Craig Hukins

Silence-breathing-snore classification from snore-related sounds.

Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. Snoring is the earliest symptom of OSA, but its potential in clinical diagnosis is not fully recognized yet. The first task in the automatic analysis of snore-related sounds (SRS) is to segment the SRS data as accurately as possible into three main classes: snoring (voiced non-silence), breathing (unvoiced non-silence) and silence. SRS data are generally contaminated with background noise. In this paper, we present classification performance of a new segmentation algorithm based on pattern recognition. We considered four features derived from SRS to classify samples of SRS into three classes. The features--number of zero crossings, energy of the signal, normalized autocorrelation coefficient at 1 ms delay and the first predictor coefficient of linear predictive coding (LPC) analysis--in combination were able to achieve a classification accuracy of 90.74% in classifying a set of test data. We also investigated the performance of the algorithm when three commonly used noise reduction (NR) techniques in speech processing--amplitude spectral subtraction (ASS), power spectral subtraction (PSS) and short time spectral amplitude (STSA) estimation--are used for noise reduction. We found that noise reduction together with a proper choice of features could improve the classification accuracy to 96.78%, making the automated analysis a possibility.

Multi-feature snore sound analysis in obstructive sleep apnea-hypopnea syndrome.

Snoring is the most common symptom of obstructive sleep apnea hypopnea syndrome (OSAHS), which is a serious disease with high community prevalence. The standard method of OSAHS diagnosis, known as polysomnography (PSG), is expensive and time consuming. There is evidence suggesting that snore-related sounds (SRS) carry sufficient information to diagnose OSAHS. In this paper we present a technique for diagnosing OSAHS based solely on snore sound analysis. The method comprises a logistic regression model fed with snore parameters derived from its features such as the pitch and total airway response (TAR) estimated using a higher order statistics (HOS)-based algorithm. Pitch represents a time domain characteristic of the airway vibrations and the TAR represents the acoustical changes brought about by the collapsing upper airways. The performance of the proposed method was evaluated using the technique of K-fold cross validation, on a clinical database consisting of overnight snoring sounds of 41 subjects. The method achieved 89.3% sensitivity with 92.3% specificity (the area under the ROC curve was 0.96). These results establish the feasibility of developing a snore-based OSAHS community-screening device, which does not require any contact measurements.

Mixed-phase modeling in snore sound analysis

Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The gold standard of diagnosis, called Polysomnography (PSG), requires a full-night hospital stay connected to over 15 channels of measurements requiring physical contact with sensors. PSG is expensive and unsuited for community screening. Snoring is the earliest symptom of OSA, but its potential in OSA diagnosis is not fully recognized yet. In this paper, we propose a novel model for SRS as the response of a mixed-phase system (total airways response, TAR) to a source excitation at the input. The TAR/source model is similar to the vocal tract/source model in speech synthesis, and is capable of capturing acoustical changes brought about by the collapsing upper airways in OSA. We propose an algorithm based on higher-order-spectra (HOS) to jointly estimate the source and TAR, preserving the true phase characteristics of the latter. Working on a clinical database of signals, we show that TAR is indeed a mixed-phased signal and second-order statistics cannot fully characterize it. Night-time speech sounds can corrupt snore recordings and pose a challenge to snore based OSA diagnosis. We show that the TAR could be used to detect speech segments embedded in snores, and derive features to diagnose OSA via non-contact, low-cost instrumentation holding potential for a community screening device.

Normal probability testing of snore signals for diagnosis of obstructive sleep apnea

Obstructive Sleep Apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The standard method of OSA diagnosis is known as Polysomnography (PSG), which requires an overnight stay in a specifically equipped facility, connected to over 15 channels of measurements. PSG requires (i) contact instrumentation and, (ii) the expert human scoring of a vast amount of data based on subjective criteria. PSG is expensive, time consuming and is difficult to use in community screening or pediatric assessment. Snoring is the most common symptom of OSA. Despite the vast potential, however, it is not currently used in the clinical diagnosis of OSA. In this paper, we propose a novel method of snore signal analysis for the diagnosis of OSA. The method is based on a novel feature that quantifies the non-Gaussianity of individual episodes of snoring. The proposed method was evaluated using overnight clinical snore sound recordings of 86 subjects. The recordings were made concurrently with routine PSG, which was used to establish the ground truth via standard clinical diagnostic procedures. The results indicated that the developed method has a detectability accuracy of 97.34%.

Nonlinear Features for Single-Channel Diagnosis of Sleep-Disordered Breathing Diseases

Studies have shown that algorithms based on single-channel airflow records are effective in screening for sleep-disordered breathing diseases (SDB). In this study, we investigate the diagnostic effectiveness of a classifier trained on a set of features derived from single-channel airflow measurements. The features considered are based on recurrence quantification analysis (RQA) of the measurement time series and are optionally augmented with single measurements of neck circumference and body mass index. The airflow measurement utilized is the nasal pressure (NP). The study used an overnight recording from each of 77 patients undergoing PSG testing. Mixture discriminant analysis was used to obtain a classifier, which predicts whether or not a measurement segment contains an SDB event. Patients were diagnosed as having SDB disease if the recording contained measurement segments predicted to include an SDB event at a rate exceeding a threshold value. A patient can be diagnosed as having SDB disease if the rate of SDB events per hour of sleep, the respiratory disturbance index (RDI), is ≥15 or sometimes ≥5. Here we trained and evaluated the classifier under each assumption, obtaining areas under receiver operating curves using fivefold cross-validation of 0.96 and 0.93, respectively. We used a two-layer structure to select the optimal operating point and assess the resulting classifier to avoid unbiased estimates. The resulting estimates for diagnostic sensitivity/specificity were 71.5%/89.5% for disease classification when RDI ≥ 15 and 63.3%/100% for RDI ≥ 5. These results were found assuming that the costs of misclassifying healthy and diseased subjects are equal, but we provide a framework to vary these costs. The results suggest that a classifier based on RQA features derived from NP measurements could be used in an automated SDB screening device.

The 2012 AASM Respiratory Event Criteria Increase the Incidence of Hypopneas in an Adult Sleep Center Population.

STUDY OBJECTIVES To investigate the effect of the 2012 American Academy of Sleep Medicine (AASM) respiratory event criteria on severity and prevalence of obstructive sleep apnea (OSA) relative to previous respiratory event criteria. METHODS A retrospective, randomized comparison was conducted in an Australian clinical sleep laboratory in a tertiary hospital. The polysomnograms (PSG) of 112 consecutive patients undertaking polysomnography (PSG) for suspected OSA were re-scored for respiratory events using either 2007 AASM recommended (AASM2007Rec), 2007 AASM alternate (AASM2007Alt), Chicago criteria (AASM1999), or 2012 AASM recommended (AASM2012) respiratory event criteria. RESULTS The median AHI using AASM2012 was approximately 90% greater than the AASM2007Rec AHI, approximately 25% greater than the AASM2007Alt AHI, and approximately 15% lower than the AASM1999 AHI. These changes increased OSA diagnoses by approximately 20% and 5% for AASM2007Rec and AASM2007Alt, respectively. Minimal changes in OSA diagnoses were observed between AASM1999 and AASM2012 criteria. To achieve the same OSA prevalence as AASM2012, the threshold for previous criteria would have to shift to 2.6/h, 3.6/h, and 7.3/h for AASM2007Rec, AASM2007Alt, and AASM1999, respectively. Differences between the AASM2007Rec and AASM2012 hypopnea indices (HI) were predominantly due to the change in desaturation levels required. Alterations to respiratory event duration rules had no effect on the HI. CONCLUSIONS This study demonstrates that implementation of the 2012 AASM respiratory event criteria will increase the AHI in patients undergoing PSG, and more patients are likely to be diagnosed with OSA. COMMENTARY A commentary on this article appears in this issue on page 1357.

Obstructive and central sleep apnoea in Arnold–Chiari malformation: resolution following surgical decompression

Airways abnormalities as well as disorders of sleep and breathing have been reported in association with the Arnold–Chiari malformation (ACM). Both obstructive and central sleep apnoea have been reported in association with ACM [1–3]. We report the case of a young patient with type I Arnold–Chiari malformation who presented with a lung abscess and symptoms of obstructive sleep apnoea. After an initial limited polysomnography suggested obstructive sleep apnoea, she was commenced on nasal continuous positive airway pressure without improvement of her symptoms. Subsequent full polysomnography demonstrated central sleep apnoea. Her central apnoeas resolved following craniotomy and decompression of the craniocervical junction. Obstructive sleep apnoea is a highly prevalent disorder and the standard diagnostic technique of attended polysomnography is expensive. As such, the use of simplified diagnostic techniques is very appealing, including the use of limited channel devices or even proceeding directly to treatment in patients with highly suggestive clinical features [4, 5]. This case highlights the limitations of such simplified techniques in certain situations and emphasises the importance of correlating a thorough clinical evaluation to sleep study results. The case also highlights the need to consider other less common conditions which may result in secondary sleep apnoea in the presence of unusual clinical features or suboptimal clinical response to standard treatments. Identifying the primary cause in these circumstances may allow corrective therapy, which, as in this case, may be curative.

Interhemispheric Asynchrony Correlates With Severity of Respiratory Disturbance Index in Patients With Sleep Apnea

Obstructive sleep apnea (OSA) hypopnea syndrome is a disorder characterized by airway obstructions during sleep; full obstructions are known as apnea and partial obstructions are called hypopnea. Sleep in OSA patients is significantly disturbed with frequent apnea/hypopnea and arousal events. We illustrate that these events lead to functional asymmetry of the brain as manifested by the interhemispheric asynchrony (IHA) computed using EEG recorded on the scalp. In this paper, based on the higher order spectra of IHA time series, we propose a new index [interhemispheric synchrony index (IHSI)], for characterizing brain asynchrony in OSA. The IHSI computation does not depend on subjective criteria and can be completely automated. The proposed method was evaluated on overnight EEG data from a clinical database of 36 subjects referred to a hospital sleep laboratory. Our results indicated that the IHSI could classify the patients into OSA/non-OSA classes with an accuracy of 91% (ρ = 0.0001), at the respiratory disturbance index threshold of 10, suggesting that the brain asynchrony carries vital information on OSA.

Optimizing speech production in the ventilator‐assisted individual following cervical spinal cord injury: a preliminary investigation

BACKGROUND Mechanical ventilation is commonly used during the acute management of cervical spinal cord injury, and is required on an ongoing basis in the majority of patients with injuries at or above C3. However, to date there have been limited systematic investigations of the options available to improve speech while ventilator-assisted post-cervical spinal cord injury. AIMS To provide preliminary evidence of any benefits gained through the addition of positive end expiratory pressure (PEEP) and/or a tracheostomy speech valve to the condition of leak speech. METHODS & PROCEDURES Speech production in the three conditions was compared in two ventilator-assisted participants using a series of instrumental and perceptual speech measures. OUTCOMES & RESULTS The addition of PEEP or the use of a speech valve resulted in speech that was superior to leak speech for both participants; however, individual variation was present. CONCLUSIONS & IMPLICATIONS Leak speech alone or with the addition of PEEP or a tracheostomy speech valve can facilitate functional communication for the ventilated patient, though PEEP and valve speech were found to be superior in the current study. These findings will be of assistance for clinicians counselling the growing population of patients who may require tracheostomy positive pressure ventilation long-term regarding communication options.

A method to screen obstructive sleep apnea using multi-variable non-intrusive measurements

Obstructive sleep apnea (OSA) is a serious sleep disorder. The current standard OSA diagnosis method is polysomnography (PSG) testing. PSG requires an overnight hospital stay while physically connected to 10-15 channels of measurement. PSG is expensive, inconvenient and requires the extensive involvement of a sleep technologist. As such, it is not suitable for community screening. OSA is a widespread disease and more than 80% of sufferers remain undiagnosed. Simplified, unattended and cheap OSA screening methods are urgently needed. Snoring is commonly associated with OSA but is not fully utilized in clinical diagnosis. Snoring contains pseudo-periodic packets of energy that produce characteristic vibrating sounds familiar to humans. In this paper, we propose a multi-feature vector that represents pitch information, formant information, a measure of periodic structure existence in snore episodes and the neck circumference of the subject to characterize OSA condition. Snore features were estimated from snore signals recorded in a sleep laboratory. The multi-feature vector was applied to a neural network for OSA/non-OSA classification and K-fold cross-validated using a random sub-sampling technique. We also propose a simple method to remove a specific class of background interference. Our method resulted in a sensitivity of 91 ± 6% and a specificity of 89 ± 5% for test data for AHI(THRESHOLD) = 15 for a database consisting of 51 subjects. This method has the potential as a non-intrusive, unattended technique to screen OSA using snore sound as the primary signal.