Log in

Relevant bibliographies by topics / Snoring Sound Analysis / Journal articles

To see the other types of publications on this topic, follow the link: Snoring Sound Analysis.

Journal articles on the topic 'Snoring Sound Analysis'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Snoring Sound Analysis.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Hayashi, Shota, Meiyo Tamaoka, Tomoya Tateishi, Yuki Murota, Ibuki Handa, and Yasunari Miyazaki. "A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis." International Journal of Environmental Research and Public Health 17, no. 8 (2020): 2951. http://dx.doi.org/10.3390/ijerph17082951.

Full text

Abstract:

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 correlati

APA, Harvard, Vancouver, ISO, and other styles

2

Yao, Yuhe, Jiecheng Zhu, Shaowei Guo, Wei Liu, Li Ding, and Jianxin Peng. "Acoustic analysis of snoring sound from different microphones." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 5 (2021): 1823–32. http://dx.doi.org/10.3397/in-2021-1962.

Full text

Abstract:

Snoring is a common symptom of obstructive sleep apnea-hypopnea syndrome. The results show that there are obvious differences for most microphones in terms of the data distribution of features in the time and frequency domain. The results of snoring analysis from different recordings devices would be totally divergent. In view of this, when developing snoring analysis devices based user selected microphones (i.e. smartphone) recorded, we should take into account the discrepancy between different microphones.

APA, Harvard, Vancouver, ISO, and other styles

3

Wang, Lurui, and Zhongwei Jiang. "Tidal Volume Level Estimation Using Respiratory Sounds." Journal of Healthcare Engineering 2023 (February 16, 2023): 1–12. http://dx.doi.org/10.1155/2023/4994668.

Full text

Abstract:

Respiratory sounds have been used as a noninvasive and convenient method to estimate respiratory flow and tidal volume. However, current methods need calibration, making them difficult to use in a home environment. A respiratory sound analysis method is proposed to estimate tidal volume levels during sleep qualitatively. Respiratory sounds are filtered and segmented into one-minute clips, all clips are clustered into three categories: normal breathing/snoring/uncertain with agglomerative hierarchical clustering (AHC). Formant parameters are extracted to classify snoring clips into simple snori

APA, Harvard, Vancouver, ISO, and other styles

4

Fang, Yu, Dongbo Liu, Sixian Zhao, and Daishen Deng. "Improving OSAHS Prevention Based on Multidimensional Feature Analysis of Snoring." Electronics 12, no. 19 (2023): 4148. http://dx.doi.org/10.3390/electronics12194148.

Full text

Abstract:

Obstructive Sleep Apnea–Hypopnea Syndrome (OSAHS), a severe respiratory sleep disorder, presents a significant threat to human health and even endangers life. As snoring is the most noticeable symptom of OSAHS, identifying OSAHS via snoring sound analysis is vital. This study aims to analyze the time-domain and frequency-domain characteristics of snoring sounds to detect OSAHS and its severity. The snoring sounds are extracted and scrutinized from nighttime acoustic signals, with spectral energy ratio features being applied, calculated via the snore detection frequency division method. A varie

APA, Harvard, Vancouver, ISO, and other styles

5

Seren, Erdal, İlker İlhanlı, Nuray Bayar Muluk, Cemal Cingi, and Deniz Hanci. "Telephonic Analysis of the Snoring Sound Spectrum." Annals of Otology, Rhinology & Laryngology 123, no. 11 (2014): 758–64. http://dx.doi.org/10.1177/0003489414538401.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Herzog, Michael, Thomas Bremert, Beatrice Herzog, Werner Hosemann, Holger Kaftan, and Alexander Müller. "Analysis of snoring sound by psychoacoustic parameters." European Archives of Oto-Rhino-Laryngology 268, no. 3 (2010): 463–70. http://dx.doi.org/10.1007/s00405-010-1386-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Sadaoka, Tatsuya, Ryuichi Kanai, Noriya Kakitsuba, Yuki Fujiwara, and Hiroaki Takahashi. "Peculiar Snoring in Patients with Multiple System Atrophy: Its Sound Source, Acoustic Characteristics, and Diagnostic Significance." Annals of Otology, Rhinology & Laryngology 106, no. 5 (1997): 380–84. http://dx.doi.org/10.1177/000348949710600504.

Full text

Abstract:

It is known that abductor paralysis (AP) of the vocal folds sometimes occurs in patients with multiple system atrophy (MSA), and some of them have sleep apnea and loud snoring during sleep. However, the site of obstruction and the sound source of the snoring are still unknown. We performed fiberscopic examinations under diazepam sedation in 8 MSA patients with AP and analyzed the snoring sound. We found that the peculiar snoring occurred with inspiratory vibration of the vocal folds, and there was no obstruction in this portion. Acoustic analysis showed that the fundamental frequency of vocal

APA, Harvard, Vancouver, ISO, and other styles

8

Peng, Hao, Huijie Xu, Zhan Gao, Weining Huang, and Yuxia He. "Acoustic analysis of overnight consecutive snoring sounds by sound pressure levels." Acta Oto-Laryngologica 135, no. 8 (2015): 747–53. http://dx.doi.org/10.3109/00016489.2015.1027414.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

QUINN, S. J., L. HUANG, P. D. M. ELLIS, and J. E. FFOWCS WILLIAMS. "The differentiation of snoring mechanisms using sound analysis." Clinical Otolaryngology 21, no. 2 (1996): 119–23. http://dx.doi.org/10.1111/j.1365-2273.1996.tb01313.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Fiz, J. A., J. Abad, R. Jané, et al. "Acoustic analysis of snoring sound in patients with simple snoring and obstructive sleep apnoea." European Respiratory Journal 9, no. 11 (1996): 2365–70. http://dx.doi.org/10.1183/09031936.96.09112365.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Shang, Yuzhuo, Bin Guo, and Zijun Zhao. "Sleep Apnea Detection Based on Snoring Sound Analysis Using DS-MS neural network." Journal of Physics: Conference Series 2637, no. 1 (2023): 012007. http://dx.doi.org/10.1088/1742-6596/2637/1/012007.

Full text

Abstract:

Abstract Sleep apnea hypopnea syndrome (OSAHS) is a high-incidence disease with serious harm and potential dangers. Currently, the traditional scheme for monitoring sleep quality mainly focuses on monitoring two physiological signals: electroencephalogram (EEG) and heartbeat. However, in the sleep state, respiration is also an important physiological signal. This paper proposes a sleep apnea detection method based on snoring sound analysis using deep learning. Firstly, snoring sound signals are preprocessed and feature extraction is performed using Mel-frequency cepstral coefficients (MFCC). T

APA, Harvard, Vancouver, ISO, and other styles

12

Lawton, E., M. Jurisevic, K. Hobart, J. Polasek, and A. Fon. "P072 The Association Between Snoring and Hearing Loss in Patients with Obstructive Sleep Apnoea." SLEEP Advances 2, Supplement_1 (2021): A44. http://dx.doi.org/10.1093/sleepadvances/zpab014.116.

Full text

Abstract:

Abstract Background Snoring is the commonest symptom of OSA, occurring in 70%-95% of patients. Snoring noise in severe OSA can reach, and exceed, peaks of 80 decibels(dB). This is a noise level at which permanent hearing loss can occur. Given the chronicity of OSA, patients may be exposed to harmful noise levels daily for many years. Methods All patients underwent an overnight diagnostic sleep study. Exclusion criteria included occupational noise exposure or previously diagnosed hearing loss or head injury. Calibrated and standardised Tecpel 332 Sound-Pressure-Level meters recorded quantitativ

APA, Harvard, Vancouver, ISO, and other styles

13

HSU, YEH-LIANG, MING-CHOU CHEN, CHIH-MING CHENG, and CHANG-HUEI WU. "DEVELOPMENT OF A PORTABLE DEVICE FOR HOME MONITORING OF SNORING." Biomedical Engineering: Applications, Basis and Communications 17, no. 04 (2005): 176–80. http://dx.doi.org/10.4015/s1016237205000275.

Full text

Abstract:

Snoring analysis is important for the diagnosis and treatment of sleep-related breathing disorders (SRBD). Snoring has traditionally been assessed in clinical practice from subjective accounts by the snorer and his/her partner. The use of polysomnographic recording is a standard evaluation procedure for SRBD patients. However, it is expensive and is not suitable for long term monitoring. This paper describes the development of a portable microcontroller based device for long-term, home monitoring of snoring. By analyzing the temporal feature of the snoring sound, this device can output the tot

APA, Harvard, Vancouver, ISO, and other styles

14

Bieger-Farhan, A. K., N. K. Chadha, A. E. Camilleri, P. Stone, and K. McGuinness. "Portable method for the determination of snoring site by sound analysis." Journal of Laryngology & Otology 118, no. 2 (2004): 135–38. http://dx.doi.org/10.1258/002221504772784595.

Full text

Abstract:

It has been shown that computerized sound frequency analysis can be used todistinguish between different snoring sites. The aim of this study was to investigate whether aportable recording method using audiotapes and digital minidisc formats could produce waveformssimilar to a computer recording.The snores of 12 subjects in their natural sleep were recorded onto audiotape, minidisc and directly onto a computer. For each snorer and recording method 30 snore samples were analysed and their power ratio was calculated indicating the relative amount of sound below and above a set frequency. It was

APA, Harvard, Vancouver, ISO, and other styles

15

Martinek, Jozef, P. Klco, M. Vrabec, T. Zatko, M. Tatar, and M. Javorka. "Cough Sound Analysis." Acta Medica Martiniana 13, Supplement-1 (2013): 15–20. http://dx.doi.org/10.2478/acm-2013-0002.

Full text

Abstract:

Abstract Cough is the most common symptom of many respiratory diseases. Currently, no standardized methods exist for objective monitoring of cough, which could be commercially available and clinically acceptable. Our aim is to develop an algorithm which will be capable, according to the sound events analysis, to perform objective ambulatory and automated monitoring of frequency of cough. Because speech is the most common sound in 24-hour recordings, the first step for developing this algorithm is to distinguish between cough sound and speech. For this purpose we obtained recordings from 20 hea

APA, Harvard, Vancouver, ISO, and other styles

16

Hsieh, Hui-Shan, Chung-Jan Kang, Hai-Hua Chuang, et al. "Screening Severe Obstructive Sleep Apnea in Children with Snoring." Diagnostics 11, no. 7 (2021): 1168. http://dx.doi.org/10.3390/diagnostics11071168.

Full text

Abstract:

Efficient screening for severe obstructive sleep apnea (OSA) is important for children with snoring before time-consuming standard polysomnography. This retrospective cross-sectional study aimed to compare clinical variables, home snoring sound analysis, and home sleep pulse oximetry on their predictive performance in screening severe OSA among children who habitually snored. Study 1 included 9 (23%) girls and 30 (77%) boys (median age of 9 years). Using univariate logistic regression models, 3% oxygen desaturation index (ODI3) ≥ 6.0 events/h, adenoidal-nasopharyngeal ratio (ANR) ≥ 0.78, tonsi

APA, Harvard, Vancouver, ISO, and other styles

17

Xu, Huijie, Weining Huang, Lisheng Yu, and Lan Chen. "Sound spectral analysis of snoring sound and site of obstruction in obstructive sleep apnea syndrome." Acta Oto-Laryngologica 130, no. 10 (2010): 1175–79. http://dx.doi.org/10.3109/00016481003694774.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Malavika Pradeep, Sridevi G, and Kavitha S. "Association of Snoring and Cardiovascular Symptoms - A Survey." International Journal of Research in Pharmaceutical Sciences 11, SPL3 (2020): 653–58. http://dx.doi.org/10.26452/ijrps.v11ispl3.2997.

Full text

Abstract:

Snoring is a loud sound that can be produced when air across the relaxed tissues of the throat. The causes of snoring include age, being overweight or out of shape, the way you are built, nasal and sinus problems, sleep posture, alcohol, smoking and medications. The present study was performed to find the association between the habit of snoring and health problems like hypertension, breathlessness, fatigue and chest pain among genders. A self-developed questionnaire to assess the snoring habits of the participants with their underlying health problems. The study was conducted on an online pla

APA, Harvard, Vancouver, ISO, and other styles

19

Prabhakar, Sunil Kumar, Harikumar Rajaguru, and Dong-Ok Won. "Coherent Feature Extraction with Swarm Intelligence Based Hybrid Adaboost Weighted ELM Classification for Snoring Sound Classification." Diagnostics 14, no. 17 (2024): 1857. http://dx.doi.org/10.3390/diagnostics14171857.

Full text

Abstract:

For patients suffering from obstructive sleep apnea and sleep-related breathing disorders, snoring is quite common, and it greatly interferes with the quality of life for them and for the people surrounding them. For diagnosing obstructive sleep apnea, snoring is used as a screening parameter, so the exact detection and classification of snoring sounds are quite important. Therefore, automated and very high precision snoring analysis and classification algorithms are required. In this work, initially the features are extracted from six different domains, such as time domain, frequency domain,

APA, Harvard, Vancouver, ISO, and other styles

20

Akhil, Chaturvedi. "Advancements in Snoring Sound Analysis for Sleep Apnea Detection: A Comprehensive Review." Journal of Scientific and Engineering Research 9, no. 7 (2022): 176–84. https://doi.org/10.5281/zenodo.14272925.

Full text

Abstract:

Sleep apnea, a prevalent sleep disorder characterized by repeated breathing interruptions during sleep, poses significant health risks if left undiagnosed. Traditional diagnostic methods like polysomnography are costly and inconvenient, limiting widespread screening. This review examines the evolution of snoring sound analysis as a promising, non-invasive alternative for detecting sleep apnea. We explore the progression from traditional signal processing methods to advanced machine learning approaches, with a focus on mel spectrograms and the recent application of Vision Transformers. By synth

APA, Harvard, Vancouver, ISO, and other styles

21

Jung, Hyovin, Jee Min Choi, Yong Soo Jeong, Seok-Chan Hong, Jin Kook Kim, and Jae Hoon Cho. "Analysis of Snoring Sound in Obstructive Sleep Apnea Patients Based on Obstruction Site." Korean Journal of Otorhinolaryngology-Head and Neck Surgery 55, no. 8 (2012): 493. http://dx.doi.org/10.3342/kjorl-hns.2012.55.8.493.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

AGRAWAL, S., P. STONE, K. MCGUINNESS, J. MORRIS, and A. E. CAMILLERI. "Sound frequency analysis and the site of snoring in natural and induced sleep." Clinical Otolaryngology and Allied Sciences 27, no. 3 (2002): 162–66. http://dx.doi.org/10.1046/j.1365-2273.2002.00554.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Raed H. Ogaili. "The Anatomical Measurements of The Soft Palate and Its Association with Snoring." Academic International Journal of Medical Update 1, no. 2 (2023): 28–39. https://doi.org/10.59675/u124.

Full text

Abstract:

Background: Snoring is a prevalent health concern affecting a large portion of adults, with variations in soft palate anatomy playing a critical role in its severity. This study explores the association between specific anatomical features of the soft palate and the occurrence and severity of snoring. The objectives of this study were to investigate dimensional variations in the soft palate between snorers and non-snorers, analyze the relationship between soft palate tissue properties and the intensity of snoring, and identify anatomical markers that may predict an individual's susceptibility

APA, Harvard, Vancouver, ISO, and other styles

24

Raed H. Ogaili. "The Anatomical Measurements of The Soft Palate and Its Association with Snoring." Academic International Journal of Medical Update 2, no. 2 (2024): 79–90. https://doi.org/10.59675/u22.12.

Full text

Abstract:

Background: Snoring is a prevalent health concern affecting a large portion of adults, with variations in soft palate anatomy playing a critical role in its severity. This study explores the association between specific anatomical features of the soft palate and the occurrence and severity of snoring. The objectives of this study were to investigate dimensional variations in the soft palate between snorers and non-snorers, analyze the relationship between soft palate tissue properties and the intensity of snoring, and identify anatomical markers that may predict an individual's susceptibility

APA, Harvard, Vancouver, ISO, and other styles

25

Agrawal, Camilleri, Mcguiness, and Stone. "Sound frequency analysis and the site of snoring in natural and sedation induced sleep." Clinical Otolaryngology and Allied Sciences 23, no. 3 (1998): 280. http://dx.doi.org/10.1046/j.1365-2273.1998.0138b.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Agarwal, Camilleri, Mcguiness, and Stone. "Sound frequency analysis and the site of snoring in natural and sedation-induced sleep." Clinical Otolaryngology and Allied Sciences 23, no. 4 (1998): 372–74. http://dx.doi.org/10.1046/j.1365-2273.1998.0156d.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Dang, Bo, Danqing Ma, Shaojie Li, Zongqing Qi, and Elly Yijun Zhu. "Deep learning-based snore sound analysis for the detection of night-time breathing disorders." Applied and Computational Engineering 76, no. 1 (2024): 109–14. http://dx.doi.org/10.54254/2755-2721/76/20240574.

Full text

Abstract:

Snoring, a prevalent symptom of obstructive sleep apnea, is believed to impact 57% of men and 40% of women in the United States. Night-time breathing disorders present significant challenges to both diagnosis and treatment, impacting millions of individuals worldwide. Traditional methods like CPAP machines and lifestyle changes face barriers such as discomfort, low adherence, and high costs, prompting the need for innovative solutions. This paper presents a novel approach using artificial intelligence, specifically deep learning, to create a snore sound analysis-based alerting system. This sys

APA, Harvard, Vancouver, ISO, and other styles

28

Kazikdas, Kadir Cagdas. "The Perioperative Utilization of Sleep Monitoring Applications on Smart Phones in Habitual Snorers With Isolated Inferior Turbinate Hypertrophy." Ear, Nose & Throat Journal 98, no. 1 (2019): 32–36. http://dx.doi.org/10.1177/0145561318823315.

Full text

Abstract:

The aim of this study is to investigate the novel use of sleep monitoring applications in simple snorers undergoing concha radiofrequency surgery and to compare and correlate the pre- and postoperative symptoms of these patients using the Nasal Obstruction Symptom Evaluation (NOSE) scale. In this retrospective analysis, we have selected 18 consecutive patients with no comorbid sleep or medical disorders suffering from chronic nasal blockage and habitual snoring due to isolated submucous inferior turbinate hyperplasia. After a median follow-up of 8.3 months, the NOSE scale and snoring sound int

APA, Harvard, Vancouver, ISO, and other styles

29

Xu, Huajun, Wei Song, Hongliang Yi, et al. "Nocturnal snoring sound analysis in the diagnosis of obstructive sleep apnea in the Chinese Han population." Sleep and Breathing 19, no. 2 (2014): 599–605. http://dx.doi.org/10.1007/s11325-014-1055-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Jun, Won-Ho, Hyung-Ju Kim, and Youn-Sik Hong. "Sleep Pattern Analysis in Unconstrained and Unconscious State." Sensors 22, no. 23 (2022): 9296. http://dx.doi.org/10.3390/s22239296.

Full text

Abstract:

Sleep accounts for one-third of an individual’s life and is a measure of health. Both sleep time and quality are essential, and a person requires sound sleep to stay healthy. Generally, sleep patterns are influenced by genetic factors and differ among people. Therefore, analyzing whether individual sleep patterns guarantee sufficient sleep is necessary. Here, we aimed to acquire information regarding the sleep status of individuals in an unconstrained and unconscious state to consequently classify the sleep state. Accordingly, we collected data associated with the sleep status of individuals,

APA, Harvard, Vancouver, ISO, and other styles

31

McDonald, Andrew, Anurag Agarwal, Ben Williams, Nai-Chieh Liu, and Jane Ladlow. "Neural network analysis of pharyngeal sounds can detect obstructive upper respiratory disease in brachycephalic dogs." PLOS ONE 19, no. 8 (2024): e0305633. http://dx.doi.org/10.1371/journal.pone.0305633.

Full text

Abstract:

Brachycephalic obstructive airway syndrome (BOAS) is a highly prevalent respiratory disease affecting popular short-faced dog breeds such as Pugs and French bulldogs. BOAS causes significant morbidity, leading to poor exercise tolerance, sleep disorders and a shortened lifespan. Despite its severity, the disease is commonly missed by owners or disregarded by veterinary practitioners. A key clinical sign of BOAS is stertor, a low-frequency snoring sound. In recent years, a functional grading scheme has been introduced to semi-objectively grade BOAS based on the presence of stertor and other abn

APA, Harvard, Vancouver, ISO, and other styles

32

Herzog, Michael, Eva Schieb, Thomas Bremert, et al. "Frequency analysis of snoring sounds during simulated and nocturnal snoring." European Archives of Oto-Rhino-Laryngology 265, no. 12 (2008): 1553–62. http://dx.doi.org/10.1007/s00405-008-0700-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Schäfer, J., and W. Pirsig. "Digital signal analysis of snoring sounds in children." International Journal of Pediatric Otorhinolaryngology 20, no. 3 (1990): 193–202. http://dx.doi.org/10.1016/0165-5876(90)90349-v.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Sakakura, Atsushi. "Acoustic analysis of snoring sounds with chaos theory." International Congress Series 1257 (December 2003): 227–30. http://dx.doi.org/10.1016/s0531-5131(03)01170-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Wang, Can, Jianxin Peng, Lijuan Song, and Xiaowen Zhang. "Automatic snoring sounds detection from sleep sounds via multi-features analysis." Australasian Physical & Engineering Sciences in Medicine 40, no. 1 (2016): 127–35. http://dx.doi.org/10.1007/s13246-016-0507-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Mikami, Tsuyoshi, Hirotaka Takahashi, and Kazuya Yonezawa. "DETECTING NONLINEAR AND NONSTATIONARY PROPERTIES OF POST-APNEIC SNORING SOUNDS USING HILBERT–HUANG TRANSFORM." Biomedical Engineering: Applications, Basis and Communications 31, no. 03 (2019): 1950017. http://dx.doi.org/10.4015/s1016237219500170.

Full text

Abstract:

This study focuses on patients with severe obstructive sleep apnea syndrome (OSAS), and clarifies the existence of nonlinear and nonstationary properties in post-apneic snoring sounds. Many researchers have tried to discover intrinsic properties of the snoring sounds in OSAS patients for the past decades using linear frequency analysis, but no one has shown any evidence of the existence of nonlinearity and nonstationarity based on the quantitative evaluation of the post-apneic snoring sounds. In this study, Hilbert–Huang transform (HHT), which is designed for analyzing nonlinear and nonstation

APA, Harvard, Vancouver, ISO, and other styles

37

Yu, Min, and Xuemei Gao. "0526 Deep Convolutional Neural Network for Groaning and Snoring Sounds Classification." SLEEP 47, Supplement_1 (2024): A226. http://dx.doi.org/10.1093/sleep/zsae067.0526.

Full text

Abstract:

Abstract Introduction Catathrenia is a rare sleep-related breathing disorder characterized by recurrent monotonous groaning during sleep. The acoustic characteristics of groaning and snoring sounds have been investigated. This study aims to propose a deep convolutional neural network (CNN) for automatic binary classification. Methods This study consisted of 3728 episodes of groaning sounds and 4577 episodes of snoring sounds obtained from synchronized audio of full-night polysomnography. Four features extracted from log-scaled mel-spectrograms were used as input. The background gaussian noise

APA, Harvard, Vancouver, ISO, and other styles

38

Chiang, Jui-Kun, Yen-Chang Lin, Chih-Ming Lu, and Yee-Hsin Kao. "Snoring Index and Neck Circumference as Predictors of Adult Obstructive Sleep Apnea." Healthcare 10, no. 12 (2022): 2543. http://dx.doi.org/10.3390/healthcare10122543.

Full text

Abstract:

Background. Snoring is the cardinal symptom of obstructive sleep apnea (OSA). The acoustic features of snoring sounds include intra-snore (including snoring index [SI]) and inter-snore features. However, the correlation between snoring sounds and the severity of OSA according to the apnea–hypopnea index (AHI) is still unclear. We aimed to use the snoring index (SI) and the Epworth Sleepiness Scale (ESS) to predict OSA and its severity according to the AHI among middle-aged participants referred for polysomnography (PSG). Methods. In total, 50 participants (mean age, 47.5 ± 12.6 years; BMI: 29.

APA, Harvard, Vancouver, ISO, and other styles

39

Hara, Hirotaka, Naoko Murakami, Yuji Miyauchi, and Hiroshi Yamashita. "Acoustic Analysis of Snoring Sounds by a Multidimensional Voice Program." Laryngoscope 116, no. 3 (2006): 379–81. http://dx.doi.org/10.1097/01.mlg.0000195378.08969.fd.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Rauscher, H., W. Popp, and H. Zwick. "Quantification of sleep disordered breathing by computerized analysis of oximetry, heart rate and snoring." European Respiratory Journal 4, no. 6 (1991): 655–59. http://dx.doi.org/10.1183/09031936.93.04060655.

Full text

Abstract:

Intermittent snoring and cyclic oscillations of heart rate and oxyhaemoglobin saturation (Sao2) are characteristic features of the obstructive sleep apnoea syndrome (OSAS). Thus, overnight recordings of laryngeal sounds and heart rate by a portable device (MESAM) and of Sao2 by oximetry are applicable to screen outpatients for the presence of OSAS. Computerized analysis for time intervals of constant heart rate and intervals between snoring sounds is used by MESAM to quantify respiratory disturbances during sleep. Rapid increases in Sao2 during the postapnoeic hyperventilation period together

APA, Harvard, Vancouver, ISO, and other styles

41

Abeyratne, Udantha R., Ajith S. Wakwella, and Craig Hukins. "Pitch jump probability measures for the analysis of snoring sounds in apnea." Physiological Measurement 26, no. 5 (2005): 779–98. http://dx.doi.org/10.1088/0967-3334/26/5/016.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Jiang, Yanmei, Jianxin Peng, and Lijuan Song. "An OSAHS evaluation method based on multi-features acoustic analysis of snoring sounds." Sleep Medicine 84 (August 2021): 317–23. http://dx.doi.org/10.1016/j.sleep.2021.06.012.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Herzog, Michael, Thomas Kühnel, Thomas Bremert, Beatrice Herzog, Werner Hosemann, and Holger Kaftan. "The impact of the microphone position on the frequency analysis of snoring sounds." European Archives of Oto-Rhino-Laryngology 266, no. 8 (2008): 1315–22. http://dx.doi.org/10.1007/s00405-008-0858-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Peng, Hao, Huijie Xu, Zhiyong Xu, et al. "Acoustic analysis of snoring sounds originating from different sources determined by drug-induced sleep endoscopy." Acta Oto-Laryngologica 137, no. 8 (2017): 872–76. http://dx.doi.org/10.1080/00016489.2017.1293291.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Koo, Soo Kweon, Soon Bok Kwon, Yang Jae Kim, JI Seung Moon, Young Jun Kim, and Sung Hoon Jung. "Acoustic analysis of snoring sounds recorded with a smartphone according to obstruction site in OSAS patients." European Archives of Oto-Rhino-Laryngology 274, no. 3 (2016): 1735–40. http://dx.doi.org/10.1007/s00405-016-4335-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Przystup, Piotr, Adam Bujnowski, Jacek Rumiński, and Jerzy Wtorek. "A Detector of Sleep Disorders for Using at Home." Journal of Telecommunications and Information Technology, no. 2 (June 30, 2014): 70–78. http://dx.doi.org/10.26636/jtit.2014.2.1025.

Full text

Abstract:

Obstructive sleep apnea usually requires all-night examination in a specialized clinic, under the supervision of a medical staff. Because of those requirements it is an expensive and a non-widely utilized test. Moving the examination procedure to patients’ home with automatic analysis algorithms involved will decrease the costs and make it available for larger group of patients. The developed device allows all-night recordings of the following biosignals: three channels ECG, thoracic impedance (respiration), snoring sounds and larynx vibrations. Additional information, like patient’s body posi

APA, Harvard, Vancouver, ISO, and other styles

47

Policht, Richard, Artur Kowalczyk, Ewa Łukaszewicz, and Vlastimil Hart. "Hissing of geese: caller identity encoded in a non-vocal acoustic signal." PeerJ 8 (November 24, 2020): e10197. http://dx.doi.org/10.7717/peerj.10197.

Full text

Abstract:

Non-vocal, or unvoiced, signals surprisingly have received very little attention until recently especially when compared to other acoustic signals. Some sounds made by terrestrial vertebrates are produced not only by the larynx but also by the syrinx. Furthermore, some birds are known to produce several types of non-syrinx sounds. Besides mechanical sounds produced by feathers, bills and/or wings, sounds can be also produced by constriction, anywhere along the pathway from the lungs to the lips or nostrils (in mammals), or to the bill (in birds), resulting in turbulent, aerodynamic sounds. The

APA, Harvard, Vancouver, ISO, and other styles

48

Luo, Huiping, Austin Scholp, and Jack J. Jiang. "The Finite Element Simulation of the Upper Airway of Patients with Moderate and Severe Obstructive Sleep Apnea Hypopnea Syndrome." BioMed Research International 2017 (October 24, 2017): 1–5. http://dx.doi.org/10.1155/2017/7058519.

Full text

Abstract:

Objectives. To investigate the snoring modes of patients with Obstructive Sleep Apnea Hypopnea Syndrome and to discover the main sources of snoring in soft tissue vibrations. Methods. A three-dimensional finite element model was developed with SolidEdge to simulate the human upper airway. The inherent modal simulation was conducted to obtain the frequencies and the corresponding shapes of the soft tissue vibrations. The respiration process was simulated with the fluid-solid interaction method through ANSYS. Results. The first 6 orders of modal vibration were 12 Hz, 18 Hz, 21 Hz, 22 Hz, 36 Hz,

APA, Harvard, Vancouver, ISO, and other styles

49

Chi, Young Hoon, Soon Bok Kwon, Tae Kyung Koh, et al. "Acoustic Analysis of Snoring Sounds in Rapid Eye Movement (REM) and Non-Rapid Eye Movement (NREM) (N2) Sleep in OSAS patients." Journal of Clinical Otolaryngology Head and Neck Surgery 34, no. 3 (2023): 76–82. http://dx.doi.org/10.35420/jcohns.2023.34.3.76.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Rajabrundha, A., A. Lakshmisangeetha, and A. Balajiganesh. "Analysis of Sleep apnea Considering Electrocardiogram Data Using Deep learning Algorithms." Journal of Physics: Conference Series 2318, no. 1 (2022): 012009. http://dx.doi.org/10.1088/1742-6596/2318/1/012009.

Full text

Abstract:

Abstract Sleep is a vital component of every human being. Adequate restful and restorative sleep reenergizes the body, enhances overall health and psychological well-being. Sleep hygiene, chaotic lifestyles, disorder breathing, stress, and anxiety contribute to poor sleep quality. Obstructive sleep apnea (OSA) sleep respiratory disorder causes temporary lapses of breathing results in gasping, choking, snoring sounds during sleep. The individual does not consciously wake up, but the brain has to start breathing again which disrupts the sleep quality. Polysomnography (PSG) sleep study is employe

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!