Snoring analysis for the screening of Sleep Apnea Hypopnea Syndrome with a single-channel device developed using polysomnographic and snoring databases

Correlation between snoring sounds and obstructive sleep apnea in adults: a meta-regression analysis

OBJECTIVE

Snoring is a dominant clinical symptom in patients with obstructive sleep apnea (OSA), and analyzing snoring sounds might be a potential alternative to polysomnography (PSG) for the assessment of OSA. This study aimed to systematically examine the correlation between the snoring sounds and the apnea-hypopnea index (AHI) as the measures of OSA severity.

MATERIAL AND METHODS

A comprehensive literature review using the MEDLINE, Embase, Cochrane Library, Scopus, and PubMed databases identified the published studies reporting the correlations between and severity of snoring and the AHI values by meta-regression analysis.

RESULTS

In total, 13 studies involving 3,153 adult patients were included in this study. The pooled correlation coefficient for snoring sounds and AHI values was 0.71 (95%CI: 0.49, 0.85) from the random-effects meta-analysis with the Knapp and Hartung adjustment. The I 2 and chi-square Q test demonstrated significant heterogeneity (97.6% and p<0.001). After adjusting for the effects of the other covariates, the mean value of the Fisher's r-to-z transformed correlation coefficient would have 0.80 less by the snoring rate (95%CI = -1.02, -0.57), 1.46 less by the snoring index (95%CI = -1.85, -1.07), and 0.21 less in the mean body mass index (95%CI = -0.31, -0.11), but 0.15 more in the mean age (95%CI = 0.10, 0.20). It fitted the data very well (R 2=0.9641).

CONCLUSION

A high correlation between the severity of snoring and the AHI was found in the studies with PSG. As compared to the snoring rate and the snoring index, the snoring intensity, the snoring frequency, and the snoring time interval index were more sensitive measures for the severity of snoring.

A Minimum Set of Physiological Parameters to Diagnose Obstructive Sleep Apnea Syndrome Using Non-Invasive Portable Monitors. A Systematic Review

Introduction. Despite its high accuracy, polysomnography (PSG) has several drawbacks for diagnosing obstructive sleep apnea (OSA). Consequently, multiple portable monitors (PMs) have been proposed. Objective. This systematic review aims to investigate the current literature to analyze the sets of physiological parameters captured by a PM to select the minimum number of such physiological signals while maintaining accurate results in OSA detection. Methods. Inclusion and exclusion criteria for the selection of publications were established prior to the search. The evaluation of the publications was made based on one central question and several specific questions. Results. The abilities to detect hypopneas, sleep time, or awakenings were some of the features studied to investigate the full functionality of the PMs to select the most relevant set of physiological signals. Based on the physiological parameters collected (one to six), the PMs were classified into sets according to the level of evidence. The advantages and the disadvantages of each possible set of signals were explained by answering the research questions proposed in the methods. Conclusions. The minimum number of physiological signals detected by PMs for the detection of OSA depends mainly on the purpose and context of the sleep study. The set of three physiological signals showed the best results in the detection of OSA.

Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network

Obstructive sleep apnea/hypopnea syndrome (OSAHS) is characterized by repeated airflow partial reduction or complete cessation due to upper airway collapse during sleep. OSAHS can induce frequent awake and intermittent hypoxia that is associated with hypertension and cardiovascular events. Full-channel Polysomnography (PSG) is the gold standard for diagnosing OSAHS; however, this PSG evaluation process is unsuitable for home screening. To solve this problem, a measuring module integrating abdominal and thoracic triaxial accelerometers, a pulsed oximeter (SpO₂) and an electrocardiogram sensor was devised in this study. Moreover, a long short-term memory recurrent neural network model is proposed to classify four types of sleep breathing patterns, namely obstructive sleep apnea (OSA), central sleep apnea (CSA), hypopnea (HYP) events and normal breathing (NOR). The proposed algorithm not only reports the apnea-hypopnea index (AHI) through the acquired overnight signals but also identifies the occurrences of OSA, CSA, HYP and NOR, which assists in OSAHS diagnosis. In the clinical experiment with 115 participants, the performances of the proposed system and algorithm were compared with those of traditional expert interpretation based on PSG signals. The accuracy of AHI severity group classification was 89.3%, and the AHI difference for PSG expert interpretation was 5.0±4.5. The overall accuracy of detecting abnormal OSA, CSA and HYP events was 92.3%.

Automatic Silence Events Detector from Smartphone Audio Signals: A Pilot mHealth System for Sleep Apnea Monitoring at Home

Obstructive sleep apnea (OSA) is a prevalent disease, but most patients remain undiagnosed and untreated. Recently, mHealth tools are being proposed to screen OSA patients at home. In this work, we analyzed full-night audio signals recorded with a smartphone microphone. Our objective was to develop an automatic detector to identify silence events (apneas or hypopneas) and compare its performance to a commercial portable system for OSA diagnosis (ApneaLink™, ResMed). To do that, we acquired signals from three subjects with both systems simultaneously. A sleep specialist marked the events on smartphone and ApneaLink signals. The automatic detector we developed, based on the sample entropy, identified silence events similarly than manual annotation. Compared to ApneaLink, it was very sensitive to apneas (detecting 86.2%) and presented an 83.4% positive predictive value, but it missed about half the hypopnea episodes. This suggests that during some hypopneas the flow reduction is not reflected in sound. Nevertheless, our detector accurately recognizes silence events, which can provide valuable respiratory information related to the disease. These preliminary results show that mHealth devices and simple microphones are promising non-invasive tools for personalized sleep disorders management at home.

A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis

The severity of obstructive sleep apnoea (OSA) is diagnosed with polysomnography (PSG), during which patients are monitored by over 20 physiological sensors overnight. These sensors often bother patients and may affect patients’ sleep and OSA. This study aimed to investigate a method for analyzing patient snore sounds to detect the severity of OSA. Using a microphone placed at the patient’s bedside, the snoring and breathing sounds of 22 participants were recorded while they simultaneously underwent PSG. We examined some features from the snoring and breathing sounds and examined the correlation between these features and the snore-specific apnoea-hypopnea index (ssAHI), defined as the number of apnoea and hypopnea events during the hour before a snore episode. Statistical analyses revealed that the ssAHI was positively correlated with the Mel frequency cepstral coefficients (MFCC) and volume information (VI). Based on clustering results, mild snore sound episodes and snore sound episodes from mild OSA patients were mainly classified into cluster 1. The results of clustering severe snore sound episodes and snore sound episodes from severe OSA patients were mainly classified into cluster 2. The features of snoring sounds that we identified have the potential to detect the severity of OSA.

Characterization of microphones for snoring and breathing events analysis in mHealth

Obstructive sleep apnea (OSA) is one of the most common sleep disorders, especially in elderly population. Despite its high prevalence and severe consequences, most patients remain undiagnosed due to serious limitations on the existing equipment. Efforts are being done to find cost-effective alternatives and mHealth solutions could play a key role. One promising approach in this context is the acoustic analysis of snoring. The sensor it requires is a microphone, which is widely available in different models and even integrated in smartphones. The objective of this work is to characterize and compare the responses of two commercial tracheal microphones and a mHealth-based microphone, as a proof-of-concept to evaluate their potential as sensors for OSA detection. To do that, we designed an experimental protocol to study OSA-related events (breaths, snores and apneas) simulated by 4 subjects. Test signals were simultaneously recorded with different microphones and posteriorly processed and analyzed. We accurately characterized the frequency response of the two commercial microphones, finding that one of them was too restrictive (bandwidth 50-250 Hz) and thus not suitable as snoring sensor for high-frequency acoustic analysis. Regarding smartphones, we studied the Samsung Galaxy S5 microphone. We found that, when located over the thorax, it provided quality signals comparable to those of tracheal microphones, with a broader frequency response. Further work is required, but this preliminary study suggests that acoustic analysis of snoring through mHealth solutions can be a feasible alternative to screen and monitor OSA patients at home.

Breathing and Snoring Sound Characteristics during Sleep in Adults

STUDY OBJECTIVES

Sound level meter is the gold standard approach for snoring evaluation. Using this approach, it was established that snoring intensity (in dB) is higher for men and is associated with increased apnea-hypopnea index (AHI). In this study, we performed a systematic analysis of breathing and snoring sound characteristics using an algorithm designed to detect and analyze breathing and snoring sounds. The effect of sex, sleep stages, and AHI on snoring characteristics was explored.

METHODS

We consecutively recruited 121 subjects referred for diagnosis of obstructive sleep apnea. A whole night audio signal was recorded using noncontact ambient microphone during polysomnography. A large number (> 290,000) of breathing and snoring (> 50 dB) events were analyzed. Breathing sound events were detected using a signal-processing algorithm that discriminates between breathing and nonbreathing (noise events) sounds.

RESULTS

Snoring index (events/h, SI) was 23% higher for men (p = 0.04), and in both sexes SI gradually declined by 50% across sleep time (p < 0.01) independent of AHI. SI was higher in slow wave sleep (p < 0.03) compared to S2 and rapid eye movement sleep; men have higher SI in all sleep stages than women (p < 0.05). Snoring intensity was similar in both genders in all sleep stages and independent of AHI. For both sexes, no correlation was found between AHI and snoring intensity (r = 0.1, p = 0.291).

CONCLUSIONS

This audio analysis approach enables systematic detection and analysis of breathing and snoring sounds from a full night recording. Snoring intensity is similar in both sexes and was not affected by AHI.

Can we discriminate between apnea and hypopnea using audio signals?

Obstructive sleep apnea (OSA) affects up to 14% of the population. OSA is characterized by recurrent apneas and hypopneas during sleep. The apnea-hypopnea index (AHI) is frequently used as a measure of OSA severity. In the current study, we explored the acoustic characteristics of hypopnea in order to distinguish it from apnea. We hypothesize that we can find audio-based features that can discriminate between apnea, hypopnea and normal breathing events. Whole night audio recordings were performed using a non-contact microphone on 44 subjects, simultaneously with the polysomnography study (PSG). Recordings were segmented into 2015 apnea, hypopnea, and normal breath events and were divided to design and validation groups. A classification system was built using a 3-class cubic-kernelled support vector machine (SVM) classifier. Its input is a 36-dimensional audio-based feature vector that was extracted from each event. Three-class accuracy rate using the hold-out method was 84.7%. A two-class model to separate apneic events (apneas and hypopneas) from normal breath exhibited accuracy rate of 94.7%. Here we show that it is possible to detect apneas or hypopneas from whole night audio signals. This might provide more insight about a patient's level of upper airway obstruction during sleep. This approach may be used for OSA severity screening and AHI estimation.

Automatic detection of whole night snoring events using non-contact microphone

Objective

Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology.

Design

Sounds during polysomnography (PSG) were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events.

Patients

Sixty-seven subjects (age 52.5±13.5 years, BMI 30.8±4.7 kg/m², m/f 40/27) referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects.

Measurements and Results

To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental). A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy) for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore) and specificity of 98.3% (noise as noise).

Conclusions

Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients.