Relationship between airflow limitation in response to upper airway negative pressure during wakefulness and obstructive sleep apnea severity.

PURPOSE: The objective was to determine if alteration in airflow induced by negative pressure (NP) applied to participants' upper airways during wakefulness, is related to obstructive sleep apnea (OSA) severity as determined by the apnea-hypopnea index (AHI). METHODS: Adults 18 years of age or greater were recruited. All participants underwent overnight polysomnography to assess their apnea-hypopnea index (AHI). While awake, participants were twice exposed, orally, to -3 cm H2O of NP for five full breaths. The ratio of the breathing volumes of the last two breaths during NP exposure to the last two breaths prior to NP exposure was deemed the NP ratio (NPR). RESULTS: Eighteen participants were enrolled. A strong relationship between the AHI and the exponentially transformed NPR (ExpNPR) for all participants was observed (R2 = 0.55, p < 0.001). A multivariable model using the independent variable ExpNPR, age, body mass index and sex accounted for 81% of variability in AHI (p = 0.0006). A leave-one-subject-out cross-validation analysis revealed that predicted AHI using the multivariable model, and actual AHI from participants' polysomnograms, were strongly related (R2 = 0.72, p < 0.001). CONCLUSION: We conclude that ExpNPR, was strongly related to the AHI, independently of demographic factors known to be related to the AHI.

Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation.

BACKGROUND: Sleep apnea is a respiratory disorder characterized by frequent breathing cessation during sleep. Sleep apnea severity is determined by the apnea-hypopnea index (AHI), which is the hourly rate of respiratory events. In positional sleep apnea, the AHI is higher in the supine sleeping position than it is in other sleeping positions. Positional therapy is a behavioral strategy (eg, wearing an item to encourage sleeping toward the lateral position) to treat positional apnea. The gold standard of diagnosing sleep apnea and whether or not it is positional is polysomnography; however, this test is inconvenient, expensive, and has a long waiting list. OBJECTIVE: The objective of this study was to develop and evaluate a noncontact method to estimate sleep apnea severity and to distinguish positional versus nonpositional sleep apnea. METHODS: A noncontact deep-learning algorithm was developed to analyze infrared video of sleep for estimating AHI and to distinguish patients with positional vs nonpositional sleep apnea. Specifically, a 3D convolutional neural network (CNN) architecture was used to process movements extracted by optical flow to detect respiratory events. Positional sleep apnea patients were subsequently identified by combining the AHI information provided by the 3D-CNN model with the sleeping position (supine vs lateral) detected via a previously developed CNN model. RESULTS: The algorithm was validated on data of 41 participants, including 26 men and 15 women with a mean age of 53 (SD 13) years, BMI of 30 (SD 7), AHI of 27 (SD 31) events/hour, and sleep duration of 5 (SD 1) hours; 20 participants had positional sleep apnea, 15 participants had nonpositional sleep apnea, and the positional status could not be discriminated for the remaining 6 participants. AHI values estimated by the 3D-CNN model correlated strongly and significantly with the gold standard (Spearman correlation coefficient 0.79, P<.001). Individuals with positional sleep apnea (based on an AHI threshold of 15) were identified with 83% accuracy and an F1-score of 86%. CONCLUSIONS: This study demonstrates the possibility of using a camera-based method for developing an accessible and easy-to-use device for screening sleep apnea at home, which can be provided in the form of a tablet or smartphone app.

Removing of Snoring Segments from Tracheal Breathing Sounds using a Wavelet-based Algorithm.

Tracheal sounds represent information about the upper airway and respiratory airflow, however, they can be contaminated by the snoring sounds. The sound of snoring has spectral content in a wide range that overlaps with that of breathing sounds during sleep. For assessing respiratory airflow using tracheal breathing sound, it is essential to remove the effect of snoring. In this paper, an automatic and unsupervised wavelet-based snoring removal algorithm is presented. Simultaneously with full-night polysomnography, the tracheal sound signals of 9 subjects with different levels of airway obstruction were recorded by a microphone placed over the trachea during sleep. The segments of tracheal sounds that were contaminated by snoring were manually identified through listening to the recordings. The selected segments were automatically categorized based on including discrete or continuous snoring pattern. Segments with discrete snoring were analyzed by an iterative wave-based filtering optimized to separate large spectral components related to snoring from smaller ones corresponded to breathing. Those with continuous snoring were first segmented into shorter segments. Then, each short segments were similarly analyzed along with a segment of normal breathing extracted from the recordings during wakefulness. The algorithm was evaluated by visual inspection of the denoised sound energy and comparison of the spectral densities before and after removing snores, where the overall rate of detectability of snoring was less than 2%.Clinical Relevance- The algorithm provides a way of separating snoring pattern from the tracheal breathing sounds. Therefore, each of them can be analyzed separately to assess respiratory airflow and the pathophysiology of the upper airway during sleep.

Relationship between Vowel Sound Features and Pharyngeal Airway Cross-Sectional Area during Normal Breathing.

Assessment of the pharyngeal airway is becoming important for delivering personalized treatment and better management of sleep apnea. However, evaluation of the pharyngeal airway area is difficult in the current state of the art. It is essential to use simple and accessible technology to measure the pharyngeal airway area. As vowel sounds are generated by vocal cords vibration and characterized by the pharyngeal airway, vowel sounds have the potential to evaluate the pharyngeal airway area. The objective of this study was to investigate the relationship between acoustic features of vowel sounds and the pharyngeal airway cross-sectional area (PAXSA) between soft palate and glottis. Twenty subjects were included in this study whose PAXSA was measured by acoustic pharyngometry. Vowel sounds were recorded with a microphone while lying supine. Vowel sound average power was calculated in different frequency ranges of 100-3000 Hz, 100-500 Hz, 500-1000 Hz, 1000-1500 Hz, 1500-2000 Hz, 2000-2500 Hz and 2500-3000 Hz. Statistical analysis showed that the decreases in the PAXSA were strongly correlated with the higher average power of vowel sounds in all frequency ranges. These results showed that individuals with low PAXSA might articulate the vowel in higher intensity. Clinical Relevance - This study demonstrates that the pharyngeal airway cross-sectional area during normal breathing has a significant effect on vowel articulation. Thus, vowel sound features can be used to estimate the resting pharyngeal airway cross-sectional area.

ECG fiducial point extraction using switching Kalman filter.

In this paper, we propose a novel method for extracting fiducial points (FPs) of the beats in electrocardiogram (ECG) signals using switching Kalman filter (SKF). In this method, according to McSharry's model, ECG waveforms (P-wave, QRS complex and T-wave) are modeled with Gaussian functions and ECG baselines are modeled with first order auto regressive models. In the proposed method, a discrete state variable called "switch" is considered that affects only the observation equations. We denote a mode as a specific observation equation and switch changes between 7 modes and corresponds to different segments of an ECG beat. At each time instant, the probability of each mode is calculated and compared among two consecutive modes and a path is estimated, which shows the relation of each part of the ECG signal to the mode with the maximum probability. ECG FPs are found from the estimated path. For performance evaluation, the Physionet QT database is used and the proposed method is compared with methods based on wavelet transform, partially collapsed Gibbs sampler (PCGS) and extended Kalman filter. For our proposed method, the mean error and the root mean square error across all FPs are 2 ms (i.e. less than one sample) and 14 ms, respectively. These errors are significantly smaller than those obtained using other methods. The proposed method achieves lesser RMSE and smaller variability with respect to others.