Detection of REM/NREM snores in obstructive sleep apnoea patients using a machine learning technique

Tracheal Sounds Features Changes in Different Sleep Stages Based on Complex Wavelet Analysis

Breathing sounds analysis during sleep is an informative method to study the upper airway. Different sleep stages may affect the breathing sound features. In this study, the tracheal breathing sounds were recorded from 5 individuals and the complex Gaussian wavelet of the deceleration phase of about 3000 successive breath cycles were calculated. The segmented portions were divided into 30 seconds episodes and the appropriate sleep stage of each segment were labeled. The results showed that the Mahalanobis distance between the real parts of the complex Gaussian wavelet coefficients and the reference distribution of each stage is changing consistently in different sleep stages.

Non-Contact Sleep Stage Detection Using Canonical Correlation Analysis of Respiratory Sound

Respiratory sound is able to differentiate sleep stages and provide a non-contact and cost-effective solution for the diagnosis and treatment monitoring of sleep-related diseases. While most of the existing respiratory sound-based methods focus on a limited number of sleep stages such as sleep/wake and wake/rapid eye movement (REM)/non-REM, it is essential to detect sleep stages at a finer level for sleep quality evaluation. In this paper, we for the first time study a sleep stage detection method aiming at classifying sleep states into four sleep stages: wake, REM, light sleep, and deep sleep from the respiratory sound. In addition to extracting time-domain features, frequency-domain features of respiratory sound, non-linear features of snoring sound are devised to better characterize snoring-related signals of respiratory sound. To effectively fuse the three sets of features, a novel feature fusion technique combining the generalized canonical correlation analysis with the ReliefF algorithm is proposed for discriminative feature selection. Final stage detection is achieved with popular classifiers including decision tree, support vector machines, K-nearest neighbor, and the ensemble classifier. To evaluate our proposed method, we built an in-house dataset, which is comprised of 13 nights of sleep audio data from a sleep laboratory. Experimental results indicate that our proposed method outperforms the existing related ones and is promising for large-scale non-contact sleep monitoring.

Sleep staging using nocturnal sound analysis

Sleep staging is essential for evaluating sleep and its disorders. Most sleep studies today incorporate contact sensors that may interfere with natural sleep and may bias results. Moreover, the availability of sleep studies is limited, and many people with sleep disorders remain undiagnosed. Here, we present a pioneering approach for rapid eye movement (REM), non-REM, and wake staging (macro-sleep stages, MSS) estimation based on sleep sounds analysis. Our working hypothesis is that the properties of sleep sounds, such as breathing and movement, within each MSS are different. We recorded audio signals, using non-contact microphones, of 250 patients referred to a polysomnography (PSG) study in a sleep laboratory. We trained an ensemble of one-layer, feedforward neural network classifiers fed by time-series of sleep sounds to produce real-time and offline analyses. The audio-based system was validated and produced an epoch-by-epoch (standard 30-sec segments) agreement with PSG of 87% with Cohen's kappa of 0.7. This study shows the potential of audio signal analysis as a simple, convenient, and reliable MSS estimation without contact sensors.

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.