Snore Sound Analysis Can Detect the Presence of Obstructive Sleep Apnea Specific to NREM or REM Sleep

Akhter, Shahin; Abeyratne, Udantha R.; Swarnkar, Vinayak; Hukins, Craig

doi:10.5664/jcsm.7168

Cited by 25 publications

(27 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We aimed to detect the severity of OSA with snoring sound analysis and used the MFCC, FF, and VI. Although the MFCC and FF were also used in past studies, they were not adequate for determining the severity of OSA [3,5,21]. U. R. Abeyratne et al used some of these features to investigate snore sounds for OSA/non-OSA classifications [22].…”

Section: Discussionmentioning

confidence: 99%

“…U. R. Abeyratne et al used some of these features to investigate snore sounds for OSA/non-OSA classifications [22]. Shahin Akhter et al also used these features to detect the presence of OSA during non-rapid eye movement (NREM) and rapid eye movement (REM) sleep [3]. These authors detected OSA/non-OSA classifications and the presence of OSA during NREM and REM sleep at some level.…”

Section: Discussionmentioning

confidence: 99%

“…To investigate the characteristics of each snore sound episode in detail, we also computed the VI as a new feature from each snore episode to more clearly capture the sequential change in the snoring sound. In the past studies, these features were mainly used for analyzing the timbre and volume of the snoring sound and did not focus on the fine temporal changes in the snoring sound, and some studies analyzed only selected snores (usually loud events) [3,17]. However, we analyzed all snore sound episodes and computed their VI.…”

Section: Discussionmentioning

confidence: 99%

“…AHI values for adults are categorized as 5 ≤ AHI < 15, mild; 15 ≤ AHI < 30, moderate; and ≤ 30, severe. OSA patients have an increased risk for cardiovascular diseases, hypertension, diabetes, stroke, neurocognitive deficits, daytime sleepiness, depression, and mood disorders [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…Snoring sounds are caused by the vibration of soft tissue in the narrowed upper airways, including anatomical structures such as the soft palate, uvula, and pharynx [12,13]. Some of these methods have been developed using features obtained from snore sounds, such as Mel frequency cepstral coefficients (MFCC), formant frequencies (FF), non-Gaussianity scores, loudness, and pitch [3]. These methods for detecting OSA are becoming available, but most remain at the prototype stage and are insufficiently validated.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis

Hayashi

Tamaoka

Tateishi

et al. 2020

IJERPH

View full text Add to dashboard Cite

The severity of obstructive sleep apnoea (OSA) is diagnosed with polysomnography (PSG), during which patients are monitored by over 20 physiological sensors overnight. These sensors often bother patients and may affect patients’ sleep and OSA. This study aimed to investigate a method for analyzing patient snore sounds to detect the severity of OSA. Using a microphone placed at the patient’s bedside, the snoring and breathing sounds of 22 participants were recorded while they simultaneously underwent PSG. We examined some features from the snoring and breathing sounds and examined the correlation between these features and the snore-specific apnoea-hypopnea index (ssAHI), defined as the number of apnoea and hypopnea events during the hour before a snore episode. Statistical analyses revealed that the ssAHI was positively correlated with the Mel frequency cepstral coefficients (MFCC) and volume information (VI). Based on clustering results, mild snore sound episodes and snore sound episodes from mild OSA patients were mainly classified into cluster 1. The results of clustering severe snore sound episodes and snore sound episodes from severe OSA patients were mainly classified into cluster 2. The features of snoring sounds that we identified have the potential to detect the severity of OSA.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis

Hayashi

Tamaoka

Tateishi

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

Evaluating Prediction Models of Sleep Apnea From Smartphone-Recorded Sleep Breathing Sounds

Cho

Jung

Shin

et al. 2022

JAMA Otolaryngol Head Neck Surg

View full text Add to dashboard Cite

Breathing sounds during sleep are an important characteristic feature of obstructive sleep apnea (OSA) and have been regarded as a potential biomarker. Breathing sounds during sleep can be easily recorded using a microphone, which is found in most smartphone devices. Therefore, it may be easy to implement an evaluation tool for prescreening purposes.OBJECTIVE To evaluate OSA prediction models using smartphone-recorded sounds and identify optimal settings with regard to noise processing and sound feature selection. DESIGN, SETTING, AND PARTICIPANTSA cross-sectional study was performed among patients who visited the sleep center of Seoul National University Bundang Hospital for snoring or sleep apnea from August 2015 to August 2019. Audio recordings during sleep were performed using a smartphone during routine, full-night, in-laboratory polysomnography. Using a random forest algorithm, binary classifications were separately conducted for 3 different threshold criteria according to an apnea hypopnea index (AHI) threshold of 5, 15, or 30 events/h. Four regression models were created according to noise reduction and feature selection from the input sound to predict actual AHI: (1) noise reduction without feature selection, (2) noise reduction with feature selection, (3) neither noise reduction nor feature selection, and (4) feature selection without noise reduction. Clinical and polysomnographic parameters that may have been associated with errors were assessed. Data were analyzed from September 2019 to September 2020. MAIN OUTCOMES AND MEASURES Accuracy of OSA prediction models.RESULTS A total of 423 patients (mean [SD] age, 48.1 [12.8] years; 356 [84.1%] male) were analyzed. Data were split into training (n = 256 [60.5%]) and test data sets (n = 167 [39.5%]). Accuracies were 88.2%, 82.3%, and 81.7%, and the areas under curve were 0.90, 0.89, and 0.90 for an AHI threshold of 5, 15, and 30 events/h, respectively. In the regression analysis, using recorded sounds that had not been denoised and had only selected attributes resulted in the highest correlation coefficient (r = 0.78; 95% CI, 0.69-0.88). The AHI (β = 0.33; 95% CI, 0.24-0.42) and sleep efficiency (β = −0.20; 95% CI, −0.35 to −0.05) were found to be associated with estimation error. CONCLUSIONS AND RELEVANCEIn this cross-sectional study, recorded sleep breathing sounds using a smartphone were used to create reasonably accurate OSA prediction models. Future research should focus on real-life recordings using various smartphone devices.

show abstract

Extracting signature responses from respiratory flows: Low‐dimensional analyses on Direct Numerical Simulation‐predicted wakes of a flapping uvula

Wang

April

et al. 2020

Numer Methods Biomed Eng

View full text Add to dashboard Cite

Uvula-induced snoring and associated obstructive sleep apnea is a complex phenomenon characterized by vibrating structures and highly transient vortex dynamics. This study aimed to extract signature features of uvula wake flows of different pathological origins and develop a linear reduced-order surrogate model for flow control. Six airway models were developed with two uvula kinematics and three pharynx constriction levels. A direct numerical simulation (DNS) flow solver based on the immersed boundary method was utilized to resolve the wake flows induced by the flapping uvula. Key spatial and temporal responses of the flow to uvula kinematics and pharynx constriction were investigated using continuous wavelet transform (CWT), proper orthogonal decomposition (POD), and dynamic mode decomposition (DMD). Results showed highly complex patterns in flow topologies. CWT analysis revealed multiscale correlations in both time and space between the flapping uvular and wake flows. POD analysis successfully separated the flows among the six models by projecting the datasets in the vector space spanned by the first three eigenmodes. Perceivable differences were also captured in the time evolution of the DMD modes among the six models. A linear reduced-order surrogate model was constructed from the predominant eigenmodes obtained from the DMD analysis and predicted vortex patterns from this surrogate model agreed well with the corresponding DNS simulations. The computational and analytical platform presented in this study could bring a variety of applications in breathing-related disorders and beyond. The computational efficiency of surrogate modeling makes it well suited for flow control, forecasting, and uncertainty analyses.

show abstract

Snore Sound Analysis Can Detect the Presence of Obstructive Sleep Apnea Specific to NREM or REM Sleep

Cited by 25 publications

References 44 publications

A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis

A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis

Evaluating Prediction Models of Sleep Apnea From Smartphone-Recorded Sleep Breathing Sounds

Extracting signature responses from respiratory flows: Low‐dimensional analyses on Direct Numerical Simulation‐predicted wakes of a flapping uvula

Contact Info

Product

Resources

About