Mapping Sleep Spindle Characteristics to Vigilance Outcomes in Patients with Obstructive Sleep Apnea.

Obstructive Sleep Apnea (OSA) is a sleep disorder associated with reduced vigilance. Vigilance status is often measured using the Psychomotor Vigilance Task (PVT). This paper investigates modelling strategies to map sleep spindle (Sp) characteristics to PVT metrics in patients with OSA. Sleep spindles (n=2305) were manually detected across blocks of sleep for 20 patients randomly selected from a cohort of 190 undergoing Polysomnography (PSG) for suspected OSA. Novel Sp metrics based on runs or "bursts" of Sps were used to model Sp characteristics to standardized (z) Lapse and Median Reaction Time (MdRT) scores, and to Groups based on zLapse and zMdRT scores. A model employing Sp Burst characteristics mapped to MdRT Group membership with an accuracy of 91.9%, (95% C.I. 90.8-93.0). The model had a sensitivity of 88.9%, (95% C.I. 87.5-89.0) and specificity of 89.1% (95% C.I. 87.3-90.5) for detecting patients with the lowest MdRTs in our cohort.Clinical Relevance- Based on these results it may be possible to use Sp data collected during overnight diagnostic PSG for OSA to detect patients at risk for attention deficits. This would improve triage for OSA therapy by identifying at risk patients at the time of OSA diagnosis and would remove the need to employ additional testing to assess vigilance status.

Obstructive sleep apnea screening by integrating snore feature classes.

Obstructive sleep apnea (OSA) is a serious sleep disorder with high community prevalence. More than 80% of OSA suffers remain undiagnosed. Polysomnography (PSG) is the current reference standard used for OSA diagnosis. It is expensive, inconvenient and demands the extensive involvement of a sleep technologist. At present, a low cost, unattended, convenient OSA screening technique is an urgent requirement. Snoring is always almost associated with OSA and is one of the earliest nocturnal symptoms. With the onset of sleep, the upper airway undergoes both functional and structural changes, leading to spatially and temporally distributed sites conducive to snore sound (SS) generation. The goal of this paper is to investigate the possibility of developing a snore based multi-feature class OSA screening tool by integrating snore features that capture functional, structural, and spatio-temporal dependences of SS. In this paper, we focused our attention to the features in voiced parts of a snore, where quasi-repetitive packets of energy are visible. Individual snore feature classes were then optimized using logistic regression for optimum OSA diagnostic performance. Consequently, all feature classes were integrated and optimized to obtain optimum OSA classification sensitivity and specificity. We also augmented snore features with neck circumference, which is a one-time measurement readily available at no extra cost. The performance of the proposed method was evaluated using snore recordings from 86 subjects (51 males and 35 females). Data from each subject consisted of 6-8 h long sound recordings, made concurrently with routine PSG in a clinical sleep laboratory. Clinical diagnosis supported by standard PSG was used as the reference diagnosis to compare our results against. Our proposed techniques resulted in a sensitivity of 93±9% with specificity 93±9% for females and sensitivity of 92±6% with specificity 93±7% for males at an AHI decision threshold of 15 events/h. These results indicate that our method holds the potential as a tool for population screening of OSA in an unattended environment.

Impact of gender on snore-based obstructive sleep apnea screening.

Obstructive sleep apnea syndrome (OSA) is a serious widespread disease in which upper airways (UA) are collapsed during sleep. OSA has marked male predominance in prevalence. Although women are less vulnerable to OSA, under-diagnosed OSA in women may associate with serious consequences. Snoring is commonly associated with OSA and one of the earliest symptoms. Snore sounds (SS) are generated due to vibration of the collapsing soft tissues of the UA. Structural and functional properties of the UA are gender dependent. SS capture these time varying gender attributed UA properties and those could be embedded in the acoustic properties of SS. In this paper, we investigate the gender-specific acoustic property differences of SS and try to exploit these differences to enhance the snore-based OSA detection performance. We developed a snore-based multi-feature vector for OSA screening and one time-measured neck circumference was augmented. Snore features were estimated from SS recorded in a sleep laboratory from 35 females and 51 males and multi-layer neural network-based pattern recognition algorithms were used for OSA/non-OSA classification. The results were K-fold cross-validated. Gender-dependent modeling resulted in an increase of around 7% in sensitivity and 6% in specificity at the decision threshold AHI = 15 against a gender-neutral model. These results established the importance of adopting gender-specific models for the snore-based OSA screening technique.

Gender dependant snore sound based multi feature obstructive sleep apnea screening method.

Obstructive Sleep Apnea (OSA) is a serious sleep disorder that occurs due to collapsing upper airways (UA). More than 80% of OSA sufferers remain undiagnosed and the situation demands simplified, convenient technology for community screening. Almost all OSA patients snore and snoring is the earliest nocturnal symptom of OSA. Snore signals (SS) are produced due to vibration of soft tissues in the narrowed parts of the UA. It is known that the UA properties are gender specific. In this paper, we work under the hypothesis that gender specific analysis of snore sounds should lead to a higher OSA detection performance. We propose a snore based multi-parametric OSA screening technique, which incorporates the gender differences in the algorithm. The multi feature vector was modeled using logistic regression based algorithms to classify subjects into OSA/non-OSA classes. The performance of the proposed method was evaluated by carrying out K-fold cross validation. This procedure was applied to male (n=51) and female (n=36) data sets recorded in a clinical sleep laboratory. Each data set consisted of sound recordings of 6-8 hr. duration. The performance of the method was evaluated against the standard laboratory method of diagnosis known as polysomongraphy. Our gender-specific technique resulted in a sensitivity of 93±9% with specificity 89±7% for females and sensitivity of 91±8% with specificity 89±12% for males. These results establish the possibility of developing cheap, convenient, non-contact and an unattended OSA screening technique.

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.

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%.

Modelling of polysomnographic respiratory measurements for artefact detection and signal restoration.

Polysomnography (PSG), which incorporates measures of sleep with measures of EEG arousal, air flow, respiratory movement and oxygenation, is universally regarded as the reference standard in diagnosing sleep-related respiratory diseases such as obstructive sleep apnoea syndrome. Over 15 channels of physiological signals are measured from a subject undergoing a typical overnight PSG session. The signals often suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artefact-corrupted signal segments are visually detected and removed from further consideration. This is a highly time-consuming process, and subjective judgement is required for the job. During typical sleep scoring sessions, the target is the detection of segments of diagnostic interest, and signal restoration is not utilized for distorted segments. In this paper, we propose a novel framework for artefact detection and signal restoration based on the redundancy among respiratory flow signals. We focus on the air flow (thermistor sensors) and nasal pressure signals which are clinically significant in detecting respiratory disturbances. The method treats the respiratory system and other organs that provide respiratory-related inputs/outputs to it (e.g., cardiovascular, brain) as a possibly nonlinear coupled-dynamical system, and uses the celebrated Takens embedding theorem as the theoretical basis for signal prediction. Nonlinear prediction across time (self-prediction) and signals (cross-prediction) provides us with a mechanism to detect artefacts as unexplained deviations. In addition to detection, the proposed method carries the potential to correct certain classes of artefacts and restore the signal. In this study, we categorize commonly occurring artefacts and distortions in air flow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. The results we obtained from a database of clinical PSG signals indicated that the proposed technique can detect artefacts/distortions with a sensitivity>88.3% and specificity>92.4%. This work has the potential to simplify the work done by sleep scoring technicians, and also to improve automated sleep scoring methods.

Sleep-stage and event dependency of brain asynchrony as manifested through surface EEG.

Electroencephalography (EEG) is a core measurement in overnight sleep studies. In this paper we study functional asymmetries of the brain as manifested through spectral correlation coefficient. Our target group is patients symptomatic of sleep apnea and referred for routine Polysomnography (PSG) testing at the hospital. We measured EEG data (using electrodes C4/A1 and C3/A2 of the International 10/20 System) as a part of the routine PSG test. Spectral correlation coefficients were computed between EEG data from the two hemispheres, for each frequency band of interest: delta, theta, alpha, and beta. Our results indicated that hemispheric correlation distinctly changes with the gross sleep type (REM/NREM) as well as with different sleep stages (stages 1-4) within NREM. It also varies in the presence of arousal events and apnea. These results may provide a basis for novel insights into the functional asymmetries of brain in sleep and sleep associated events such as arousals and apnea.

Inter-hemispheric asynchrony of the brain during events of apnoea and EEG arousals.

Sleep apnoea hypopnea syndrome (SAHS) is a serious sleep disorder affecting a large percentage of the population. Apnoea/hypopnea and electroencephalographic-arousal (EEGA) events occur frequently in SAHS patients. These events significantly disturb the sleep architecture, as revealed through nocturnal EEG signals. Even though EEG carries vital information on the state of the brain, its use in clinical SAHS diagnosis is limited mainly to routine sleep staging. In this paper, we address this issue. We propose a novel measure, called the inter-hemispheric asynchrony (Psi(a-->b)), to capture EEG-symmetry changes associated with a transition a --> b between the brain states 'a' and 'b'. Our work takes into account macro-states such as the traditional sleep stages, and micro-states such as EEGA and apnoea/hypopnea events. We measured EEG data using electrodes C4-A2 and C3-A1 of the International 10/20 System from 18 subjects undergoing polysomnography (PSG) testing. These electrode pairs are symmetrical about the brain mid-line and allow us to discern any hemispheric EEG asymmetry. EEG data were segmented and filtered into classical bands delta(0.5-4 Hz), theta(4.1-8 Hz), alpha(8.1-12 Hz) and beta(12.1-16 Hz). Then they were further categorized according to the particular sleep state of their origin. Spectral correlation coefficients were computed between the EEG data from the two hemispheres and averaged over the overnight EEG recording. This was done for each frequency band and state of interest, and then the measure Psi(a-->b) was computed. Results from the 18 subjects showed that Psi(a-->b) increased significantly (p < 0.05) when the sleep state changed from NREM to REM, in all the frequency bands considered. Similarly, within both NREM and REM macro-states, Psi(a-->b) changes significantly (p < 0.1) with micro-state changes from the background state towards apnoea/hypopnea and EEGA states. Extensive statistical analysis we conducted with the 18 subjects indicated that the measure Psi(a-->b) provides a novel insight into the functional asymmetry of the brain during SAHS events.

Radiofrequency tissue volume reduction of the soft palate in simple snoring.

BACKGROUND: Snoring is common and often associated with social morbidity. Current therapies are generally unsatisfactory, but radiofrequency tissue volume reduction (RFTVR) palatoplasty offers a new approach. OBJECTIVE: To assess the outcomes and morbidity associated with RFTVR palatoplasty. DESIGN: Open, prospective trial. SETTING: Tertiary referral center. PATIENTS: 20 adults with loud habitual snoring without clinically significant obstructive sleep apnea. INTERVENTIONS: Three treatments with RFTVR to the middle, distal, and proximal thirds of the midline of the soft palate. MAIN OUTCOME MEASURES: Clinical assessment (visual analog scores) before and after each treatment, polysomnography (with sound intensity measurements), and lateral cephalometry performed prior to the first treatment and 2 months following the final treatments. RESULTS: After treatment, there was a significant overall improvement in the snoring visual analog score (7.5+/-1.5 to 4.6+/-2.5; P<.001), a small reduction in the proportion of sleep spent snoring at 50 to 60 dB (P = .03), and mild pain that was controlled with simple analgesia. There were no long-term adverse effects. Individual response could not be predicted by demographic, polysomnographic, or cephalometric data. Treatment of the proximal third of the soft palate was associated with fewer adverse effects but also seemed less effective than at the other sites. CONCLUSIONS: (1) The RFTVR palatoplasty is well tolerated with very low morbidity. (2) It is associated with subjective improvement in snoring in most patients. (3) Placement of lesions seems to influence outcome. (4) The improvement is accompanied by a marginal change in objective measurements, suggesting either an acoustic change independent of sound intensity or a placebo effect. (5) A randomized controlled trial is needed to further evaluate this therapy.

Daytime predictors of sleep hypoventilation in Duchenne muscular dystrophy.

Sleep hypoventilation is an inevitable consequence of Duchenne muscular dystrophy (DMD), usually preceding daytime respiratory failure. Appropriate scheduling of polysomnography and the introduction of noninvasive ventilation (NIV) during sleep are not defined. Our aim was to determine the parameters of daytime lung function associated with sleep hypoventilation in patients with DMD. As our method we chose a prospective comparison of wakeful respiratory function (spirometry, lung volumes, maximal mouth pressures, arterial blood gases) with outcomes of polysomnography. All measurements were made with subjects breathing air. Nineteen subjects were studied. The FEV(1) was correlated with Pa(CO(2)) (r = -0.70, p < 0.001) and base excess (r = -0.68, p < 0.01). All of these parameters were significantly related to sleep oxygenation (proportion of total sleep time spent at an Sa(O(2)) /= 2%); a Pa(CO(2)) of >/= 45 mm Hg was an equally sensitive (91%) but more specific (75%) indicator while a base excess of >/= 4 mmol/L was highly specific (100%) but less sensitive (55%). After introduction of NIV during sleep (n = 8), there was a significant reduction in wakeful Pa(CO(2)) (54 +/- 7.4 to 49.1 +/- 4 mm Hg, p < 0.02) over 0. 9 +/- 0.4 yr despite a further decline in FEV(1) (0.84 +/- 0.46 to 0. 64 +/- 0.39 L, p < 0.05). We conclude that in patients with DMD, (1) arterial blood gases should be performed once the FEV(1) falls below 40% of the predicted value; (2) polysomnography should be considered when the Pa(CO(2)) is >/= 45 mm Hg, particularly if the base excess is >/= 4 mmol/L; (3) the decrease in wakeful Pa(CO(2)) after NIV administered during sleep implicates sleep hypoventilation in the pathogenesis of respiratory failure; and (4) impaired ventilatory drive is a possible mechanism for respiratory failure, as the NIV-associated decrease in wakeful Pa(CO(2)) occurs despite a further decline in ventilatory capacity, suggesting continuing deterioration in respiratory muscle function.

Epidemiology of burns due to domestic flammable agents.

An analysis of the epidemiological factors relating to domestic flammable agents has shown that 17.7 per cent of admissions over a 5-year period were involved in domestic flammable injuries; 87.7 per cent of the patients were male, with 38.9 per cent being young males between 12 and 19 years old. Petrol and diesel accounted for 56.8 per cent of the burns and the average body surface area burned was 17.7 per cent. Most commonly the face, hands and limbs were burned, and the average length of stay was 18.25 days, 69.2 per cent of the burns were due to human error and were thus potentially preventable, 21.2 per cent had predisposing conditions with 8.9 per cent being due to alcohol. It was considered that the strategies to prevent these burns injuries should be aimed particularly at young males.