Screening of obstructive sleep apnea using Hilbert-Huang decomposition of oronasal airway pressure recordings

Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review

Obstructive sleep apnea (OSA) is a chronic condition affecting up to 1 billion people, globally. Despite this spread, OSA is still thought to be underdiagnosed. Lack of diagnosis is largely attributed to the high cost, resource-intensive, and time-consuming nature of existing diagnostic technologies during sleep. As individuals with OSA do not show many symptoms other than daytime sleepiness, predicting OSA while the individual is awake (wakefulness) is quite challenging. However, research especially in the last decade has shown promising results for quick and accurate methodologies to predict OSA during wakefulness. Furthermore, advances in machine learning algorithms offer new ways to analyze the measured data with more precision. With a widening research outlook, the present review compares methodologies for OSA screening during wakefulness, and recommendations are made for avenues of future research and study designs.

Dynamic Bayesian Model for Detecting Obstructive Respiratory Events by Using an Experimental Model

In this study, we propose a model-based tool for the detection of obstructive apnea episodes by using ECG features from a single lead channel. Several sequences of recurrent apnea were provoked in separate 15-min periods in anesthetized rats during an experimental model of obstructive sleep apnea (OSA). Morphology-based ECG markers and the beat-to-beat interval (RR) were assessed in each sequence. These markers were used to train dynamic Bayesian networks (DBN) with different orders and feature combinations to find a good tradeoff between network complexity and apnea-detection performance. By using a filtering approach, the resulting DBNs were used to infer the apnea probability signal for subsequent episodes in the same rat. These signals were then processed using by 15-s epochs to determine whether epochs were classified as apneic or nonapneic. Our results showed that fifth-order models provided suitable RMSE values, since higher order models become significantly more complex and present worse generalization. A global threshold of 0.2 gave the best overall performance for all combinations tested, with Acc = 81.3%, Se = 69.8% and Sp = 81.5%, using only two parameters including the RR and Ds (R-wave downslope) markers. We concluded that multivariate models using DBNs represent a powerful tool for detecting obstructive apnea episodes in short segments, which may also serve to estimate the number of total events in a given time period.

Empirical Mode Decomposition and Hilbert Spectrum for Abnormality Detection in Normal and Abnormal Walking Transitions

Sensor-based human activity recognition (HAR) is a method for observing a person's activity in an environment. With this method, it is possible to monitor remotely. HAR can analyze a person's gait, whether normal or abnormal. Some of its applications may use several sensors mounted on the body, but this method tends to be complex and inconvenient. One alternative to wearable sensors is using video. One of the most commonly used HAR platforms is PoseNET. PoseNET is a sophisticated platform that can detect the skeleton and joints of the body, which are then known as joints. However, a method is still needed to process the raw data from PoseNET to detect subject activity. Therefore, this research proposes a way to detect abnormalities in gait using empirical mode decomposition and the Hilbert spectrum and transforming keys-joints, and skeletons from vision-based pose detection into the angular displacement of walking gait patterns (signals). Joint change information is extracted using the Hilbert Huang Transform to study how the subject behaves in the turning position. Furthermore, it is determined whether the transition goes from normal to abnormal subjects by calculating the energy in the time-frequency domain signal. The test results show that during the transition period, the energy of the gait signal tends to be higher than during the walking period.

Airflow Analysis in the Context of Sleep Apnea

The airflow (AF) is a physiological signal involved in the overnight polysomnography (PSG) that reflects the respiratory activity. This signal is able to show the particularities of sleep apnea and is therefore used to define apneic events. In this regard, a growing number of studies have shown the usefulness of employing the overnight airflow as the only or combined information source for diagnosing sleep apnea in both children and adults. Due to its easy acquisition and interpretation, this biosignal has been widely analyzed by means of different signal processing techniques. In this chapter, we review the main methodological approaches applied to characterize and extract relevant information from this signal. In view of the results, we can conclude that the overnight airflow successfully reflects the particularities caused by the occurrence of apneic and hypopneic events and provides useful information for obtaining relevant biomarkers that characterize this disease.

Voice Disorder Identification by using Hilbert-Huang Transform (HHT) and K Nearest Neighbor (KNN)

OBJECTIVES

Clinical evaluation of dysphonic voices involves a multidimensional approach, including a variety of instrumental and noninstrumental measures. Acoustic analyses provide an objective, noninvasive and intelligent measures of voice quality. Based on sound recordings, this paper proposes a new classification method of voice disorders with HHT and KNN.

METHODS

In this research, 12 features of each sample is calculated by HHT. Based on the algorithm of Linear Prediction Coefficient (LPCC), a sample can be characterized by 9 features. After each sample is expressed by 21 features, the classifier is constructed based on KNN. In addition, classifier based on KNN was further compared with random forest and extra trees classifiers in relation to their classification performance of voice disorder.

RESULTS

The experiment results revel that classifier based on KNN showed better performance than other two classifiers with accuracy rate of 93.3%, precision of 93%, recall rate of 95%, F1-score of 94% and the area of receiver operating characteristic curve is 0.976.

CONCLUSIONS

The method put forward in this paper can be effectively used to classify voice disorders.

Sleep Apnea and Hypopnea Events Detection Based on Airflow Signals Using LSTM Network

Sleep Apnea-Hypopnea Syndrome (SAHS) is a sleep-related breathing disorder which involves the reduction in breathing airway when patiens sleep. However, a large proportion of patients are usually undiagnosed and untreated which may lead to the risk of life. In this paper, we propose an automatic SAHS event detection method based on Long Short-Term Memory (LSTM) network via nasal airway pressure and temperature signals from clinical polysomnography (PSG) dataset. Focusing on time location of the events, we firstly segment the two channels of signals into a series of sequences by feature extraction. Secondly, a LSTM network is established and these sequences are subsequently fed into this LSTM network for SAHS event classification. The experimental results on both our clinical PSG dataset and public MIT-BIH PSG database show that our method is promising in terms of recall.

Frequency Network Analysis of Heart Rate Variability for Obstructive Apnea Patient Detection

Obstructive sleep apnea (OSA) is a popular sleep disorder. Traditional OSA diagnosis methods are cumbersome and expensive, which bring inconvenience for patient diagnosis and heavy workload for physician. Automatically identifying OSA patients from electrocardiogram (ECG) records is important for clinical diagnosis and treatment. In this paper, a new method based on the frequency and network domains is proposed to automatically recognize OSA patients with nocturnal ECG records. First, each RR-interval (beat to beat heart rate) series was divided into segments. By calculating the power spectral density (PSD) of heart rate variability segment with Lomb-Scargle method, the dynamic time warping (DTW) distance was used to evaluate the similarity (dissimilarity) of the lower frequency in the PSD series, then the DTW distance matrix was transformed to a binary matrix, and then network metrics were calculated to discriminate OSA patients with healthy subjects. The new method was tested with data of 389 subjects collected from two public databases that consist of normal subjects without OSA (apnea-hypopnea index, AHI 5) and OSA patients (AHI 5). Results show that a single network metric (local clustering coefficient) can recognize OSA patients with 90.1% accuracy, 88.29% sensitivity, and 90.5% specificity, and confirm the potential of using the ECG records for OSA patients recognition.

Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine

Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.

Apnea-hypopnea index prediction through an assessment of autonomic influence on heart rate in wakefulness

With the high prevalence of obstructive sleep apnea, the issue of developing a practical tool for obstructive sleep apnea screening has been raised. Conventional obstructive sleep apnea screening tools are limited in their ability to help clinicians make rational decisions due to their inability to predict the apnea-hypopnea index. Our study aimed to develop a new prediction model that can provide a reliable apnea-hypopnea index value during wakefulness. We hypothesized that patients with more severe obstructive sleep apnea would exhibit more attenuated waking vagal tone, which may result in lower effectiveness in decreasing heart rate as a response to deep inspiration breath-holding. Prior to conducting nocturnal in-laboratory polysomnography, 30 non-obstructive sleep apnea (apnea-hypopnea index<5events/h) subjects and 246 patients with obstructive sleep apnea participated in a 75-second experiment that consisted of a 60-second baseline measurement and consecutive 15-second deep inspiration breath-hold sessions. Two apnea-hypopnea index predictors were devised by considering the vagal activities reflected in the electrocardiographic recordings acquired during the experiment. Using the predictors obtained from 184 individuals, regression analyses and k-fold cross-validation tests were performed to develop an apnea-hypopnea index prediction model. For the remaining 92 individuals, the developed model provided an absolute error (mean±SD) of 3.53±2.67events/h and a Pearson's correlation coefficient of 0.99 (P<0.01) between the apnea-hypopnea index predictive values and the reference values reported by polysomnography. Our study is the first to achieve reliable and time-efficient prediction of the apnea-hypopnea index during wakefulness.

Computer-Assisted Automated Scoring of Polysomnograms Using the Somnolyzer System

STUDY OBJECTIVES

Manual scoring of polysomnograms is a time-consuming and tedious process. To expedite the scoring of polysomnograms, several computerized algorithms for automated scoring have been developed. The overarching goal of this study was to determine the validity of the Somnolyzer system, an automated system for scoring polysomnograms.

DESIGN

The analysis sample comprised of 97 sleep studies. Each polysomnogram was manually scored by certified technologists from four sleep laboratories and concurrently subjected to automated scoring by the Somnolyzer system. Agreement between manual and automated scoring was examined. Sleep staging and scoring of disordered breathing events was conducted using the 2007 American Academy of Sleep Medicine criteria.

SETTING

Clinical sleep laboratories.

MEASUREMENTS AND RESULTS

A high degree of agreement was noted between manual and automated scoring of the apnea-hypopnea index (AHI). The average correlation between the manually scored AHI across the four clinical sites was 0.92 (95% confidence interval: 0.90-0.93). Similarly, the average correlation between the manual and Somnolyzer-scored AHI values was 0.93 (95% confidence interval: 0.91-0.96). Thus, interscorer correlation between the manually scored results was no different than that derived from manual and automated scoring. Substantial concordance in the arousal index, total sleep time, and sleep efficiency between manual and automated scoring was also observed. In contrast, differences were noted between manually and automated scored percentages of sleep stages N1, N2, and N3.

CONCLUSION

Automated analysis of polysomnograms using the Somnolyzer system provides results that are comparable to manual scoring for commonly used metrics in sleep medicine. Although differences exist between manual versus automated scoring for specific sleep stages, the level of agreement between manual and automated scoring is not significantly different than that between any two human scorers. In light of the burden associated with manual scoring, automated scoring platforms provide a viable complement of tools in the diagnostic armamentarium of sleep medicine.

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.

Positive airway pressure and electrical stimulation methods for obstructive sleep apnea treatment: a patent review (2005 - 2014)

INTRODUCTION

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a major health problem with significant negative effects on the health and quality of life. Continuous positive airway pressure (CPAP) is currently the primary treatment option and it is considered the most effective therapy for OSAHS. Nevertheless, comfort issues due to improper fit to patient's changing needs and breathing gas leakage limit the patient's adherence to treatment.

AREAS COVERED

The present patent review describes recent innovations in the treatment of OSAHS related to optimization of the positive pressure delivered to the patient, methods and systems for continuous self-adjusting pressure during inspiration and expiration phases, and techniques for electrical stimulation of nerves and muscles responsible for the airway patency.

EXPERT OPINION

In the last few years, CPAP-related inventions have mainly focused on obtaining an optimal self-adjusting pressure according to patient's needs. Despite intensive research carried out, treatment compliance is still a major issue. Hypoglossal electrical nerve stimulation could be an effective secondary treatment option when CPAP primary therapy fails. Several patents have been granted focused on selective stimulation techniques and parameter optimization of the stimulating pulse waveform. Nevertheless, there remain important issues to address, like effectiveness and adverse events due to improper stimulation.

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.

Identification of Obstructive Sleep Apnea patients from tracheal breath sound analysis during wakefulness in polysomnographic studies

Obstructive Sleep Apnea (OSA) is currently diagnosed by a full nocturnal polysomnography (PSG), a very expensive and time-consuming method. In previous studies we were able to distinguish patients with OSA through formant frequencies of breath sound during sleep. In this study we aimed at identifying OSA patients from breath sound analysis during wakefulness. The respiratory sound was acquired by a tracheal microphone simultaneously to PSG recordings. We selected several cycles of consecutive inspiration and exhalation episodes in 10 mild-moderate (AHI<;30) and 13 severe (AHI>=30) OSA patients during their wake state before getting asleep. Each episode's formant frequencies were estimated by linear predictive coding. We studied several formant features, as well as their variability, in consecutive inspiration and exhalation episodes. In most subjects formant frequencies were similar during inspiration and exhalation. Formant features in some specific frequency band were significantly different in mild OSA as compared to severe OSA patients, and showed a decreasing correlation with OSA severity. These formant characteristics, in combination with some anthropometric measures, allowed the classification of OSA subjects between mild-moderate and severe groups with sensitivity (specificity) up to 88.9% (84.6%) and accuracy up to 86.4%. In conclusion, the information provided by formant frequencies of tracheal breath sound recorded during wakefulness may allow identifying subjects with severe OSA.

Using the Hilbert-Huang transform to measure the electroencephalographic effect of propofol

Monitoring the effect of anesthetic drugs on the central nervous system is a major ongoing challenge in anesthesia research. A number of electroencephalogram (EEG)-based monitors of the anesthetic drug effect such as the bispectral (BIS) index have been proposed to analyze the EEG signal during anesthesia. However, the BIS index has received some criticism. This paper offers a method based on the Hilbert-Huang transformation to calculate an index, called the Hilbert-Huang weighted regional frequency (HHWRF), to quantify the effect of propofol on brain activity. The HHWRF and BIS indices are applied to EEG signals collected from nine patients during a controlled propofol induction and emergence scheme. The results show that both the HHWRF and BIS track the gross changes in the EEG with increasing and decreasing anesthetic drug effect (the prediction probability P(k) of 0.85 and 0.83 for HHWRF and BIS, respectively). Our new index can reflect the transition from unconsciousness to consciousness faster than the BIS, as indicated from the pharmacokinetic and pharmacodynamic modeled parameters and also from the analysis around the point of reawakening. This method could be used to design a new EEG monitoring system to estimate the propofol anesthetic drug effect.

Multiclass classification of subjects with sleep apnoea-hypopnoea syndrome through snoring analysis

The gold standard for diagnosing sleep apnoea-hypopnoea syndrome (SAHS) is polysomnography (PSG), an expensive, labour-intensive and time-consuming procedure. Accordingly, it would be very useful to have a screening method to allow early assessment of the severity of a subject, prior to his/her referral for PSG. Several differences have been reported between simple snorers and SAHS patients in the acoustic characteristics of snoring and its variability. In this paper, snores are fully characterised in the time domain, by their sound intensity and pitch, and in the frequency domain, by their formant frequencies and several shape and energy ratio measurements. We show that accurate multiclass classification of snoring subjects, with three levels of SAHS, can be achieved on the basis of acoustic analysis of snoring alone, without any requiring information on the duration or the number of apnoeas. Several classification methods are examined. The best of the approaches assessed is a Bayes model using a kernel density estimation method, although good results can also be obtained by a suitable combination of two binary logistic regression models. Multiclass snore-based classification allows early stratification of subjects according to their severity. This could be the basis of a single channel, snore-based screening procedure for SAHS.

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

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

Assessment of obstructive sleep apnea and its severity during wakefulness

In this article, a novel technique for assessment of obstructive sleep apnea (OSA) during wakefulness is proposed; the technique is based on tracheal breath sound analysis of normal breathing in upright sitting and supine body positions. We recorded tracheal breath sounds of 17 non-apneic individuals and 35 people with various degrees of severity of OSA in supine and upright sitting positions during both nose and mouth breathing at medium flow rate. We calculated the power spectrum, Kurtosis, and Katz fractal dimensions of the recorded signals and used the one-way analysis of variance to select the features, which were statistically significant between the groups. Then, the maximum relevancy minimum redundancy method was used to reduce the number of characteristic features to two. Using the best two selected features, we classified the participant into severe OSA and non-OSA groups as well as non-OSA or mild vs. moderate and severe OSA groups; the results showed more than 91 and 83% accuracy; 85 and 81% specificity; 92 and 95% sensitivity, for the two types of classification, respectively. The results are encouraging for identifying people with OSA and also prediction of OSA severity. Once verified on a larger population, the proposed method offers a simple and non-invasive screening tool for prediction of OSA during wakefulness.

Obstructive sleep apnea prediction during wakefulness

In this paper, a novel technique based on signal processing of breath sounds during wakefulness for prediction of obstructive sleep apnea (OSA) is proposed. We recorded tracheal breath sounds of 35 people with various severity of OSA and 17 non-apneic individuals; the breath sounds were recorded in supine and upright positions during both nose and mouth breathing at medium flow rate. Power spectrum, Kurtosis and Katz fractal dimension of the recorded signals in every posture and breathing maneuver were calculated. We used one-way ANOVA to select the features with most significant differences between the groups followed by the Maximum Relevancy Minimum Redundancy (mRMR) method to reduce the number of characteristic features to three, and investigated the separability of the groups based on the three selected features. The results are encouraging for classification of patients using the selected features. Once being verified on a larger population, the proposed method offers a fast, simple and non-invasive screening tool for prediction of OSA during wakefulness.