The acoustics of snoring

Unconstrained snoring detection using a smartphone during ordinary sleep

Background

Snoring can be a representative symptom of a sleep disorder, and thus snoring detection is quite important to improving the quality of an individual’s daily life. The purpose of this research is to develop an unconstrained snoring detection technique that can be integrated into a smartphone application. In contrast with previous studies, we developed a practical technique for snoring detection during ordinary sleep by using the built-in sound recording system of a smartphone, and the recording was carried out in a standard private bedroom.

Method

The experimental protocol was designed to include a variety of actions that frequently produce noise (including coughing, playing music, talking, rining an alarm, opening/closing doors, running a fan, playing the radio, and walking) in order to accurately recreate the actual circumstances during sleep. The sound data were recorded for 10 individuals during actual sleep. In total, 44 snoring data sets and 75 noise datasets were acquired. The algorithm uses formant analysis to examine sound features according to the frequency and magnitude. Then, a quadratic classifier is used to distinguish snoring from non-snoring noises. Ten-fold cross validation was used to evaluate the developed snoring detection methods, and validation was repeated 100 times randomly to improve statistical effectiveness.

Results

The overall results showed that the proposed method is competitive with those from previous research. The proposed method presented 95.07% accuracy, 98.58% sensitivity, 94.62% specificity, and 70.38% positive predictivity.

Conclusion

Though there was a relatively high false positive rate, the results show the possibility for ubiquitous personal snoring detection through a smartphone application that takes into account data from normally occurring noises without training using preexisting data.

Telephonic Analysis of the Snoring Sound Spectrum

Objective:

Snoring is a sound caused by vibration of collapsed and/or unsteady airway walls of the pharynx and soft palate. We compared stored spectra of snoring sounds recorded via cell phone (CP) and a microphone placed over the head (head phone [HP]).

Methods:

Thirty-four snoring patients were included in this prospective study. Groups were identified by reference to body mass index (BMI) values: group 1, BMI < 25 kg/m2 (n = 8); group 2, BMI 25 to 29 kg/m2 (n = 10); and group 3, BMI ≥ 30 kg/m2 (n = 16). Snoring sounds were recorded using CPs and HPs and digitally analyzed. We identified the frequencies with the highest snoring powers (Fmax values) and snoring sound intensity levels (SSILs).

Results:

Fmax ranged from 520 to 985 Hz in HP recordings and from 845 to 1645 Hz in CP recordings. Snoring sound intensity level values increased in proportion to BMI and were 6 to 24 dB in HP recordings and 19 to 52 dB in CP recordings. Thus, the CP values of Fmax and SSIL were higher than the HP values. In obese patients of group 3, almost all Fmax and SSIL values were higher than those of groups 1 and 2. In particular, the CP Fmax values were elevated in such patients. The advanced technologies used in modern CPs may allow some snoring sounds in susceptible individuals to be defined as oronasal.

Conclusion:

Cell phone technology allows snoring to be evaluated in patients located in areas remote from a hospital. To explore the intensity of snoring and to postoperatively monitor the efficacy of surgery used to treat snoring, telephonic sound analysis is both new and effective and reduces the need for patient attendance at a hospital. Those experiencing severe snoring and/or who are obese should be told of what can be done to solve such problems.

Simple snoring: not quite so simple after all?

Simple snoring (SS), in the absence of obstructive sleep apnoea (OSA), is a common problem, yet our understanding of its causes and consequences is incomplete. Our understanding is blurred by the lack of consistency in the definition of snoring, methods of assessment, and degree of concomitant complaints. Further, it remains contentious whether SS is independently associated with daytime sleepiness, or adverse health outcomes including cardiovascular disease and metabolic syndrome. Regardless of this lack of clarity, it is likely that SS exists on one end of a continuum, with OSA at its polar end. This possibility highlights the necessity of considering an otherwise 'annoying' complaint, as a serious risk factor for the development and progression of sleep apnoea, and consequent poor health outcomes. In this review, we: 1) highlight variation in prevalence estimates of snoring; 2) review the literature surrounding the distinctions between SS, upper airway resistance syndrome (UARS) and OSA; 3) present the risk factors for SS, in as far as it is distinguishable from UARS and OSA; and 4) describe common correlates of snoring, including cardiovascular disease, metabolic syndrome, and daytime sleepiness.

Capability of a neck worn device to measure sleep/wake, airway position, and differentiate benign snoring from obstructive sleep apnea

To evaluate the accuracy of a neck-worn device in measuring sleep/wake, detecting supine airway position, and using loud snoring to screen for obstructive sleep apnea. Study A included 20 subjects who wore the neck-device during polysomnography (PSG), with 31 records obtained from diagnostic and split-night studies. Study B included 24 community-based snorers studied in-home for up to three-nights with obstructive sleep apnea (OSA) severity measured with a validated Level III recorder. The accuracy of neck actigraphy-based sleep/wake was measured by assessing sleep efficiency (SE). Differences in sleep position measured at the chest and neck during PSG were compared to video-editing. Loud snoring acquired with an acoustic microphone was compared to the apnea-hypopnea index (AHI) by- and acrosspositions. Over-reported SE by neck actigraphy was inversely related to OSA severity. Measurement of neck and chest supine position were highly correlated with video-edits (r = 0.93, 0.78). Chest was bias toward over-estimating supine time while the majority of neck-device supine position errors occurred during CPAP titrations. Snoring was highly correlated with the overall, supine, and non-supine PSG-AHI (r = 0.79, 0.74, 0.83) and was both sensitive and specific in detecting overall, supine, and non-supine PSGAHI >10 (sensitivity = 81, 88, 82 %; specificity = 87, 79, 100 %). At home sleep testing-AHI > 10, the sensitivity and specificity of loud snoring was superior when users were predominantly non-supine as compared to baseline (sensitivity = 100, 92 %; specificity = 88, 77 %). Neck actigraphy appears capable of estimating sleep/wake. The accuracy of supine airway detection with the neck-device warrants further investigation. Measurement of loud snoring appears to provide a screening tool for differentiating positional apneic and benign snorers.

A review of signals used in sleep analysis

This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep.

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.

Mean tracheal sound energy during sleep is related to daytime blood pressure

STUDY OBJECTIVES

The pathological role of snoring independent of obstructive sleep apnea remains under debate. The authors hypothesized that snoring sound intensity, as assessed by mean tracheal sound energy (Leq) during sleep, is related to daytime blood pressure.

DESIGN

Retrospective analysis of clinical records and polysomnography data.

SETTING

Sleep laboratory at a national hospital in Japan.

PATIENTS

Consecutive patients who underwent diagnostic polysomnography with suspicion of sleep apnea between January 2005 and December 2009 (n = 1,118).

INTERVENTIONS

Not applicable.

MEASUREMENTS AND RESULTS

Leq was calculated from tracheal sound spectra recorded every 0.2 sec during polysomnography. Daytime high blood pressure (HBP) was defined as taking antihypertensive drugs or having a systolic blood pressure ≥ 140 mm Hg or a diastolic blood pressure ≥ 90 mmHg at the patient's first clinical visit. Patient age, sex, body mass index, apnea-hypopnea index, alcohol consumption, and smoking were considered as confounders. Leq during sleep was associated with HBP after adjusting for all confounding factors (n = 1,074, P = 0.00019). This association was demonstrated even in nonapneic nonobese patients (n = 232, P = 0.012).

CONCLUSIONS

The association between snoring intensity, as assessed by mean sound energy, and blood pressure suggests a pathological role for heavy snoring. Further study in a general population is warranted.

Snoring intensity after a first session of soft palate radiofrequency: predictive value of the final result

OBJECTIVES/HYPOTHESIS

To determine whether snoring sound intensity measured after a first soft palate radiofrequency (RF) session for simple snoring helps predict the final result of the treatment.

STUDY DESIGN

Observational retrospective study.

METHODS

We conducted a retrospective review of 105 subjects presenting with simple snoring or mild sleep apnea. All patients underwent two to three sessions of RF-assisted stiffening of the soft palate. In addition, uvulectomy was performed in case of a long uvula, and two paramedian trenches were created in the presence of palatal webbing. Snoring sound intensity was evaluated by the bed partner after each session.

RESULTS

Eighty-six men and 19 women were included in the study. Mean age was 51.7 ± 9.8 years, and mean body mass index was 24.7 ± 4.4 kg/m(2) . The mean apnea/hypopnea index was 6.6 ± 4.2/h. The mean snoring sound intensity, as evaluated on a 10-cm visual analog scale (VAS), decreased from 8.2 ± 1.5 to 3.5 ± 2.2 after all sessions (P < .0001). A score of 3 was determined as being a score that satisfied the bed partner. Two groups were formed according to the final snoring sound intensity, using 3 as a threshold. Both groups had similar preoperative characteristics, but the snoring sound intensity was significantly lower after the first session in the group with final score <3 (P = .01). Similarly, a VAS score >7 after the first session was associated with a final score <3 in 30% of the cases.

CONCLUSIONS

Snoring sound intensity after the first RF session helps predict the final outcome of RF-assisted stiffening of the soft palate for simple snoring.

Evaluation of upper airway obstruction in snoring patients using digital video stroboscopy

Stroboscopy is routinely used in voice disorder clinics but its use in studying the mechanisms of upper airway obstruction in patients who snore has not yet been described. This study combines the use of stroboscopy during sleep nasendoscopy to evaluate the oscillations and vibrations observed during snoring in slow motion. In addition, we utilised the multi-dimensional voice programme simultaneously to study some of the acoustic parameters of snoring whilst visualising the dynamics of the upper airway. Forty-five patients with primary snoring or mild obstructive sleep apnoea were recruited at two different centres and underwent sleep nasendoscopy. The simultaneous use of acoustic analysis was included to ascertain whether sound analysis alone could replace the need for using the sedation endoscopy in these patients. The use of a stroboscopic light source indeed enhanced the visualisation during the procedure and some subtle aspects of the mechanisms of upper airway obstruction, such as vibrations of the posterior pharyngeal wall and mucosal waves were identified. Most of the patients in this study exhibited multilevel obstruction and thus acoustic analysis alone would not be sufficient in accurately locating the site of upper airway obstruction in snorers.

Artificial neural networks for breathing and snoring episode detection in sleep sounds

Obstructive sleep apnea (OSA) is a serious disorder characterized by intermittent events of upper airway collapse during sleep. Snoring is the most common nocturnal symptom of OSA. Almost all OSA patients snore, but not all snorers have the disease. Recently, researchers have attempted to develop automated snore analysis technology for the purpose of OSA diagnosis. These technologies commonly require, as the first step, the automated identification of snore/breathing episodes (SBE) in sleep sound recordings. Snore intensity may occupy a wide dynamic range (> 95 dB) spanning from the barely audible to loud sounds. Low-intensity SBE sounds are sometimes seen buried within the background noise floor, even in high-fidelity sound recordings made within a sleep laboratory. The complexity of SBE sounds makes it a challenging task to develop automated snore segmentation algorithms, especially in the presence of background noise. In this paper, we propose a fundamentally novel approach based on artificial neural network (ANN) technology to detect SBEs. Working on clinical data, we show that the proposed method can detect SBE at a sensitivity and specificity exceeding 0.892 and 0.874 respectively, even when the signal is completely buried in background noise (SNR < 0 dB). We compare the performance of the proposed technology with those of the existing methods (short-term energy, zero-crossing rates) and illustrate that the proposed method vastly outperforms conventional techniques.

Normal non-regular snores as a tool for screening SAHS severity

Snoring is one of the earliest and most consistent sign of upper airway obstruction leading to Sleep Apnea-Hypopnea Syndrome (SAHS). Several studies on post-apneic snores, snores that are emitted immediately after an apnea, have already proven that this type of snoring is most distinct from that of normal snoring. However, post-apneic snores are more unlikely and sometimes even inexistent in simple snorers and mild SAHS subjects. In this work we address that issue by proposing the study of normal non-regular snores. They correspond to successive snores that are separated by normal breathing cycles. The results obtained establish the feasibility of acoustic parameters of normal non-regular snores as a promising tool for a prompt screening of SAHS severity.

Detection of post apnea sounds and apnea periods from sleep sounds

Obstructive Sleep Apnea Syndrome (OSAS) is defined as a sleep related breathing disorder that causes the body to stop breathing for about 10 seconds and mostly ends with a loud sound due to the opening of the airway. OSAS is traditionally diagnosed using polysomnography, which requires a whole night stay at the sleep laboratory of a hospital, with multiple electrodes attached to the patient's body. Snoring is a symptom which may indicate the presence of OSAS; thus investigation of snoring sounds, which can be recorded in the patient's own sleeping environment, has become popular in recent years to diagnose OSAS. In this study, we aim to develop a new method to detect post-apnea snoring episodes with the goal of diagnosing apnea or creating new criteria similar to apnea / hypopnea index. Emphasis is placed on detecting post apnea episodes, hence the apnea periods. In this method, first segmentation is done to eliminate the silence parts. Then, these episodes are represented by distinctive features; some of these features are available in literature but some of them are novel. Finally, episodes are classified using supervised methods. False alarm rates are reduced by adding additional constraints into the detection algorithm. These methods are applied to snoring sound signals of OSAS patients, recorded in Gulhane Military Medical Academy, to verify the success of our algorithms.

Bayes classification of snoring subjects with and without Sleep Apnea Hypopnea Syndrome, using a Kernel method

The gold standard for diagnosing Sleep Apnea Hypopnea Syndrome (SAHS) is the Polysomnography (PSG), an expensive, labor-intensive and time-consuming procedure. It would be helpful to have a simple screening method that allowed to early determining the severity of a subject prior to his/her enrolment for a PSG. Several differences have been reported in the acoustic snoring characteristics between simple snorers and SAHS patients. Previous studies usually classify snoring subjects into two groups given a threshold of Apnea-Hypoapnea Index (AHI). Recently, Bayes multi-group classification with Gaussian Probability Density Function (PDF) has been proposed, using snore features in combination with apnea-related information. In this work we show that the Bayes classifier with Kernel PDF estimation outperforms the Gaussian approach and allows the classification of SAHS subjects according to their severity, using only the information obtained from snores. This could be the base of a single channel, snore-based, screening procedure for SAHS.

Acoustic characteristics of snoring according to obstruction site determined by sleep videofluoroscopy

CONCLUSION

Acoustic characteristics of snoring sound, such as pitch and formant, differed according to the site of upper airway obstruction determined by sleep videofluoroscopy (SVF). Snoring sound analysis can complement determination of the site of obstruction in snoring and sleep apnea patients.

OBJECTIVES

The aim of this study was to evaluate the acoustic characteristics of snoring according to obstruction site determined by SVF.

METHODS

Ninety patients who underwent simultaneous snoring sound recording during SVF were included in this study. Acoustic parameters of snoring such as pitch (min, mean, max) and formant (1,2) were analyzed. Site of obstruction was determined by SVF and classified according to anatomic structure and level of obstruction.

RESULTS

Mean value of peak frequency showed significant difference between soft palate and isolated tongue base or epiglottis obstruction and combined obstruction involving soft palate and tongue base or epiglottis. Peak frequency of velopharyngeal obstruction showed difference only with hypopharyngeal obstruction. First formant showed similar results in the structure classification whereas velopharyngeal obstruction showed significant difference compared with other levels of obstruction. Other parameters (intensity, jitter, shimmer) did not show significance according to site of obstruction.

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

Several studies have shown differences in acoustic snoring characteristics between patients with Sleep Apnea-Hypopnea Syndrome (SAHS) and simple snorers. Usually a few manually isolated snores are analyzed, with an emphasis on postapneic snores in SAHS patients. Automatic analysis of snores can provide objective information over a longer period of sleep. Although some snore detection methods have recently been proposed, they have not yet been applied to full-night analysis devices for screening purposes. We used a new automatic snoring detection and analysis system to monitor snoring during full-night studies to assess whether the acoustic characteristics of snores differ in relation to the Apnea-Hypopnea Index (AHI) and to classify snoring subjects according to their AHI. A complete procedure for device development was designed, using databases with polysomnography (PSG) and snoring signals. This included annotation of many types of episodes by an expert physician: snores, inspiration and exhalation breath sounds, speech and noise artifacts, The AHI of each subject was estimated with classical PSG analysis, as a gold standard. The system was able to correctly classify 77% of subjects in 4 severity levels, based on snoring analysis and sound-based apnea detection. The sensitivity and specificity of the system, to identify healthy subjects from pathologic patients (mild to severe SAHS), were 83% and 100%, respectively. Besides, the Apnea Index (AI) obtained with the system correlated with the obtained by PSG or Respiratory Polygraphy (RP) (r=0.87, p<0.05).

Clinical and polysomnographic determinants of snoring

Snoring is considered one of the hallmarks of sleep-disordered breathing, but its determinants remain obscure in both obstructive sleep apnoea (apnoeic) and non-apnoeic snorers. We aimed to document positional dependency of snoring along with its association with clinical and polysomnographic variables. Seventy-seven apnoeic and 27 non-apnoeic snorers who complained for every-night loud snoring and slept in supine and lateral positions in all sleep stages during overnight polysomnography were included. Snoring (i.e. sound intensity > 40 dB) was quantified by measuring the mean and maximum sound intensity, and snoring frequency. In apnoeic and non-apnoeic snorers, mean snoring intensity and snoring frequency were higher in supine than in lateral positions irrespective of sleep stage, and were also usually higher in N3 in comparison to rapid eye movement and/or N2 sleep stage in any given position. Positional change in snoring intensity as expressed by the ratio of mean intensity in the supine to lateral positions was independently and positively correlated with body mass index, tonsils size and age in the total of patients. Snoring is more prominent in the supine position and in N3 sleep stage in apnoeic and non-apnoeic snorers. Snoring positional dependence is determined by body mass index, tonsils size and age.

Fluid-structure interaction modeling of upper airways before and after nasal surgery for obstructive sleep apnea

Nasal obstruction frequently has been associated with obstructive sleep apnea (OSA). Although correction of an obstructed nasal airway is considered an important component in OSA treatment, the effect of nasal surgery on OSA remains controversial. Variation in airway anatomy between before and after nasal surgery may cause significant differences in airflow patterns within the upper airway. In this paper, anatomically accurate models of the interaction between upper airway and soft palate were developed from prenasal and post-nasal surgery multidetector computed tomography data of a patient with OSA and nasal obstruction. Computational modeling for inspiration and expiration was performed by using fluid-structure interaction method. The airflow characteristics such as velocity, turbulence intensity and pressure drop, and displacement distribution of soft palate are selected for comparison. Airway resistances significantly decrease after the nasal surgery, especially in the velopharynx region because of an enlarged pharyngeal cavity and a reduced upstream resistance. Meanwhile, the decreased aerodynamic force would result in a smaller displacement of soft palates, which would lead to slight impact of the soft palate motion on the airflow characteristics. The present results suggest that airflow distribution in the whole upper airway and soft palate motions have improved following nasal surgery.

All night analysis of time interval between snores in subjects with sleep apnea hypopnea syndrome

Sleep apnea–hypopnea syndrome (SAHS) is a serious sleep disorder, and snoring is one of its earliest and most consistent symptoms. We propose a new methodology for identifying two distinct types of snores: the so-called non-regular and regular snores. Respiratory sound signals from 34 subjects with different ranges of Apnea-Hypopnea Index (AHI = 3.7–109.9 h⁻¹) were acquired. A total number of 74,439 snores were examined. The time interval between regular snores in short segments of the all night recordings was analyzed. Severe SAHS subjects show a shorter time interval between regular snores (p = 0.0036, AHI cp: 30 h⁻¹) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p = 0.006, AHI cp: 30 h⁻¹) is seen for less severe SAHS subjects. Features derived from the analysis of time interval between regular snores achieved classification accuracies of 88.2 % (with 90 % sensitivity, 75 % specificity) and 94.1 % (with 94.4 % sensitivity, 93.8 % specificity) for AHI cut-points of severity of 5 and 30 h⁻¹, respectively. The features proved to be reliable predictors of the subjects’ SAHS severity. Our proposed method, the analysis of time interval between snores, provides promising results and puts forward a valuable aid for the early screening of subjects suspected of having SAHS.

Cues to body size in the formant spacing of male koala (Phascolarctos cinereus) bellows: honesty in an exaggerated trait

Determining the information content of vocal signals and understanding morphological modifications of vocal anatomy are key steps towards revealing the selection pressures acting on a given species' vocal communication system. Here, we used a combination of acoustic and anatomical data to investigate whether male koala bellows provide reliable information on the caller's body size, and to confirm whether male koalas have a permanently descended larynx. Our results indicate that the spectral prominences of male koala bellows are formants (vocal tract resonances), and show that larger males have lower formant spacing. In contrast, no relationship between body size and the fundamental frequency was found. Anatomical investigations revealed that male koalas have a permanently descended larynx: the first example of this in a marsupial. Furthermore, we found a deeply anchored sternothyroid muscle that could allow male koalas to retract their larynx into the thorax. While this would explain the low formant spacing of the exhalation and initial inhalation phases of male bellows, further research will be required to reveal the anatomical basis for the formant spacing of the later inhalation phases, which is predictive of vocal tract lengths of around 50 cm (nearly the length of an adult koala's body). Taken together, these findings show that the formant spacing of male koala bellows has the potential to provide receivers with reliable information on the caller's body size, and reveal that vocal adaptations allowing callers to exaggerate (or maximise) the acoustic impression of their size have evolved independently in marsupials and placental mammals.

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.

Effects of acute and chronic sleep deprivation on daytime alertness and cognitive performance of healthy snorers and non-snorers

BACKGROUND

Respiratory events during sleep usually lead to micro arousals resulting in consecutive daytime sleepiness even in healthy snorers. The present study investigated the evolution of subjective and objective daytime sleepiness and reaction time in healthy snorers submitted to acute and chronic sleep deprivation.

METHODS

Objective sleepiness was measured by the MSLT, subjective sleepiness by the Karolinska Sleepiness Scale (KSS), and reaction time (RT) by the Psychomotor Vigilance Test. Mean sleep latencies, KSS scores and performance were analyzed through repeated measures ANOVAs with one between-factor (snorers and non-snorers) and two within-factors (sleep deprivation [baseline, acute, and chronic sleep deprivation] and time-of-day).

RESULTS

The findings reveal that sleep deprivation does not enhance snoring but that, during baseline, objective daytime sleepiness is higher in snorers than in non-snorers (shorter sleep latencies) with no difference in subjective assessments. The effects of acute and chronic sleep deprivation on sleep are similar in both groups, but, after acute sleep deprivation, RT and attentional lapses (RT >500 ms) are higher in snorers. Chronic sleep deprivation produces similar results in both groups.

CONCLUSION

These results suggest that respiratory efforts may be involved in the increased vulnerability to sleep deprivation of healthy snorers when compared to non-snorers.

Regular and non regular snore features as markers of SAHS

Sleep Apnea-Hypopnea Syndrome (SAHS) diagnosis is still done with an overnight multi-channel polysomnography. Several efforts are being made to study profoundly the snore mechanism and discover how it can provide an opportunity to diagnose the disease. This work introduces the concept of regular snores, defined as the ones produced in consecutive respiratory cycles, since they are produced in a regular way, without interruptions. We applied 2 thresholds (TH(adaptive) and TH(median)) to the time interval between successive snores of 34 subjects in order to select regular snores from the whole all-night snore sequence. Afterwards, we studied the effectiveness that parameters, such as time interval between successive snores and the mean intensity of snores, have on distinguishing between different levels of SAHS severity (AHI (Apnea-Hypopnea Index) < 5h(-1), AHI <10 h(-1), AHI < 15 h(-1), AHI < 30 h(-1)). Results showed that TH(adaptive) outperformed TH(median) on selecting regular snores. Moreover, the outcome achieved with non-regular snores intensity features suggests that these carry key information on SAHS severity.

Nocturnal sound analysis for the diagnosis of obstructive sleep apnea

A novel method for screening obstructive sleep apnea syndrome (OSAs) based on nocturnal acoustic signal is proposed. Full-night audio signals from sixty subjects were segmented into snore, noise and silence events using semi-automatic algorithm based on Gaussian mixture models which achieves more than 90% (92%) sensitivity (specificity) and produces an average of 2,000 snores per subject. A classification into 3 groups is proposed for the diagnosis: comparison group - non-OSA subjects (apnea hypopnea index, AHI < 10), mild to moderate OSA (10 < AHI < 30) and severe OSA (AHI>30). A Bayes classifier was implemented, fed with five acoustic features, all correlated with the severity of the syndrome: (1) Inter Event Silence, which quantifies segments suspicious as apnea; (2) Mel Cepstability, measures the entire night stability of the spectrum, expressed using mel-frequency cepstrum; (3) Energy Running Variance, a criterion for the variation of the nocturnal acoustic pattern; (4) Apneic Phase Ratio, exploiting the finding that snores around apnea events expressing larger acoustic variation; and (5) Pitch Density. Correct classification of 92% for resubstitution method and 80% for 5-fold cross validation method was achieved. Moreover, in a case of two groups with a threshold of AHI=10, a sensitivity (specificity) of 96.5% (90.6%) and 87.5% (82.1%) for resubstitution and cross-validation respectively were obtained.

Analysis of snoring sound by psychoacoustic parameters

The analysis of snoring sounds has been in focus for the past two decades. Conventional approaches by fast Fourier transformation face various limitations and demonstrate the necessity for alternative methods of investigation. Psychoacoustic analyses which are common for environmental noise analyses propose a potential approach. The present study investigates the psychoacoustic qualities (loudness, sharpness, roughness) of three different real snoring sounds (primary snoring, PS; Upper airway resistance syndrome, UARS; obstructive sleep apnea syndrome, OSAS) and their alterations under increasing, artificially created sound pressure levels (SPL) from 60-85 dB. PS and UARS were detected to obtain a greater loudness as well as a higher increase under increasing SPL than OSAS. The sharpness was higher in PS and UARS, remaining stable under rising SPL compared to OSAS. The intensities of roughness were at higher levels for PS compared to URAS and OSAS, with an increase of all snoring sounds under rising SPL. By merging the psychoacoustic qualities, an individual acoustic fingerprint can be created to differentiate the three types of snoring. A potential application is proposed for the analysis of snoring sounds during polysomnography as well as for an adequate evaluation of the annoyance by snoring sounds.