Analysis of snoring sound by psychoacoustic parameters

Review on Biomedical Sensors, Technologies and Algorithms for Diagnosis of Sleep Disordered Breathing: Comprehensive Survey

This paper provides a comprehensive review of available technologies for measurements of vital physiology related parameters that cause sleep disordered breathing (SDB). SDB is a chronic disease that may lead to several health problems and increase the risk of high blood pressure and even heart attack. Therefore, the diagnosis of SDB at an early stage is very important. The essential primary step before diagnosis is measurement. Vital health parameters related to SBD might be measured through invasive or non-invasive methods. Nowadays, with respect to increase in aging population, improvement in home health management systems is needed more than even a decade ago. Moreover, traditional health parameter measurement techniques such as polysomnography are not comfortable and introduce additional costs to the consumers. Therefore, in modern advanced self-health management devices, electronics and communication science are combined to provide appliances that can be used for SDB diagnosis, by monitoring a patient's physiological parameters with more comfort and accuracy. Additionally, development in machine learning algorithms provides accurate methods of analysing measured signals. This paper provides a comprehensive review of measurement approaches, data transmission, and communication networks, alongside machine learning algorithms for sleep stage classification, to diagnose SDB.

Evaluation of acoustic characteristics of snoring sounds obtained during drug-induced sleep endoscopy

INTRODUCTION

Snoring sounds are discussed to contain acoustic information about their geneses. Nocturnal snoring can easily be recorded acoustically but it is difficult to visually verify its genesis. Contrary, snoring patterns induced by drug-induced sleep endoscopy (DISE) can be visually differentiated. The aim of the study was to classify patterns of obstructions and vibration during DISE and to evaluate acoustic characteristics between these different patterns of snoring.

METHODS

DISE was performed in 41 male patients with sleep-disordered breathing. The recorded video sequences (n = 108) were classified visually at a mute mode in different patterns of snoring (velar, velar obstructive, tonsillar, post-apnoeic). The sound tracks of these subgroups were analysed and compared with regard to the parameters sound pressure level, loudness, sharpness, roughness, fluctuations strength and centre frequency.

RESULTS

Obstructive snoring patterns revealed a higher loudness than non-obstructive patterns (>25 sone). Velar snoring showed more roughness (>150 cAsper) than tonsillar and post-apnoeic snoring and revealed the lowest centre frequency (<3000 Hz) of all patterns. Tonsillar snoring presented the highest sharpness (>1.6 acum) whereas post-apnoeic snoring revealed the largest fluctuation strength (>50 cVacil).

CONCLUSION

Different snoring patterns induced by DISE can be classified visually, and an approach to differentiate them acoustically by means of psychoacoustic analyses is demonstrated. On the basis of these results, nocturnal snoring might also be differentiated by psychoacoustic algorithms which could be implemented in acoustic polygraphic screening devices in the future.

Modeling the pharyngeal anatomical effects on breathing resistance and aerodynamically generated sound

The objective of this study was to systematically assess the effects of pharyngeal anatomical details on breathing resistance and acoustic characteristics by means of computational modeling. A physiologically realistic nose-throat airway was reconstructed from medical images. Individual airway anatomy such as the uvula, pharynx, and larynx was then isolated for examination by gradually simplifying this image-based model geometry. Large eddy simulations with the FW-H acoustics model were used to simulate airflows and acoustic sound generation with constant flow inhalations in rigid-walled airway geometries. Results showed that pharyngeal anatomical details exerted a significant impact on breathing resistance and energy distribution of acoustic sound. The uvula constriction induced considerably increased levels of pressure drop and acoustic power in the pharynx, which could start and worsen snoring symptoms. Each source anatomy was observed to generate a unique spectrum with signature peak frequencies and energy distribution. Moreover, severe pharyngeal airway narrowing led to an upward shift of sound energy in the high-frequency range. Results indicated that computational aeroacoustic modeling appeared to be a practical tool to study breathing-related disorders. Specifically, high-frequency acoustic signals might disclose additional clues to the mechanism of apneic snoring and should be included in future acoustic studies.