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ABSTRACT: 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.
Physiological Measurement 01/2011; 32(1):83-97. · 1.68 Impact Factor
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ABSTRACT: Chronic sleepiness is a common symptom in the sleep disorders, such as, Obstructive Sleep Apnea, Periodic leg movement disorder, narcolepsy, etc. It affects 8% of the adult population and is associated with significant morbidity and increased risk to individual and society. MSLT and MWT are the existing tests for measuring sleepiness. Sleep Latency (SL) is the main measures of sleepiness computed in these tests. These are the laboratory-based tests and require services of an expert sleep technician. There are no tests available to detect inadvertent sleep onset in real time and which can be performed in any professional work environment to measure sleepiness level. In this article, we propose a fully automated, objective sleepiness analysis technique based on the single channel of EEG. The method uses a one-dimensional slice of the EEG Bispectrum representing a nonlinear transformation of the underlying EEG generator to compute a novel index called Sleepiness Index. The SL is then computed from the SI. Working on the patient's database of 42 subjects we computed SI and estimated SL. A strong significant correlation (r ≥ 0.70, s < 0.001) was found between technician scored SL and that computed via SI. The proposed technology holds promise in the automation of the MSLT and MWT tests. It can also be developed into a sleep management system, wherein the SI is incorporated into a sleepiness index alert unit to alarm the user when sleepiness level crosses the predetermined threshold.
Medical & Biological Engineering 12/2010; 48(12):1203-13. · 1.76 Impact Factor
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ABSTRACT: 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.
IEEE transactions on bio-medical engineering 12/2010; 57(12):2947-55. · 2.15 Impact Factor
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ABSTRACT: 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 > or = 15 or sometimes > or = 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 > or = 15 and 63.3%/100% for RDI > or = 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.
IEEE transactions on bio-medical engineering 03/2010; 57(8):1973-81. · 2.15 Impact Factor
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ABSTRACT: Obstructive Sleep Apnea (OSA) is a serious sleep disorder. OSA is commonly associated with snoring but it is not fully utilized
in diagnosis. Snoring contains pseudoperiodic (“pitch of snoring”) packets of energy that produces the characteristic vibrating
sounds familiar to us. Our hypothesis is that the pitch of snoring carries information on the state of the upper airways enabling
us to characterize it. Snore Related Sounds (SRS) have the advantage as they can be acquired via non-contact measurements
cheaply. However, in practice, it is likely that SRS may be corrupted by background interference such as bed sounds, duvet
sounds and speech sounds (collectively referred to as “Other Sounds”, OS).
In this paper, we explore Intra Snore Pitch Jump probability which captures and quantizes pitch jumps as a feature to diagnose
OSA. In particular, we focus into the: (i) the effect of other sounds on the performance of pitch-jump based OSA diagnosis,
and (ii) propose a snore episode lengthnormalization as an efficient way to improve the pitch-jump algorithm.
The proposed method was tested on a database of 39 subjects (training set: n=10; validation set: n=29), and ROC curves were
drawn. Our method led to improved diagnostic performance (sensitivities of 95% at specificities 86% .
12/2009: pages 2311-2314;
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ABSTRACT: Spinal cord-injured subjects were assessed during the acute admission for sleep-disordered breathing. Polysomnography demonstrated a high incidence of sleep apnoea that persisted during the acute phase. There was no correlation with respiratory function tests or measures of ventilatory control. Screening of this population is worthwhile although the clinical significance is uncertain.
Previous studies have demonstrated an increased incidence of sleep apnoea in spinal cord-injured patients. Many of these studies were performed in long-term, stable spinal cord injury (SCI). The aims of this study were: (i) to determine the prevalence of sleep-disordered breathing (SDB) in acute SCI; (ii) to document the change in SDB over time during the rehabilitation period; and (iii) to correlate the degree of SDB with ventilatory parameters.
Sixteen subjects with an acute SCI level T12 and above with complete motor impairment (American Spinal Injury Association impairment scale A or B) were recruited. Assessment, including polysomnography, respiratory function testing, and hypoxic and hypercapnic ventilatory responses, were performed 6-8 weeks post SCI, and repeated 6 months post SCI.
Eleven of 16 subjects (73%) had evidence of sleep apnoea, five of whom were moderate to severe. This high incidence persisted during the acute admission, with 9 of 12 subjects (75%) having sleep apnoea on polysomnography 20 weeks following injury. There was no correlation between the severity of SDB and other measures, such as level or completeness of injury, respiratory function tests or measures of ventilatory responses.
We have demonstrated a high incidence of sleep apnoea in the acute phase of SCI that persisted during the acute admission. Despite the high incidence of sleep apnoea, patients were relatively asymptomatic. Screening of this population would appear worthwhile given the high prevalence, although the significance of the sleep apnoea and clinical impact is not known.
Respirology 11/2009; 15(2):272-6. · 2.42 Impact Factor
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ABSTRACT: Sleep fragmentation is the predominant factor causing excessive daytime sleepiness in diseases such as sleep apnea and periodic leg movement syndrome. The reference standard for quantifying sleep fragmentation is the arousal index (ArI), which is defined as the average number of arousals per hour of sleep. Arousal scoring is tedious and subjective resulting in considerable inter- and intra-rater variability. Moreover, ArI is only weakly correlated with other indicators of sleep fragmentation such as the total sleep time (TST) and the sleep efficiency (SE). This introduces consistency problems, making the ArI difficult to interpret in practice. In this article, we address these issues by proposing a novel measure of sleep fragmentation termed the weighted-transition sleep fragmentation index (chi). This new measure is derived by capturing the different sleep states transitions and assigning weights to them. A significant correlation was found between chi and all other indices of sleep fragmentation (r = 0.72, sigma = 0.0001, r = -0.59, sigma = 0.001, r = -0.72, sigma = 0.0001, respectively, for ArI, TST and SE. These results suggest that chi is an accurate and useful tool for clinical practice.
Medical & Biological Engineering 09/2009; 47(10):1053-61. · 1.76 Impact Factor
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ABSTRACT: Chronic sleepiness is a common symptom in the sleep disorders, such as, Obstructive Sleep Apnea, Periodic leg movement syndrome, narcolepsy etc. It affects 5% of the adult population and is associated with significant morbidity and increased risk to individual and society. MSLT and MWT are the existing tests for measuring sleepiness. Sleep Latency (SL) is the main measures of sleepiness computed in these tests. Existing method of SL computation relies on the visual extraction of specific features in multi-channel electrophysiological data (EEG, EOG, and EMG) using the R&K criteria (1968). This process is cumbersome, time consuming, and prone to inter and intra-scorer variability. In this paper we propose a fully automated, objective sleepiness analysis technique based on the single channel of EEG. The method uses a one-dimensional slice of the EEG Bisprectrum representing a nonlinear transformation of the underlying EEG generator to compute a novel index called Sleepiness Index. The SL is then computed from the SI. A strong correlation (r = 0.93, rho = 0.0001) was found between technician scored SL and that computed via SI. The proposed Sleepiness Index can provide an elegant solution to the problems surrounding manual scoring and objective sleepiness.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:5543-6.
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ABSTRACT: 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.
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.
Speech production in the three conditions was compared in two ventilator-assisted participants using a series of instrumental and perceptual speech measures.
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.
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.
International Journal of Language & Communication Disorders 10/2008; 44(3):382-93. · 1.95 Impact Factor
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ABSTRACT: 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.
Physiological Measurement 03/2008; 29(2):227-43. · 1.68 Impact Factor
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ABSTRACT: 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.
Medical & Biological Engineering & Computing 09/2007; 45(8):791-806. · 1.88 Impact Factor
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ABSTRACT: The purpose of this research was to identify the determinants of right ventricular (RV) dysfunction in overweight and obese subjects.
Right ventricular dysfunction in obese subjects is usually ascribed to comorbid diseases, especially obstructive sleep apnea. We used tissue Doppler imaging to identify the determinants of RV dysfunction in overweight and obese subjects.
Standard and tissue Doppler echocardiography was performed in 112 overweight (body mass index [BMI] 25 to 29.9 kg/m2) or obese (BMI >30 kg/m2) subjects and 36 referents (BMI <25 kg/m2), including 22 with obstructive sleep apnea but no obesity. Tissue Doppler was used to measure RV systolic (s(m)) and diastolic (e(m)) velocities and strain indexes.
Obese subjects with BMI >35 kg/m2 had reduced RV function compared with referent subjects, evidenced by reduced s(m) (6.5 +/- 2.4 cm/s vs. 10.2 +/- 1.5 cm/s, p < 0.001), peak strain (-21 +/- 4% vs. -28 +/- 4%, p < 0.001), peak strain rate (-1.4 +/- 0.4 s(-1) vs. -2.0 +/- 0.5 s(-1), p < 0.001), and e(m) (-6.8 +/- 2.4 cm/s vs. -10.3 +/- 2.5 cm/s, p < 0.001), irrespective of the presence of sleep apnea. Similar but lesser degrees of reduced systolic function (p < 0.05) were present in overweight (BMI 25 to 29.9 kg/m2) and mildly obese (BMI 30 to 35 kg/m2) groups. Differences in RV e(m), s(m), and strain indexes were demonstrated between the severely versus overweight and mildly obese groups (p < 0.05). Body mass index remained independently related to RV changes after adjusting for age, log insulin, and mean arterial pressures. In obese patients, these changes were associated with reduced exercise capacity but not the duration of obesity and presence of sleep apnea or its severity.
Increasing BMI is associated with increasing severity of RV dysfunction in overweight and obese subjects without overt heart disease, independent of sleep apnea.
Journal of the American College of Cardiology 03/2006; 47(3):611-6. · 14.16 Impact Factor
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ABSTRACT: 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 ten channels of measurements requiring physical contact with sensors. PSG is inconvenient, expensive and unsuited for community screening. Snoring is the earliest symptom of OSA, but its potential in clinical diagnosis is not fully recognized yet. Diagnostic systems intent on using snore-related sounds (SRS) face the tough problem of how to define a snore. In this paper, we present a working definition of a snore, and propose algorithms to segment SRS into classes of pure breathing, silence and voiced/unvoiced snores. We propose a novel feature termed the 'intra-snore-pitch-jump' (ISPJ) to diagnose OSA. Working on clinical data, we show that ISPJ delivers OSA detection sensitivities of 86-100% while holding specificity at 50-80%. These numbers indicate that snore sounds and the ISPJ have the potential to be good candidates for a take-home device for OSA screening. Snore sounds have the significant advantage in that they can be conveniently acquired with low-cost non-contact equipment. The segmentation results presented in this paper have been derived using data from eight patients as the training set and another eight patients as the testing set. ISPJ-based OSA detection results have been derived using training data from 16 subjects and testing data from 29 subjects.
Physiological Measurement 11/2005; 26(5):779-98. · 1.68 Impact Factor
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8th International Conference on Control, Automation, Robotics and Vision, ICARCV 2004, Kunming, China, 6-9 December 2004, Proceedings; 01/2004
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