apnea (OSA) in this population. Altogether, 2066 professional truck drivers received a structured questionnaire. A total of 175 drivers had a clinical examination and were invited to participate in sleep laboratory studies, including cardiorespiratory polygraphy. We studied associations of different risk factors with the presence of sleep apnea. We established a new simple screening tool for obstructive sleep apnea (OSA) that was compared to other existing screening tools. A total of 1095 drivers completed the questionnaire. Successful cardiorespiratory polygraphy was obtained for 172 drivers. Full data were available for 160 male drivers included in the analyses. The following five risk factors for sleep apnea formed the BAMSA score (0 to 5): BMI > 30 kgm-2, age > 50 years, male gender, snoring at least one night per week, and the presence of apnea at least sometimes. BAMSA showed a sensitivity of 85.7% and a specificity of 78.8% in detecting AHI ≥ 15 when using a cut-off point of 4, and the ROC area was 0.823. BAMSA is a sensitive and easy-to-use tool in predicting obstructive sleep apnea in male professional drivers.

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.

Classification of sleep apnea using EMD-based features and PSO-trained neural networks

In this study, we propose a method for detecting obstructive sleep apnea (OSA) based on the features extracted from empirical mode decomposition (EMD) and the neural networks trained by particle swarm optimization (PSO) in the classification phase. After extracting the features from the intrinsic mode functions (IMF) of each heart rate variability (HRV) signal of each segment, these features were applied to the input of popular classifiers such as multi-layer perceptron neural networks (MLPNN), Naïve Bayes, linear discriminant analysis (LDA), k-nearest neighborhood (KNN), and support vector machines (SVM) were applied. The results show that the MLPNN learned with back propagation (BP) algorithm has a diagnostic accuracy of less than 90%, and this may be due to being derivative based property of the BP algorithm, which causes trapping in the local minima. For Improving MLPNN's performance, we used the PSO algorithm instead of the BP method in training part. Therefore, the MLPNN's accuracy improved from 89.36 to 97.66% after the application of the PSO algorithm. The proposed method has also reached to 97.78 and 97.96% in sensitivity and specificity, respectively. So, it can be concluded that the proposed method achieves better or comparable results when compared with the previous works in this field.

Nocturnal oximetry in the diagnosis of obstructive sleep apnea syndrome in potentially hypoxic patients due to neuromuscular diseases

INTRODUCTION

Polysomnography is the recommended method for the diagnosis of obstructive sleep apnea (OSA); however, it is expensive, uncomfortable, and inaccessible. Alternative diagnostic methods are necessary, and Nocturnal Oximetry (NO) has proven to be reliable. Nevertheless, there have been doubts about its accuracy in patients with a history of hypoxia. Hence, the objective of this study was to evaluate the performance of NO in patients with neuromuscular diseases (NMD).

METHOD

This was a cross-sectional study in patients with NMD suspected of having OSA. We performed a statistical analysis using Spearman's correlation coefficients (SCCs). We used the value of the area under the ROC curve (AUCROC), just as we calculated the sensitivities (Sens) and specificities (Spec) for the chosen variables.

RESULTS

The sample comprised 41 patients; 51.2% with muscular dystrophies and 48.8% with motor neuron diseases, with a predominance of men (63.4%). Median age was 42 (19.7-55) years, body mass index (BMI) was 27.9 (23.8-32) kg/m2, forced vital capacity was 67% (54%-76.5%), and maximum inspiratory pressure was-60 cmH2O (-87.5 to -50). The prevalence of OSA was 75.7%. We analyzed and selected the best four oximetric variables with the following performance in identifying the apnea/hypopnea index >5/h, ODI3/2, cutoff>5/h, AUCROC 0.919, Sens 82.3%, Spec 91.7%; ODI3/5, cutoff>11.2/h, AUCROC 0.904, Sens 82.3%, Spec 87.5%; ODI4/5, cutoff>6.02, AUCROC 0.839, Sens 70.6%, Spec 91.6%, and ODI5/5, cutoff>0.87/h, AUCROC 0.870, Sens 94.1%, and Spec 70.8%.

CONCLUSION

NO can be used as a diagnostic tool for OSA, even in patients with neuromuscular diseases and potentially hypoxic diseases.

Multispectral Video Fusion for Non-Contact Monitoring of Respiratory Rate and Apnea

Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. However, current technologies are not sufficiently robust to be used in clinical applications. For example, they fail to estimate an accurate respiratory rate (RR) during apnea. We present a novel algorithm based on multispectral data fusion that aims at estimating RR also during apnea. The algorithm independently addresses the RR estimation and apnea detection tasks. Respiratory information is extracted from multiple sources and fed into an RR estimator and an apnea detector whose results are fused into a final respiratory activity estimation. We evaluated the system retrospectively using data from 30 healthy adults who performed diverse controlled breathing tasks while lying supine in a dark room and reproduced central and obstructive apneic events. Combining multiple respiratory information from multispectral cameras improved the root mean square error (RMSE) accuracy of the RR estimation from up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also improved. Furthermore, the independent consideration of apnea detection led to a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications.

Oximetry use in obstructive sleep apnea

INTRODUCTION

Overnight oximetry has been proposed as an accessible, simple, and reliable technique for obstructive sleep apnea syndrome (OSAS) diagnosis. From visual inspection to advanced signal processing, several studies have demonstrated the usefulness of oximetry as a screening tool. However, there is still controversy regarding the general application of oximetry as a single screening methodology for OSAS. Areas covered: Currently, high-resolution portable devices combined with pattern recognition-based applications are able to achieve high performance in the detection of this disease. In this review, recent studies involving automated analysis of oximetry by means of advanced signal processing and machine learning algorithms are analyzed. Advantages and limitations are highlighted and novel research lines aimed at improving the screening ability of oximetry are proposed. Expert commentary: Oximetry is a cost-effective tool for OSAS screening in patients showing high pretest probability for the disease. Nevertheless, exhaustive analyses are still needed to further assess unattended oximetry monitoring as a single diagnostic test for sleep apnea, particularly in the pediatric population and in populations with significant comorbidities. In the following years, communication technologies and big data analyses will overcome current limitations of simplified sleep testing approaches, changing the detection and management of OSAS.

Diagnostic accuracy of level IV portable sleep monitors versus polysomnography for obstructive sleep apnea: a systematic review and meta-analysis

PURPOSE

Obstructive sleep apnea (OSA) is the most common sleep-related breathing disorder. In-laboratory, overnight type I polysomnography (PSG) is the current "gold standard" for diagnosing OSA. Home sleep apnea testing (HSAT) using portable monitors (PMs) is an alternative testing method offering better comfort and lower costs. We aimed to systematically review the evidence on diagnostic ability of type IV PMs compared to PSG in diagnosing OSA.

METHODS

Participants: patients ≥16 years old with symptoms suggestive of OSA;intervention: type IV PMs (devices with < 2 respiratory channels); comparator: in-laboratory PSG; outcomes: diagnostic accuracy measures;studies: cross-sectional, prospective observational/experimental/quasi-experimental studies; information sources: MEDLINE and Cochrane Library from January 1, 2010 to May 10, 2016. All stages of review were conducted independently by two investigators.

RESULTS

We screened 6054 abstracts and 117 full-text articles to select 24 full-text articles for final review. These 24 studies enrolled a total of 2068 patients with suspected OSA and evaluated 10 different PMs with one to six channels. Only seven (29%) studies tested PMs in the home setting. The mean difference (bias) between PSG-measured and PM-measured apnea-hypopnea index (AHI) ranged from - 14.8 to 10.6 events/h. At AHI ≥ 5 events/h, the sensitivity of type IV PMs ranged from 67.5-100% and specificity ranged from 25 to 100%.

CONCLUSION

While current evidence is not very strong for the stand-alone use of level IV PMs in clinical practice, they can potentially widen access to diagnosis and treatment of OSA. Policy recommendations regarding HSAT use should also consider the health and broader social implications of false positive and false negative diagnoses.

Regularity analysis of nocturnal oximetry recordings to assist in the diagnosis of sleep apnoea syndrome

The relationship between sleep apnoea-hypopnoea syndrome (SAHS) severity and the regularity of nocturnal oxygen saturation (SaO2) recordings was analysed. Three different methods were proposed to quantify regularity: approximate entropy (AEn), sample entropy (SEn) and kernel entropy (KEn). A total of 240 subjects suspected of suffering from SAHS took part in the study. They were randomly divided into a training set (96 subjects) and a test set (144 subjects) for the adjustment and assessment of the proposed methods, respectively. According to the measurements provided by AEn, SEn and KEn, higher irregularity of oximetry signals is associated with SAHS-positive patients. Receiver operating characteristic (ROC) and Pearson correlation analyses showed that KEn was the most reliable predictor of SAHS. It provided an area under the ROC curve of 0.91 in two-class classification of subjects as SAHS-negative or SAHS-positive. Moreover, KEn measurements from oximetry data exhibited a linear dependence on the apnoea-hypopnoea index, as shown by a correlation coefficient of 0.87. Therefore, these measurements could be used for the development of simplified diagnostic techniques in order to reduce the demand for polysomnographies. Furthermore, KEn represents a convincing alternative to AEn and SEn for the diagnostic analysis of noisy biomedical signals.

Screening of obstructive sleep apnea with empirical mode decomposition of pulse oximetry

Detection of desaturations on the pulse oximetry signal is of great importance for the diagnosis of sleep apneas. Using the counting of desaturations, an index can be built to help in the diagnosis of severe cases of obstructive sleep apnea-hypopnea syndrome. It is important to have automatic detection methods that allows the screening for this syndrome, reducing the need of the expensive polysomnography based studies. In this paper a novel recognition method based on the empirical mode decomposition of the pulse oximetry signal is proposed. The desaturations produce a very specific wave pattern that is extracted in the modes of the decomposition. Using this information, a detector based on properly selected thresholds and a set of simple rules is built. The oxygen desaturation index constructed from these detections produces a detector for obstructive sleep apnea-hypopnea syndrome with high sensitivity (0.838) and specificity (0.855) and yields better results than standard desaturation detection approaches.

Respiratory perioperative management of patients with obstructive sleep apnea

Obstructive sleep apnea (OSA) has become a major public health problem in the United State and Europe. However, perioperative strategies regarding diagnostic options and management of untreated OSA remain inadequate. Preoperative screening and identification of patients with undiagnosed OSA may lead to early perioperative interventions that may alter cardiopulmonary events associated with surgery and anesthesia.(1) Hence, clinicians need to become familiar with the preoperative screening and diagnosis of OSA. Perioperative management of a patient with OSA should be modified and may include regional anesthesia and alternative analgesic techniques such as nonsteroidal anti-inflammatory drugs that may reduce the need for systemic opioids. Additionally, supplemental oxygen and continuous pulse oximetry monitoring should be utilized to maintain baseline oxygen saturation. Postoperatively patients should remain in a semi-upright position and positive pressure therapy should be used in patients with high-risk OSA.