Acoustic analysis of snoring before and after palatal surgery

SnoreNet: Detecting Snore Events from Raw Sound Recordings

Snoring is one of the earliest symptoms of Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS). Snore detection is the first step in developing non-invasive, low-cost, and totally sound-based OSAHS analysis approaches. In this work, we propose a simple yet effective deep neural network, named SnoreNet, for detecting snores from a continuous sound recording. Without manually crafted features, SnoreNet can capture the characteristics of snores. Since snore varies in temporal length, SnoreNet combines output from multiple feature maps to detect snore. In each feature map, SnoreNet uses a set of default bounding box generated by a base length and different scales to match snores. SnoreNet adjusts the box to better locate snores and predicts a score for the presence of snore in each default bounding box. The performance of SnoreNet was evaluated on a newly collected snore pattern classes dataset, which achieves 81.82% average precision (AP).

Recurrent Neural Network for Classification of Snoring and Non-Snoring Sound Events

Obstructive sleep apnea (OSA) is a disorder that affects up to 38% of the western population. It is characterized by repetitive episodes of partial or complete collapse of the upper airway during sleep. These episodes are almost always accompanied by loud snoring. Questionnaires such as STOP-BANG exploit snoring to screen for OSA. However, they are not quantitative and thus do not exploit its full potential. A method for automatic detection of snoring in whole-night recordings is required to enable its quantitative evaluation. In this study, we propose such a method. The centerpiece of the proposed method is a recurrent neural network for modeling of sequential data with variable length. Mel-frequency cepstral coefficients, which were extracted from snoring and non-snoring sound events, were used as inputs to the proposed network. A total of 20 subjects referred to clinical sleep recording were also recorded by a microphone that was placed 70 cm from the top end of the bed. These recordings were used to assess the performance of the proposed method. When it comes to the detection of snoring events, our results show that the proposed method has an accuracy of 95%, sensitivity of 92%, and specificity of 98%. In conclusion, our results suggest that the proposed method may improve the process of snoring detection and with that the process of OSA screening. Follow-up clinical studies are required to confirm this potential.

Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification

Background

Polysomnography (PSG) is the gold standard test for obstructive sleep apnea (OSA), but it incurs high costs, requires inconvenient measurements, and is limited by a one-night test. Thus, a repetitive OSA screening test using affordable data would be effective both for patients interested in their own OSA risk and in-hospital PSG. The purpose of this research was to develop a four-OSA severity classification model using a patient’s breathing sounds.

Methods

Breathing sounds were recorded from 83 subjects during a PSG test. There was no exclusive experimental protocol or additional recording instruments use throughout the sound recording procedure. Based on the Apnea-Hypopnea Index (AHI), which indicates the severity of sleep apnea, the subjects’ sound data were divided into four-OSA severity classes. From the individual sound data, we proposed two novel methods which were not attempted in previous OSA severity classification studies. First, the total transition probability of approximated sound energy in time series, and second, the statistical properties derived from the dimension-reduced cyclic spectral density. In addition, feature selection was conducted to achieve better results with a more relevant subset of features. Then, the classification model was trained using support vector machines and evaluated using leave-one-out cross-validation.

Results

The overall results show that our classification model is better than existing multiple OSA severity classification method using breathing sounds. The proposed method demonstrated 79.52% accuracy for the four-class classification task. Additionally, it demonstrated 98.0% sensitivity, 75.0% specificity, and 92.78% accuracy for OSA subject detection classification with AHI threshold 5.

Conclusions

The results show that our proposed method can be used as part of an OSA screening test, which can provide the subject with detailed OSA severity results from only breathing sounds.

Acoustic parameters of snoring sound to assess the effectiveness of sleep nasendoscopy in predicting surgical outcome

Objective

To assess the effectiveness of two grading systems used to predict surgical outcome in nonapneic snorers.

Study Design

A prospective observational study. Prior to undergoing palatal surgery, 20 patients completed a sleep nasendoscopic examination involving sequential steady-state sedation with intravenous propofol. Using a combination of acoustic parameters of snoring sound as an objective outcome measurement, and the answers to a specifically designed questionnaire as a subjective outcome measurement, the effectiveness of each grading system in predicting surgical outcome was examined.

Results

Depending on the outcome measurement used, sensitivity in predicting success of surgery for snoring varied from 16.7% to 50.0% and specificity from 38.5% to 62.5% for the Pringle and Croft system, while sensitivity varied from 91.7% to 100% and specificity from 30.8% to 31.5% for the Camilleri system.

Conclusion

Sleep nasendoscopy using these classifications cannot be recommended as a reliable predictor of surgical outcome in nonapneic snorers. EBM rating: C-4

Comparison of Standard and Novel Signal Analysis Approaches to Obstructive Sleep Apnea Classification

Obstructive sleep apnea (OSA) is a disorder characterized by repeated pauses in breathing during sleep, which leads to deoxygenation and voiced chokes at the end of each episode. OSA is associated by daytime sleepiness and an increased risk of serious conditions such as cardiovascular disease, diabetes, and stroke. Between 2 and 7% of the adult population globally has OSA, but it is estimated that up to 90% of those are undiagnosed and untreated. Diagnosis of OSA requires expensive and cumbersome screening. Audio offers a potential non-contact alternative, particularly with the ubiquity of excellent signal processing on every phone. Previous studies have focused on the classification of snoring and apneic chokes. However, such approaches require accurate identification of events. This leads to limited accuracy and small study populations. In this work, we propose an alternative approach which uses multiscale entropy (MSE) coefficients presented to a classifier to identify disorder in vocal patterns indicative of sleep apnea. A database of 858 patients was used, the largest reported in this domain. Apneic choke, snore, and noise events encoded with speech analysis features were input into a linear classifier. Coefficients of MSE derived from the first 4 h of each recording were used to train and test a random forest to classify patients as apneic or not. Standard speech analysis approaches for event classification achieved an out-of-sample accuracy (Ac) of 76.9% with a sensitivity (Se) of 29.2% and a specificity (Sp) of 88.7% but high variance. For OSA severity classification, MSE provided an out-of-sample Ac of 79.9%, Se of 66.0%, and Sp = 88.8%. Including demographic information improved the MSE-based classification performance to Ac = 80.5%, Se = 69.2%, and Sp = 87.9%. These results indicate that audio recordings could be used in screening for OSA, but are generally under-sensitive.

[Sk2 guidelines"diagnosis and therapy of snoring in adults" : compiled by the sleep medicine working group of the German Society of Otorhinolaryngology, Head and Neck Surgery]

These guidelines aim to facilitate high quality medical care of adults with snoring problems. The guidelines were devised for application in both in- and outpatient environments and are directed primarily at all those concerned with the diagnosis and therapy of snoring. According to the AWMF three-level concept, these represent S2k guidelines.A satisfactory definition of snoring does not currently exist. Snoring is the result of vibration of soft tissue structures in narrow regions of the upper airway during breathing while asleep. Ultimately, these vibrations are caused by the sleep-associated decrease in muscle tone in the area of the upper airway dilator muscles. A multitude of risk factors for snoring have been described and its occurrence is multifactorial. Data relating to the frequency of snoring vary widely, depending on the way in which the data are collected. Snoring is usually observed in middle-aged individuals and affected males predominate. Clinical diagnosis of snoring should comprise a free evaluation of the patient's medical history. Where possible this should also involve their bed partner and the case history can be complimented by questionnaires. To determine the airflow relevant structures, a clinical examination of the nose should be performed. This examination may also include nasal endoscopy. Examination of the oropharynx is particularly important and should be performed. The larynx and the hypopharynx should be examined. The size of the tongue and the condition of the mucous membranes should be recorded as part of the oral cavity examination, as should the results of a dental assessment. Facial skeleton morphology should be assessed for orientation purposes. Technical examinations may be advisable in individual cases. In the instance of suspected sleep-related breathing disorders, relevant comorbidities or where treatment for snoring has been requested, an objective sleep medicine examination should be performed. Snoring is not-at least as we currently understand it-a disease associated with a medical threat; therefore there is currently no medical necessity to treat the condition. All overweight patients with snoring problems should strive to lose weight. If snoring is associated with the supine position, positional therapy can be considered. Some cases of snoring can be appropriately treated using an intraoral device. Selected minimally invasive surgical procedures on the soft palate can be recommended to treat snoring, provided that examinations have revealed a suitable anatomy. The choice of technique is determined primarily by the individual anatomy. At an appropriate interval after the commencement or completion a therapeutic measure, a follow-up examination should be conducted to assess the success of the therapy and to aid in the planning of any further treatments.

Diagnosis and treatment of snoring in adults-S2k Guideline of the German Society of Otorhinolaryngology, Head and Neck Surgery

OBJECTIVES

This guideline aims to promote high-quality care by medical specialists for subjects who snore and is designed for everyone involved in the diagnosis and treatment of snoring in an in- or outpatient setting.

DISCUSSION

To date, a satisfactory definition of snoring is lacking. Snoring is caused by a vibration of soft tissue in the upper airway induced by respiration during sleep. It is triggered by relaxation of the upper airway dilator muscles that occurs during sleep. Multiple risk factors for snoring have been described and snoring is of multifactorial origin. The true incidence of snoring is not clear to date, as the incidence differs throughout literature. Snoring is more likely to appear in middle age, predominantly in males. Diagnostic measures should include a sleep medical history, preferably involving an interview with the bed partner, and may be completed with questionnaires. Clinical examination should include examination of the nose to evaluate the relevant structures for nasal breathing and may be completed with nasal endoscopy. Evaluation of the oropharynx, larynx, and hypopharynx should also be performed. Clinical assessment of the oral cavity should include the size of the tongue, the mucosa of the oral cavity, and the dental status. Furthermore, facial skeletal morphology should be evaluated. In select cases, technical diagnostic measures may be added. Further objective measures should be performed if the medical history and/or clinical examination suggest sleep-disordered breathing, if relevant comorbidities are present, and if the subject requests treatment for snoring. According to current knowledge, snoring is not associated with medical hazard, and generally, there is no medical indication for treatment. Weight reduction should be achieved in every overweight subject who snores. In snorers who snore only in the supine position, positional treatment can be considered. In suitable cases, snoring can be treated successfully with intraoral devices. Minimally invasive surgery of the soft palate can be considered as long as the individual anatomy appears suitable. Treatment selection should be based on individual anatomic findings. After a therapeutic intervention, follow-up visits should take place after an appropriate time frame to assess treatment success and to potentially indicate further intervention.

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.

Do anthropometric parameters change the characteristics of snoring sound?

Snoring sounds is commonly known to be associated with obstructive sleep apnea (OSA). There are many studies trying to distinguish between the snoring sounds of non-OSA and those of OSA patients. However, OSA is only one of the conditions that affect the structure of upper airway. In this study, we investigated the effect of anthropometric parameters on the snoring sounds. Since snoring sounds are non-Gaussian signals by nature, we derived its Higher Order Statistical (HOS) features and investigated the statistical significance of the anthropometric parameters on each of these features. Data were collected from 40 patients with different levels of OSA. Tracheal respiratory sounds collected by a microphone placed over suprasternal notch, were recorded simultaneously with full-night Polysomnography (PSG) data during sleep. The snoring segments were identified semi-automatically from respiratory sounds using an unsupervised snore detection algorithm. The bispectrum of each SS segment was estimated. We calculated two common HOS measures, Skewness and Kurtosis, plus a new feature called Projected Median Bifrequency (PMBF) from the SS segments. Then, we investigated the statistical relationship between these features and anthropometric parameters such as height, Body Mass Index (BMI), age, gender, and Apnea-Hypopnea Index (AHI). The result showed that gender, BMI, height, and AHI are the parameters that do change the characteristics of snoring sounds significantly.

Automatic and unsupervised snore sound extraction from respiratory sound signals

In this paper, an automatic and unsupervised snore detection algorithm is proposed. The respiratory sound signals of 30 patients with different levels of airway obstruction were recorded by two microphones: one placed over the trachea (the tracheal microphone), and the other was a freestanding microphone (the ambient microphone). All the recordings were done simultaneously with full-night polysomnography during sleep. The sound activity episodes were identified using the vertical box (V-Box) algorithm. The 500-Hz subband energy distribution and principal component analysis were used to extract discriminative features from sound episodes. An unsupervised fuzzy C-means clustering algorithm was then deployed to label the sound episodes as either snore or no-snore class, which could be breath sound, swallowing sound, or any other noise. The algorithm was evaluated using manual annotation of the sound signals. The overall accuracy of the proposed algorithm was found to be 98.6% for tracheal sounds recordings, and 93.1% for the sounds recorded by the ambient microphone.

Unsupervised classification of respiratory sound signal into snore/no-snore classes

In this study, an automatic and online snore detection algorithm is proposed. The respiratory sound signals were recorded simultaneously with Polysomnography (PSG) data during sleep from 20 patients (10 simple snorers and 10 OSA patients). The sound signals were recorded by two tracheal and ambient microphones. The potential snoring episodes were identified using Vertical Box (V-Box) algorithm. The normalized 500Hz sub-band energy features of each episode were calculated. Principal component analysis (PCA) was applied to a 10-dimensional feature space to reduce it to a new 2-dimensional feature space. An unsupervised K-means clustering algorithm was then deployed to label the sound episodes as either snore or no-snore class. The performance of the algorithm was evaluated using manual annotation of the sound signals. The overall accuracy of the system was found to be 98.2% for the tracheal recordings and 95.5% for the sounds recorded by the ambient microphone.

The acoustics of snoring

Snoring is a prevalent disorder affecting 20-40% of the general population. The mechanism of snoring is vibration of anatomical structures in the pharyngeal airway. Flutter of the soft palate accounts for the harsh aspect of the snoring sound. Natural or drug-induced sleep is required for its appearance. Snoring is subject to many influences such as body position, sleep stage, route of breathing and the presence or absence of sleep-disordered breathing. Its presentation may be variable within or between nights. While snoring is generally perceived as a social nuisance, rating of its noisiness is subjective and, therefore, inconsistent. Objective assessment of snoring is important to evaluate the effect of treatment interventions. Moreover, snoring carries information relating to the site and degree of obstruction of the upper airway. If evidence for monolevel snoring at the site of the soft palate is provided, the patient may benefit from palatal surgery. These considerations have inspired researchers to scrutinize the acoustic characteristics of snoring events. Similarly to speech, snoring is produced in the vocal tract. Because of this analogy, existing techniques for speech analysis have been applied to evaluate snoring sounds. It appears that the pitch of the snoring sound is in the low-frequency range (<500 Hz) and corresponds to a fundamental frequency with associated harmonics. The pitch of snoring is determined by vibration of the soft palate, while nonpalatal snoring is more 'noise-like', and has scattered energy content in the higher spectral sub-bands (>500 Hz). To evaluate acoustic properties of snoring, sleep nasendoscopy is often performed. Recent evidence suggests that the acoustic quality of snoring is markedly different in drug-induced sleep as compared with natural sleep. Most often, palatal surgery alters sound characteristics of snoring, but is no cure for this disorder. It is uncertain whether the perceived improvement after palatal surgery, as judged by the bed partner, is due to an altered sound spectrum. Whether some acoustic aspects of snoring, such as changes in pitch, have predictive value for the presence of obstructive sleep apnea is at present not sufficiently substantiated.

Investigation of sequential properties of snoring episodes for obstructive sleep apnoea identification

In this paper, 'snore regularity' is studied in terms of the variations of snoring sound episode durations, separations and average powers in simple snorers and in obstructive sleep apnoea (OSA) patients. The goal was to explore the possibility of distinguishing among simple snorers and OSA patients using only sleep sound recordings of individuals and to ultimately eliminate the need for spending a whole night in the clinic for polysomnographic recording. Sequences that contain snoring episode durations (SED), snoring episode separations (SES) and average snoring episode powers (SEP) were constructed from snoring sound recordings of 30 individuals (18 simple snorers and 12 OSA patients) who were also under polysomnographic recording in Gülhane Military Medical Academy Sleep Studies Laboratory (GMMA-SSL), Ankara, Turkey. Snore regularity is quantified in terms of mean, standard deviation and coefficient of variation values for the SED, SES and SEP sequences. In all three of these sequences, OSA patients' data displayed a higher variation than those of simple snorers. To exclude the effects of slow variations in the base-line of these sequences, new sequences that contain the coefficient of variation of the sample values in a 'short' signal frame, i.e., short time coefficient of variation (STCV) sequences, were defined. The mean, the standard deviation and the coefficient of variation values calculated from the STCV sequences displayed a stronger potential to distinguish among simple snorers and OSA patients than those obtained from the SED, SES and SEP sequences themselves. Spider charts were used to jointly visualize the three parameters, i.e., the mean, the standard deviation and the coefficient of variation values of the SED, SES and SEP sequences, and the corresponding STCV sequences as two-dimensional plots. Our observations showed that the statistical parameters obtained from the SED and SES sequences, and the corresponding STCV sequences, possessed a strong potential to distinguish among simple snorers and OSA patients, both marginally, i.e., when the parameters are examined individually, and jointly. The parameters obtained from the SEP sequences and the corresponding STCV sequences, on the other hand, did not have a strong discrimination capability. However, the joint behaviour of these parameters showed some potential to distinguish among simple snorers and OSA patients.

Single dose injection snoreplasty: investigation or treatment?

INTRODUCTION

Many surgical and nonsurgical procedures have been designed for the treatment of snoring due to palatal flutter. All work in some, but not all, snorers. The difficulty lies in making the definitive diagnosis of palatal flutter as the cause of snoring, and in deciding which patients should undergo which treatment, which in some cases are relatively radical.

AIMS

This study aimed to assess the usefulness of injection snoreplasty in differentiating palatal flutter from other forms of snoring. This was done in the hope of determining which patients would benefit from definitive palatal surgery such as uvulopalatopharyngoplasty and laser-assisted uvuloplasty.

MATERIALS

Sixty consecutive patients referred for habitual snoring were treated with sodium tetradycil sulphate during their first consultation visit. No patients were excluded and none refused the treatment. Forty patients received a single 1 ml dose of 1 per cent sodium tetradycil sulphate, and twenty patients received a single 1 ml dose of 3 per cent sodium tetradycil sulphate under topical anaesthesia. Visual analogue snoring scales were completed by the patient and their partner six weeks, three months, six months and 12 months after the procedure.

RESULTS

Forty of the 60 patients showed improvement in snoring and therefore were considered for definitive surgery. Four of the 60 patients found the investigation unpleasant and did not want any further treatment. Of the 40 patients who showed improvement, 29 maintained this at one year. The other 11 underwent uvulopalatopharyngoplasty or laser-assisted palatoplasty. All patients had successful snoring scale outcomes following the surgery.

CONCLUSION

A significant number of the patients, 62 per cent, were demonstrated to have significant improvement in the short term. Single dose injection snoreplasty seems not only to be an effective investigation but may constitute a safe and simple treatment within the clinic. At the very least, patients in whom the palate appears not to be the problem are prevented from undergoing painful, unpleasant surgery. Our results support the use of injection snoreplasty, both as an investigation and in some patients as a treatment, for habitual snoring.

An efficient method for snore/nonsnore classification of sleep sounds

A new method to detect snoring episodes in sleep sound recordings is proposed. Sleep sound segments (i.e., 'sound episodes' or simply 'episodes') are classified as snores and nonsnores according to their subband energy distributions. The similarity of inter- and intra-individual spectral energy distributions motivated the representation of the feature vectors in a lower dimensional space. Episodes have been efficiently represented in two dimensions using principal component analysis, and classified as snores or nonsnores. The sound recordings were obtained from individuals who are suspected of OSAS pathology while they were connected to the polysomnography in Gülhane Military Medical Academy Sleep Studies Laboratory (GMMA-SSL), Ankara, Turkey. The data from 30 subjects (18 simple snorers and 12 OSA patients) with different apnoea/hypopnea indices were classified using the proposed algorithm. The system was tested by using the manual annotations of an ENT specialist as a reference. The accuracy for simple snorers was found to be 97.3% when the system was trained using only simple snorers' data. It drops to 90.2% when the training data contain both simple snorers' and OSA patients' data. (Both of these results were obtained by using training and testing sets of different individuals.) In the case of snore episode detection with OSA patients the accuracy is 86.8%. All these results can be considered as acceptable values to use the system for clinical purposes including the diagnosis and treatment of OSAS. The method proposed here has been used to develop a tool for the ENT clinic of GMMA-SSL that provides information for objective evaluation of sleep sounds.

Acoustic parameters of snoring sound to assess the effectiveness of the Müller Manoeuvre in predicting surgical outcome

OBJECTIVE

To assess the effectiveness of the Müller Manoeuvre in predicting surgical outcome in non-apnoeic snorers.

METHODS

Forty-one non-apnoeic snorers performed the Müller Manoeuvre, prior to palatal surgery for snoring. Pre-operatively and between 1.0 and 4.1 months (mean 2.5 months) post-operatively, patients were admitted overnight when their sleeping position and snoring sounds were recorded. At the time of the post-operative recordings, patients were required to complete a specifically designed questionnaire. Snore files comprising the inspiratory component of the first 100 snores whilst the patient was supine, were extracted. Snore duration (s), snore loudness (dBA), snore periodicity (%) and the energy ratios for the frequency bands 0-200, 0-250 and 0-400 Hz were calculated. Only patients who showed improvements in snore periodicity and all energy ratios were considered to be surgical successes. In addition, patients were also categorised as 'successes' or 'failures' depending on their responses to specific questionnaire questions. The effectiveness of the Müller Manoeuvre in predicting surgical outcome was then tested using these categories.

RESULTS

The 41 patients included 35 men and 6 women. Mean age: 47 years (24-67 years). Mean PNIFR 145 (80-230). Median reported alcohol intake was 11-15 units/week (0 to 26-30 units/week). Mean BMI: 30.6 kg/m2 (24.3-47.2 kg/m2). Twenty-four patients underwent an uvulopalatal elevation palatoplasty and seventeen a traditional palatoplasty. Following the Müller Manoeuvre, patients were categorised as 'ideal', 'suboptimal, but acceptable' or 'unsuitable' for surgery. Using the acoustic parameters, 23/41 patients were considered a surgical success, whilst 18/41 were considered failures. Using the questionnaire responses, 14/40 patients were considered a surgical success, whilst 26/40 were considered failures. There was no correlation between the subjective and objective outcomes (rho=0.193; p=0.227). Neither pre-operative BMI, type of palatoplasty performed, patient gender, age, PNIFR or reported alcohol intake were confounders of surgical outcome. For patients considered 'ideal' and 'suboptimal, but acceptable', using acoustic outcomes, the Müller Manoeuvre had a specificity of 55.5% and a sensitivity of 30.4%, compared with a sensitivity of 57.7% and a specificity of 28.6% when questionnaire responses were used. If only patients considered 'ideal' were considered, the specificity was 66.7%, and the sensitivity 21.7% when using acoustic outcomes, compared with a sensitivity of 69.2% and a specificity of 78.6% when questionnaire responses were used.

CONCLUSION

The Müller Manoeuvre appears to have no role in the pre-operative assessment of palatal surgery for non-apnoeic snorers.

Acoustic parameters of snoring sound to compare natural snores with snores during 'steady-state' propofol sedation

OBJECTIVES

To investigate the acoustic similarity between natural and sedation-induced snores.

DESIGN

Prospective observational study.

SETTING

University Hospital Aintree, Liverpool, UK.

PARTICIPANTS

Twenty-one patients, who had already had overnight snore recordings, completed a pre-operative sleep nasendoscopic examination. Endoscopic examination of the upper aero-digestive tract was performed at sequentially increasing, steady-state sedation levels, using intravenous propofol administered according to a weight/time-based algorithm to predict blood and effect site (tissue) concentrations. At each sedation level at which snoring occurred, snoring sound was recorded. From these samples, snore files, comprising the inspiratory sound of each snore were created. Similarly, from natural snores recorded pre-operatively, snore files, comprising the inspiratory sounds of the first 100 snores with the patient sleeping in a supine position, were also created.

MAIN OUTCOME MEASURES

Snore duration (s), loudness (dBA), periodicity (%) and energy ratios for the frequency sub-bands 0-200, 0-250 and 0-400 Hz.

RESULTS

Snore loudness increased significantly (P < 0.0001), whilst energy ratios for frequency bands 0-200, 0-250 and 0-400 Hz all decreased significantly as sedation level increased (P < 0.001). A significant difference between natural snoring and snoring induced at the lowest sedation level was shown (P < 0.0001). Endoscopic examination was not tolerated at this sedation level.

CONCLUSIONS

The acoustic characteristics of sedation-induced and natural snores are sufficiently different to recommend the need for further research to determine whether the technique of sleep nasendoscopy is, in fact, a valid predictor of outcome of snoring surgery.