Characterizing the NREM/REM sleep specific obstructive sleep apnea severity using snore sounds.

Obstructive Sleep Apnea (OSA) patients have frequent breathing obstructions and upper airway (UA) collapse during sleep. It is clinically important to estimate OSA severity separately for Rapid Eye Movement (REM) and non-REM (NREM) sleep states, but the task requires Polysomnography (PSG) which uses about 15-20 body contact sensors and subjective assessment. Almost all OSA patients snore. Vibration in narrowed UA muscles cause snoring in OSA. Moreover, as sleep states are associated with distinct breathing patterns and UA muscle tone, REM/NREM specific information must be available via snore/breathing sounds. Our previous works have shown that snoring carries significant information related to REM/NREM sleep states and OSA. We hypothesized that such information from snoring sound could be used to characterize OSA specific to REM/NREM sleep states independent of PSG. We acquired overnight audio recording from 91 patients (56 males and 35 females) undergoing PSG and labeled snore sounds as belonging to REM/NREM stages based on PSG. We then developed features to capture REM/NREM specific information and trained logistic regression (LR) classifier models to map snore features to OSA severity bands. Considering separate LR models for males and females, we achieved 94-100% sensitivity (84-89% specificity) for NREM stages at the OSA severity threshold of 30 events/h. Corresponding sensitivity for REM stages were 92-97% with specificity 83-85%. Results indicate that it is feasible to estimate severe/non-severe OSA in REM/NREM sleep based on snore/breathing sounds alone, acquired using simple bedside sound acquisition devices such as mobile phones.

Variations of snoring properties with macro sleep stages in a population of Obstructive Sleep Apnea patients.

Snoring is common in Obstructive Sleep Apnea (OSA) patients. Snoring originates from the vibration of soft tissues in the upper airways (UA). Frequent UA collapse in OSA patients leads to sleep disturbances and arousal. In a routine sleep diagnostic procedure, sleep is broadly divided into rapid eye movement (REM), non-REM (NREM) states. These Macro-Sleep States (MSS) are known to be involved with different neuromuscular activities. These differences should influence the UA mechanics in OSA patients as well as the snoring sound (SS). In this paper, we propose a logistic regression model to investigate whether the properties of SS from OSA patients can be separated into REM/NREM group. Analyzing mathematical features of more than 500 SS events from 7 OSA patients, the model achieved 76% (± 0.10) sensitivity and 75% (± 0.10) specificity in categorizing REM and NREM related snores. These results indicate that snoring is affected by REM/NREM states and proposed method has potential in differentiating MSS.

Neural network based algorithm for automatic identification of cough sounds.

Cough is the most common symptom of the several respiratory diseases containing diagnostic information. It is the best suitable candidate to develop a simplified screening technique for the management of respiratory diseases in timely manner, both in developing and developed countries, particularly in remote areas where medical facilities are limited. However, major issue hindering the development is the non-availability of reliable technique to automatically identify cough events. Medical practitioners still rely on manual counting, which is laborious and time consuming. In this paper we propose a novel method, based on the neural network to automatically identify cough segments, discarding other sounds such a speech, ambient noise etc. We achieved the accuracy of 98% in classifying 13395 segments into two classes, 'cough' and 'other sounds', with the sensitivity of 93.44% and specificity of 94.52%. Our preliminary results indicate that method can develop into a real-time cough identification technique in continuous cough monitoring systems.

Cough sound analysis - a new tool for diagnosing pneumonia.

Pneumonia kills over 1,800,000 children annually throughout the world. Prompt diagnosis and proper treatment are essential to prevent these unnecessary deaths. Reliable diagnosis of childhood pneumonia in remote regions is fraught with difficulties arising from the lack of field-deployable imaging and laboratory facilities as well as the scarcity of trained community healthcare workers. In this paper, we present a pioneering class of enabling technology addressing both of these problems. Our approach is centered on automated analysis of cough and respiratory sounds, collected via microphones that do not require physical contact with subjects. We collected cough sounds from 91 patients suspected of acute respiratory illness such as pneumonia, bronchiolitis and asthma. We extracted mathematical features from cough sounds and used them to train a Logistic Regression classifier. We used the clinical diagnosis provided by the paediatric respiratory clinician as the gold standard to train and validate our classifier against. The methods proposed in this paper could separate pneumonia from other diseases at a sensitivity and specificity of 94% and 75% respectively, based on parameters extracted from cough sounds alone. Our method has the potential to revolutionize the management of childhood pneumonia in remote regions of the world.

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.

High frequency analysis of cough sounds in pediatric patients with respiratory diseases.

Cough is a common symptom in a range of respiratory diseases and is considered a natural defense mechanism of the body. Despite its critical importance in the diagnosis of illness, there are no golden methods to objectively assess cough. In a typical consultation session, a physician may briefly listen to the cough sounds using a stethoscope placed against the chest. The physician may also listen to spontaneous cough sounds via naked ears, as they naturally propagate through air. Cough sounds carry vital information on the state of the respiratory system but the field of cough analysis in clinical medicine is in its infancy. All existing cough analysis approaches are severely handicapped by the limitations of the human hearing range and simplified analysis techniques. In this paper, we address these problems, and explore the use of frequencies covering a range well beyond the human perception (up to 90 kHz) and use wavelet analysis to extract diagnostically important information from coughs. Our data set comes from a pediatric respiratory ward in Indonesia, from subjects diagnosed with asthma, pneumonia and rhinopharyngitis. We analyzed over 90 cough samples from 4 patients and explored if high frequencies carried useful information in separating these disease groups. Multiple regression analysis resulted in coefficients of determination (R(2)) of 77-82% at high frequencies (15 kHz-90 kHz) indicating that they carry useful information. When the high frequencies were combined with frequencies below 15kHz, the R(2) performance increased to 85-90%.

Automated algorithm for Wet/Dry cough sounds classification.

Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis. Wet coughs are more likely to be associated with bacterial infections. At present, the wet/dry decision is based on the subjective judgment of a physician, during a typical consultation session. It is not available for long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop fully automated technology to classify cough into 'Wet' and 'Dry' categories. We propose novel features and a Logistic regression-based model for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric and adult coughs recorded using a bed-side non-contact microphone. The sensitivity and specificity of the classification were obtained as 79±9% and 72.7±8.7% respectively. These indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.

High frequency region of the snore spectra carry important information on the disease of sleep apnoea.

Snoring is the most common symptom of obstructive sleep apnoea (OSA). Several researchers have reported differences between the power spectra of non-OSA and OSA snorers. The traditional approach over the years has been to record snore sounds at a bandwidth of < 5 kHz. Narrowing of the upper airways during OSA events and the resulting upward shift of snore frequencies also lend support to the idea of examining snore sounds beyond 5 kHz. In this paper, we compute the power spectra of snores in three different bands defined as: low-frequency band (LFB: < 5 kHz); middle-frequency band (MFB: 5-10 kHz) and high-frequency band (HFB: 10-20 kHz). We illustrate that there is a significant difference between non-OSA snorers (Apnoea Hypopnoea Index (AHI) < 10) and OSA snorers (AHI > 10) in the region > 5 kHz. We then develop a feature to diagnose OSA based on the spectral differences in the high frequency region and evaluate its performance on a database of 20 subjects. Our results strongly suggest that the high-frequency region of the snore sounds carry information, hitherto disregarded, on the disease of sleep apnoea.

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.