PSG-Audio, a scored polysomnography dataset with simultaneous audio recordings for sleep apnea studies.

The sleep apnea syndrome is a chronic condition that affects the quality of life and increases the risk of severe health conditions such as cardiovascular diseases. However, the prevalence of the syndrome in the general population is considered to be heavily underestimated due to the restricted number of people seeking diagnosis, with the leading cause for this being the inconvenience of the current reference standard for apnea diagnosis: Polysomnography. To enhance patients' awareness of the syndrome, a great endeavour is conducted in the literature. Various home-based apnea detection systems are being developed, profiting from information in a restricted set of polysomnography signals. In particular, breathing sound has been proven highly effective in detecting apneic events during sleep. The development of accurate systems requires multitudinous datasets of audio recordings and polysomnograms. In this work, we provide the first open access dataset, comprising 212 polysomnograms along with synchronized high-quality tracheal and ambient microphone recordings. We envision this dataset to be widely used for the development of home-based apnea detection techniques and frameworks.

Classification of sleep apnea based on EEG sub-band signal characteristics.

Sleep apnea syndrome (SAS) is a disorder in which respiratory airflow frequently stops during sleep. Alterations in electroencephalogram (EEG) signal are one of the physiological changes that occur during apnea, and can be used to diagnose and monitor sleep apnea events. Herein, we proposed a method to automatically distinguish sleep apnea events using characteristics of EEG signals in order to categorize obstructive sleep apnea (OSA) events, central sleep apnea (CSA) events and normal breathing events. Through the use of an Infinite Impulse Response Butterworth Band pass filter, we divided the EEG signals of C3-A2 and C4-A1 into five sub-bands. Next, we extracted sample entropy and variance of each sub-band. The neighbor composition analysis (NCA) method was utilized for feature selection, and the results are used as input coefficients for classification using random forest, K-nearest neighbor, and support vector machine classifiers. After a 10-fold cross-validation, we found that the average accuracy rate was 88.99%. Specifically, the accuracy of each category, including OSA, CSA and normal breathing were 80.43%, 84.85%, and 95.24%, respectively. The proposed method has great potential in the automatic classification of patients' respiratory events during clinical examinations, and provides a novel idea for the development of an automatic classification system for sleep apnea and normal events without the need for expert intervention.

Variability and Misclassification of Sleep Apnea Severity Based on Multi-Night Testing.

BACKGROUND: Portable monitoring is a convenient means for diagnosing sleep apnea. However, data on whether one night of monitoring is sufficiently precise for the diagnosis of sleep apnea are limited. RESEARCH QUESTION: The current study sought to determine the variability and misclassification in disease severity over three consecutive nights in a large sample of patients referred for sleep apnea. METHODS: A sample of 10,340 adults referred for sleep apnea testing was assessed. A self-applied type III monitor was used for three consecutive nights. The apnea-hypopnea index (AHI) was determined for each night, and a reference AHI was computed by using data from all 3 nights. Pairwise correlations and the proportion misclassified regarding disease severity were computed for each of the three AHI values against the reference AHI. RESULTS: Strong correlations were observed between the AHI from each of the 3 nights (r = 0.87-0.89). However, substantial within-patient variability in the AHI and significant misclassification in sleep apnea severity were observed based on any 1 night of monitoring. Approximately 93% of the patients with a normal study on the first night and 87% of those with severe sleep apnea on the first night were correctly classified compared with the reference derived from all three nights. However, approximately 20% of the patients with mild and moderate sleep apnea on the first night were misdiagnosed either as not having sleep apnea or as having mild disease, respectively. CONCLUSIONS: In patients with mild to moderate sleep apnea, one night of portable testing can lead to misclassification of disease severity given the substantial night-to-night variability in the AHI.

Classification of Sleep Apnea Severity by Electrocardiogram Monitoring Using a Novel Wearable Device.

Sleep apnea (SA) is a prevalent disorder diagnosed by polysomnography (PSG) based on the number of apnea-hypopnea events per hour of sleep (apnea-hypopnea index, AHI). PSG is expensive and technically complex; therefore, its use is rather limited to the initial diagnostic phase and simpler devices are required for long-term follow-up. The validity of single-parameter wearable devices for the assessment of sleep apnea severity is still debated. In this context, a wearable electrocardiogram (ECG) acquisition system (ECG belt) was developed and its suitability for the classification of sleep apnea severity was investigated using heart rate variability analysis with or without data pre-filtering. Several classification algorithms were compared and support vector machine was preferred due to its simplicity and overall performance. Whole-night ECG signals from 241 patients with a suspicion of sleep apnea were recorded using both the ECG belt and patched ECG during PSG recordings. 65% of patients had an obstructive sleep apnea and the median AHI was 21 [IQR: 7-40] h - 1 . The classification accuracy obtained from the ECG belt (accuracy: 72%, sensitivity: 70%, specificity: 74%) was comparable to the patched ECG (accuracy: 74%, sensitivity: 88%, specificity: 61%). The highest classification accuracy was obtained for the discrimination between individuals with no or mild SA vs. moderate to severe SA. In conclusion, the ECG belt provided signals comparable to patched ECG and could be used for the assessment of sleep apnea severity, especially during follow-up.

Effect of mean apnea-hypopnea duration in patients with obstructive sleep apnea on clinical and polysomnography parameter.

OBJECTIVE: Apnea-hypopnea index is the number of apnea-hypopnea events observed during polysomnography within an hour. Mean apnea-hypopnea duration is the mean duration of all apneas and hypopneas. In this study, we aimed to investigate the association of mean apnea-hypopnea duration in patients with obstructive sleep apnea with clinical and polysomnographic parameters. METHODS: In our hospital, a total of 764 patients were diagnosed with OSA by polysomnography in 2017. Age, body mass index, and the current diseases were recorded. Sleep structures obtained from polysomnography readings, blood oxygen levels, apnea-hypopnea index, and mean average duration were recorded. Patients with mean average duration of 20 s or more were assigned to the long average duration group and those with less than 20 s were assigned to the short average duration group. Groups were compared in terms of clinical and polysomnographic parameters. RESULTS: Snoring, witnessed apnea, morning tiredness, and hypertension were significantly higher in the long average duration group. There was statistically significantly more male patients and higher neck circumference in the MAD group. Total wake duration, percentage of sleep, stage 3, stage 1, and mean oxygen saturation percentage of the long average duration group were significantly reduced. CONCLUSION: In present study, the patients with obstructive sleep apnea with long average duration were found to have more negative effects of sleep apnea than the patients with short average duration. We think that the use of mean apnea-hypopnea duration as an indicator with apnea-hypopnea index will be beneficial for the follow-up and treatment of the disease.

Multi-Class Classification of Sleep Apnea/Hypopnea Events Based on Long Short-Term Memory Using a Photoplethysmography Signal.

In this study, we proposed a new method for multi-class classification of sleep apnea/hypopnea events based on a long short-term memory (LSTM) using photoplethysmography (PPG) signals. The three-layer LSTM model was used with batch-normalization and dropout to classify the multi-class events including normal, apnea, and hypopnea. The PPG signals, which were measured by the nocturnal polysomnography with 7 h from 82 patients suffered from sleep apnea, were used to model training and evaluation. The performance of the proposed method was evaluated on the training set from 63 patients and test set from 13 patients. The results of the LSTM model showed the following high performances: the positive predictive value of 94.16% for normal, 81.38% for apnea, and 97.92% for hypopnea; sensitivity of 86.03% for normal, 91.24% for apnea, and 99.38% for hypopnea events. The proposed method had especially higher performance of hypopnea classification which had been a drawback of previous studies. Furthermore, it can be applied to a system that can classify sleep apnea/hypopnea and normal events automatically without expert's intervention at home.

Anthropometric Measures and Prediction of Maternal Sleep-Disordered Breathing.

STUDY OBJECTIVES: Pregnant women are at risk for sleep-disordered breathing (SDB); however, screening methods in this dynamic population are not well studied. The aim of this study was to examine whether anthropometric measures can accurately predict SDB in pregnant women. METHODS: Pregnant women with snoring and overweight/obesity were recruited in the first trimester. Anthropometric measures were performed according to the International Standards for Anthropometric Assessment, including a seated neutral and extended neck Mallampati class. Home sleep apnea monitoring was performed using a level III device after completion of anthropometric assessment. SDB was defined as an apnea-hypopnea index ≥ 5 events/h of sleep. Pearson and Spearman tests examined correlations between various measures. Generalized linear models, sensitivity, specificity, and area under the curve as well as odds ratios were performed to test the model. RESULTS: A total of 129 participants were recruited, and 23 had SDB. Average gestational age was 10.6 ± 1.9 weeks. Due to concerns over multicollinearity, the final model included extended Mallampati class and upright neck circumference. Neck circumference was significantly higher in participants with Mallampati classes 2/3 and grade 4 compared to participants with Mallampati class 1 (P = .0005). Increasing neck circumference was associated with higher odds of SDB (P = .0022). In Mallampati class 1, odds ratio for SDB was 2.89 (1.19, 7.03) per unit increase in neck circumference. CONCLUSIONS: Modeling neck circumference while allowing for differences by Mallampati class showed a nearly threefold increase in the risk of SDB with increasing neck circumference in women with Mallampati class 1. Other potential sites of airway obstruction need to be investigated in future research.

A new simple efficient classification technique for severity of sleep apnea with mathematical model and interpretation.

BACKGROUND: Obstructive Sleep Apnea (OSA) is the cessation of breathing during sleep due to the collapse of the upper airway. Polysomnographic recording is a conventional method for detection of OSA. Although it provides reliable results, it is expensive and cumbersome. Thus, an advanced non-invasive heart rate variability (HRV) signal processing technique other than the standard spectral analysis, which also has efficiency limitations, is needed for identification of OSA and classification of apnea levels. OBJECTIVE: The main purpose of this work was to predict the severity of sleep apnea using an efficient method based on the combination of time-domain and frequency-domain analysis of the HRV to classify sleep apnea into three different levels (mild, moderate, and severe) according to its severity and to distinguish them from normal subjects. METHODS: The statistical signal characterization of the FFT-based spectrum of the RRI data is used in this work in order to rank patients to full polysomnography. Data of 20 normal subjects, 20 patients with mild apnea, 20 patients with moderate apnea and 20 patients with severe apnea were used in this study. RESULTS: Accuracy result of 100% was obtained between severe and normal subjects, 100% between mild and normal subjects, and 100% between apnea (mild, moderate, severe) and normal subjects. This perfect accuracy is obtained using the parameter mean (mt). The physiological interpretation of the SSC parameters has been derived using a mathematical model system. CONCLUSIONS: An efficient method for screening of sleep apnea with 100% efficiency in classification of sleep apnea levels, is investigated in this work.

Pulse transit time in pregnancy: a new way to diagnose and classify sleep disordered breathing?

STUDY OBJECTIVES: There are significant discrepancies between the prevalence of snoring and that of objectively defined sleep disordered breathing among pregnant women, suggesting subtle airflow limitations that may not be captured by conventional scoring. This study examined the performance of pulse transit time, an indirect measure of arterial stiffness and sympathetic activation, in pregnancy. METHODS: Pregnant women with obesity and snoring and a group of controls without symptoms of sleep disordered breathing were recruited in the first trimester. Women underwent a level III in-laboratory sleep monitoring study including an electrocardiogram and pulse oximetry, and pulse transit time was measured. Sleep disordered breathing was defined as an apnea-hypopnea index at least five events per hour of sleep. Statistical analysis was performed using Spearman correlation, Fisher's exact t-test, and univariate analysis. RESULTS: Of the 222 women, 38 met criteria for sleep disordered breathing. Pulse transit time drops were very prevalent (95% of participants with snoring had > 5 drops per hour). Median apnea-hypopnea index was 0.7 (interquartile range [IQR]: 2.6) events per hour whereas median pulse transit time drop index was 20.70 (IQR: 35.90) events per hour. Pulse transit time index was significantly higher in snorers with apnea-hypopnea index less than five events per hours and participants with apnea-hypopnea index greater than five events per hour compared to controls. Examination of random epochs with pulse transit time drops showed that 95% of pulse transit time drops were associated with airflow limitation. CONCLUSIONS: Pulse transit time ascertains frequent events of sympathetic activation in at-risk women with and without sleep disordered breathing beyond conventional apneas and hypopneas. Pulse transit time may be an important addition to the identification of clinically significant sleep disordered breathing in pregnant women, and may identify more sleep disordered breathing than apnea-hypopnea index.

Effect of Three Hypopnea Scoring Criteria on OSA Prevalence and Associated Comorbidities in the General Population.

STUDY OBJECTIVES: Apnea-hypopnea index (AHI) is the main polysomnographic measure to diagnose obstructive sleep apnea (OSA). We aimed to evaluate the effect of three standard hypopnea definitions on the prevalence of OSA and its association with cardiometabolic outcomes in the general population. METHODS: We analyzed data from the HypnoLaus study (Lausanne, Switzerland), in which 2,162 participants (51% women, 57 ± 19 years) underwent in-home full polysomnography. AHI was calculated using three hypopnea definitions: AASM1999 (≥ 50% decrease in airflow or lower airflow reduction associated with oxygen desaturation ≥ 3% or an arousal), AASM2007 (≥ 30% airflow reduction associated with ≥ 4% oxygen desaturation), and AASM2012(≥ 30% airflow reduction associated with ≥ 3% oxygen desaturation or an arousal). Participants underwent clinical assessment for hypertension, diabetes, and metabolic syndrome. RESULTS: Median AHI of AASM1999, AASM2007 and AASM2012 criteria were 10.9, 4.4, and 10.1 events/h, respectively. OSA prevalence defined as AHI ≥ 5, ≥ 15, and ≥ 30 events/h was 74.5%, 39.3%, and 16.3% using AASM1999; 46.9%, 18.8%, and 6.8% using AASM2007; and 72.2%, 36.6%, and 14.9% using AASM2012. Different AHI thresholds derived from AASM1999, AASM2007, and AASM2012 criteria, respectively, were associated with hypertension (11.5, 4.8, 10.7 events/h), diabetes (15.7, 7.1, 14.4 events/h), and metabolic syndrome (12.8, 5.5, 11.8 events/h). CONCLUSIONS: Hypopnea definition has a major effect on AHI and on OSA prevalence in the general population and, hence, important implications for public health policies. There is a twofold difference in the threshold above which an association with diabetes, hypertension, and metabolic syndrome is observed using AASM2007 compared to AASM1999 or AASM2012 criteria.

Apnoea and hypopnoea scoring for people with spinal cord injury: new thresholds for sleep disordered breathing diagnosis and severity classification.

STUDY DESIGN: Descriptive study. OBJECTIVES: To determine the effect of respiratory event rule-set changes on the apnoea hypopnoea index, and diagnostic and severity thresholds in people with acute and chronic spinal cord injury. SETTING: Eleven acute spinal cord injury inpatient hospitals across Australia, New Zealand, Canada and England; community dwelling chronic spinal cord injury patients in their own homes. METHODS: Polysomnography of people with acute (n = 24) and chronic (n = 78) tetraplegia were reanalysed from 1999 American Academy of Sleep Medicine (AASM) respiratory scoring, to 2007 AASM 'alternative' and 2012 AASM respectively. Equivalent cut points for published 1999 AASM sleep disordered breathing severity ranges were calculated using receiver operator curves, and results presented alongside analyses from the able-bodied. RESULTS: In people with tetraplegia, shift from 1999 AASM to 2007 AASM 'alternative' resulted in a 22% lower apnoea hypopnoea index, and to 2012 AASM a 17% lower index. In people with tetraplegia, equivalent cut-points for 1999 AASM severities of 5,15 and 30 were calculated at 2.4, 8.1 and 16.3 for 2007 AASM 'alternative' and 3.2, 10.0 and 21.2 for 2012 AASM. CONCLUSION: Interpreting research, prevalence and clinical polysomnography results conducted over different periods requires knowledge of the relationship between different rule-sets, and appropriate thresholds for diagnosis of disease. SPONSORSHIP: This project was proudly supported by the Traffic Accident Commission (Program grant) and the National Health and Medical Research Council (PhD stipend 616605).

An Intelligent Sleep Apnea Classification System Based on EEG Signals.

Sleep Apnea is a sleep disorder which causes stop in breathing for a short duration of time that happens to human beings and animals during sleep. Electroencephalogram (EEG) plays a vital role in detecting the sleep apnea by sensing and recording the brain's activities. The EEG signal dataset is subjected to filtering by using Infinite Impulse Response Butterworth Band Pass Filter and Hilbert Huang Transform. After pre-processing, the filtered EEG signal is manipulated for sub-band separation and it is fissioned into five frequency bands such as Gamma, Beta, Alpha, Theta, and Delta. This work employs features such as energy, entropy, and variance which are computed for each frequency band obtained from the decomposed EEG signals. The selected features are imported for the classification process by using machine learning classifiers including Support Vector Machine (SVM) with Kernel Functions, K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN). The performance measures such as accuracy, sensitivity, and specificity are computed and analyzed for each classifier and it is inferred that the Support Vector Machine based classification of sleep apnea produces promising results.

Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings.

Complexity, costs, and waiting list issues demand a simplified alternative for sleep apnea-hypopnea syndrome (SAHS) diagnosis. The blood oxygen saturation signal (SpO2) carries useful information about SAHS and can be easily acquired from overnight oximetry. In this study, SpO2 single-channel recordings from 320 subjects were obtained at patients' homes and were used to automatically obtain statistical, spectral, nonlinear, and clinical SAHS-related information. Relevant, nonredundant data from these analyses were subsequently used to train and validate four machine-learning methods with the ability to classify SpO2 signals into one of the four SAHS-severity degrees (no-SAHS, mild, moderate, and severe). All the models trained (linear discriminant analysis, 1-vs-all logistic regression, Bayesian multilayer perceptron, and AdaBoost) outperformed the diagnostic ability of the conventionally used 3% oxygen desaturation index. An AdaBoost model built with linear discriminants as base classifiers reached the highest figures. It achieved 0.479 Cohen's κ in the SAHS severity classification, as well as 92.9%, 87.4%, and 78.7% accuracies in binary classification tasks using increasing severity thresholds (apnea-hypopnea index: 5, 15, and 30 events/hour, respectively). These results suggest that machine-learning can be used along with SpO2 information acquired at a patients' home to help in SAHS diagnosis simplification.

Varying Hypopnea Definitions Affect Obstructive Sleep Apnea Severity Classification and Association With Cardiovascular Disease.

STUDY OBJECTIVES: To compare clinical features and cardiovascular risks in patients with obstructive sleep apnea (OSA) based on ≥ 3% desaturation or arousal, and ≥ 4% desaturation hypopnea criteria. METHODS: This is a cross-sectional analysis of 1,400 veterans who underwent polysomnography for suspected sleep-disordered breathing. Hypopneas were scored using ≥ 4% desaturation criteria per the American Academy of Sleep Medicine (AASM) 2007 guidelines, then re-scored using ≥ 3% desaturation or arousal criteria per AASM 2012 guidelines. The effect on OSA disease categorization by these two different definitions were compared and correlated with symptoms and cardiovascular associations using unadjusted and adjusted logistic regression. RESULTS: The application of the ≥ 3% desaturation or arousal definition of hypopnea captured an additional 175 OSA diagnoses (12.5%). This newly diagnosed OSA group (OSAnew) was symptomatic with daytime sleepiness similarly to those in whom OSA had been diagnosed based on ≥ 4% desaturation criteria (OSA4%). The OSAnew group was more obese and more likely to be male than those without OSA based on either criterion (No-OSA). However, the OSAnew group was younger, less obese, more likely female, and had a lesser smoking history compared to the OSA4% group. Those with any severity of OSA4% had an increased adjusted odds ratio for arrhythmias (odds ratio = 1.95 [95% confidence interval 1.37-2.78], P = .0155). The more inclusive hypopnea definition (ie, ≥ 3% desaturation or arousal) resulted in recategorization of OSA diagnosis and severity, and attenuated the increased odds ratio for arrhythmias observed in mild and moderate OSA4%. However, severe OSA based on ≥ 3% desaturation or arousals (OSA3%/Ar) remained a significant risk factor for arrhythmias. OSA based on any definition was not associated with ischemic heart disease or heart failure. CONCLUSIONS: The most current AASM criteria for hypopnea identify a unique group of patients who are sleepy, but who are not at increased risk for cardiovascular disease. Though the different hypopnea definitions result in recategorization of OSA severity, severe disease whether defined by ≥ 3% desaturation/arousals or ≥ 4% desaturation remains predictive of cardiac arrhythmias. COMMENTARY: A commentary on this article appears in this issue on page 1971.

Use of Chest Wall EMG to Classify Hypopneas as Obstructive or Central.

STUDY OBJECTIVES: To compare classification of hypopneas as obstructive or central based on an effort signal derived from surface chest wall electromyography (CW-EMG-EF) coupled with airflow amplitude versus classification using The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications (AASM Scoring Manual) criteria; and to characterize hypopneas classified as obstructive versus central using a resistance surrogate. METHODS: CW-EMG was recorded in the eighth intercostal space at the right midaxillary line. Five hypopneas were randomly selected from 65 consecutive adult clinical positive airway pressure titration studies meeting study criteria. A blinded scorer classified the hypopneas based on two groups of signals: Group 1: positive airway pressure flow (PAP flow), chest and abdominal effort, and snoring; or Group 2: smoothed PAP flow (for blinding amplitude but not flattening visible) and effort (CW-EMG-EF). A resistance surrogate (CW-EMG-EF / PAP flow) normalized to a pre-event breath was compared between obstructive and central hypopneas classified by AASM Scoring Manual criteria. RESULTS: The percentage agreement (Group 1 versus Group 2) was 92% and the kappa was 0.75 (95% confidence interval 0.65 to 0.85). The resistance surrogate was significantly higher in obstructive hypopneas versus central hypopneas during the first and second half of hypopneas. The resistance surrogate (mean ± standard deviation) for the second half of hypopnea was obstructive: 7.59 ± 7.24 versus central: 1.27 ± 0.56, P < .001). The resistance surrogate increased from the first to second half of hypopnea only for obstructive hypopneas. CONCLUSIONS: CW-EMG provides a useful complementary signal for hypopnea classification and a resistance surrogate based on CW-EMG is much higher in hypopneas classified as obstructive by AASM Scoring Manual criteria.

Prevalence and Clinical Correlates of Sleep-Disordered Breathing in Patients Hospitalized With Acute Decompensated Heart Failure.

BACKGROUND: Limited data are available regarding the presence of sleep-disordered breathing (SDB) assessed using polysomnography in patients hospitalized with left ventricular (LV) systolic dysfunction after acute decompensated heart failure (ADHF). We investigated the prevalence and clinical correlates of SDB in patients hospitalized with ADHF and LV systolic dysfunction. METHODS: Prospectively collected data from 105 consecutive patients with an LV ejection fraction < 50% who were hospitalized with ADHF from May 2012 to July 2014 were retrospectively assessed. Polysomnography was performed during the initial hospitalization after the initial improvement in ADHF acute signs and symptoms. The apnea-hypopnea index (AHI), including obstructive or central AHI, was computed as a severity of obstructive or central sleep apnea. Echocardiography and blood sampling for various parameters, such as B-type natriuretic peptide level, were performed systematically. RESULTS: The proportions of patients with an AHI ≥ 5 events per hour and those with an AHI ≥ 15 events per hour were 93% and 69%, respectively, and central sleep apnea was predominant (66% and 44%, respectively). In the multivariate analysis, only body mass index (BMI) was independently correlated with AHI, whereas age, BMI, and E/e' level were independently correlated with obstructive AHI. In addition, use of loop diuretics and E/e' level were independently correlated with central AHI. CONCLUSIONS: SDB determined using polysomnography was common in hospitalized patients with ADHF and LV systolic dysfunction. Age, BMI, and E/e' levels were significantly correlated with obstructive sleep apnea severity, whereas E/e' levels and use of loop diuretics were significantly correlated with central sleep apnea severity.

[Guidelines in Practice: The New S3 Guideline "Sleeping Disorders - Sleep-Related Abnormal Breathing"]. / Leitlinien in der Praxis: Die neue S3-Leitlinie "Nicht erholsamer Schlaf/Schlafstörungen" ­ Kapitel "Schlafbezogene Atmungsstörungen".

Sleep related breathing disorders include central sleep apnea (CSA), obstructive sleep apnea (OSA), sleep-related hypoventilation, and sleep-related hypoxia. These disorders are frequent and growing in clinical relevance. The related chapter of the S3 guideline "Non-restorative sleep/Sleep disorders", published by the German Sleep Society (DGSM), has recently been updated in November 2016. Epidemiology, diagnostics, therapeutic procedures, and classification of sleep related disorders have been revised. Concerning epidemiology, a considerably higher mortality rate among pregnant women with OSA has been emphasized. With regards to diagnostics, the authors point out that respiratory polygraphy may be sufficient in diagnosing OSA, if a typical clinical condition is given. For CSA, recommendations were changed to diagnose CSA with low apnea rates present. Significant changes for treating CSA in patients with left ventricular dysfunction have been introduced. In addition, there is now to be differentiated between sleep-related hypoventilation and sleep-related hypoxaemia. Obesity hypoventilation syndrome is discussed in more detail. This article sums up and comments on the published changes.

Heart failure and sleep disordered breathing.

Sleep-disordered breathing (SDB) is frequently observed in patients with heart failure (HF), and complex pathologic conditions exist between both conditions. In this review article, we describe the characteristics of SDB complicated with HF, the prognostic impact of SDB in HF patients, and the favorable effects of positive airway pressure in HF patients with SDB.

[Guidelines in Practice: The New S3 Guideline "Sleeping Disorders - Sleep-Related Abnormal Breathing"]. / Leitlinien in der Praxis: Die neue S3-Leitlinie "Nicht erholsamer Schlaf/Schlafstörungen" ­ Kapitel "Schlafbezogene Atmungsstörungen".

Sleep related breathing disorders include central sleep apnea (CSA), obstructive sleep apnea (OSA), sleep-related hypoventilation, and sleep-related hypoxia. These disorders are frequent and growing in clinical relevance. The related chapter of the S3 guideline "Non-restorative sleep/Sleep disorders", published by the German Sleep Society (DGSM), has recently been updated in November 2016. Epidemiology, diagnostics, therapeutic procedures, and classification of sleep related disorders have been revised. Concerning epidemiology, a considerably higher mortality rate among pregnant women with OSA has been emphasized. With regards to diagnostics, the authors point out that respiratory polygraphy may be sufficient in diagnosing OSA, if a typical clinical condition is given. For CSA, recommendations were changed to diagnose CSA with low apnea rates present. Significant changes for treating CSA in patients with left ventricular dysfunction have been introduced. In addition, there is now to be differentiated between sleep-related hypoventilation and sleep-related hypoxaemia. Obesity hypoventilation syndrome is discussed in more detail. This article sums up and comments on the published changes.

Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review.

BACKGROUND AND OBJECTIVE: Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios. METHODS: This study aims to systematically review the literature on systems for the detection and/or prediction of apnea events using a classification model. RESULTS: Forty-five included studies revealed a combination of classification techniques for the diagnosis of apnea, such as threshold-based (14.75%) and machine learning (ML) models (85.25%). In addition, the ML models, were clustered in a mind map, include neural networks (44.26%), regression (4.91%), instance-based (11.47%), Bayesian algorithms (1.63%), reinforcement learning (4.91%), dimensionality reduction (8.19%), ensemble learning (6.55%), and decision trees (3.27%). CONCLUSIONS: A classification model should provide an auto-adaptive and no external-human action dependency. In addition, the accuracy of the classification models is related with the effective features selection. New high-quality studies based on randomized controlled trials and validation of models using a large and multiple sample of data are recommended.