• Corpus ID: 53613215

The Methods of Acoustical Analysis of Snoring for the Diagnosis of OSAHS

@inproceedings{Wang2017TheMO,
  title={The Methods of Acoustical Analysis of Snoring for the Diagnosis of OSAHS},
  author={Can Wang and Jianxin Peng},
  year={2017}
}
Obstructive sleep apnea hypopnea syndrome (OSAHS) is a serious respiratory disorder and the current detection method is Polysomnography (PSG). However, PSG is time consuming, high cost and inappropriate for the diagnosis at home. In recent years, the studies of acoustical analysis of snoring for the OSAHS diagnosis have got rapid development. Researchers have tried to explore a portable monitoring system that is affordable and can offer greater comfort to patients. In this review, we summarize… 
3 Citations

Figures from this paper

Review on Biomedical Sensors, Technologies and Algorithms for Diagnosis of Sleep Disordered Breathing: Comprehensive Survey
TLDR
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.
Sound frequency spectra of snore in relation to the site of obstruction among snorers
TLDR
There was a significant association between the snoring sound frequency and site of unilevel obstruction, and Severity of OSA was significantly associated with multileVEL obstruction.
A viable snore detection system: hardware and software implementations
TLDR
A stand-alone, custom-made biomedical system was introduced for a long-term monitoring of sleep and detection of snoring events, and high level validation confirmed the reliability and utility of the system in detecting snoring.

References

SHOWING 1-10 OF 64 REFERENCES
A model of breathing abnormalities in sleep for development of classification and diagnosis techniques
TLDR
The simulation model proposed in this study provides a repeat-able test-bed for signal processing techniques that are under development to aid clinical diagnosis of underlying pathological problems and to identify periods of sleep apnea that may develop into more serious conditions.
Pitch-jitter analysis of snoring sounds for the diagnosis of sleep apnea
TLDR
A mathematical model for snoring is presented, and its usefulness in diagnosing OSA is illustrated, and new features to diagnose OSA at low cost are provided.
Obstructive apnea hypopnea index estimation by analysis of nocturnal snoring signals in adults.
TLDR
Acoustic analysis based on intra- and inter-snore properties can differentiate subjects according to AHI and an acoustic-based screening system may address the growing needs for reliable OSA screening tool.
Nocturnal sound analysis for the diagnosis of obstructive sleep apnea
TLDR
A novel method for screening obstructive sleep apnea syndrome (OSAs) based on nocturnal acoustic signal is proposed using semi-automatic algorithm based on Gaussian mixture models which achieves more than 90% sensitivity (specificity) and produces an average of 2,000 snores per subject.
Mixed-phase modeling in snore sound analysis
TLDR
A novel model for SRS as the response of a mixed-phase system (total airways response, TAR) to a source excitation at the input is proposed, and it is shown that the TAR could be used to detect speech segments embedded in snores, and derive features to diagnose OSA via non-contact, low-cost instrumentation holding potential for a community screening device.
Significance of snoring and other sounds appearing during the night based on the ECG
  • K. Czopek
  • Medicine
    2012 Computing in Cardiology
  • 2012
TLDR
There is a significant difference in sound spectrum between snoring, normal breath and obstructive apneas which is correlated with ECG signal, and sound spectrum analysis as a supplementary method to determine the obstruction site for patients with obstructive sleep apnea syndrome (OSAS) or other disturbances during the night is demonstrated.
Spectral envelope analysis of snoring signals
TLDR
A method to classify simple snorers and OSAS patients based on spectral envelope estimation of snoring signals is proposed and significant differences are found in the formant frequencies of both groups.
Artificial neural networks for breathing and snoring episode detection in sleep sounds
TLDR
This paper proposes a fundamentally novel approach based on artificial neural network (ANN) technology to detect SBEs and shows that the proposed method vastly outperforms conventional techniques.
Acoustic analysis of overnight consecutive snoring sounds by sound pressure levels
Abstract Conclusion: The sound pressure level (SPL) parameters, especially the A-weighted equivalent sound level (LAeq) and accumulative percentile sound level 10 (L10), were significantly different
Snoring sounds variability as a signature of obstructive sleep apnea.
...
...