Sleep apnea severity based on estimated tidal volume and snoring features from tracheal signals

Ghahjaverestan, Nasim Montazeri; Saha, Shumit; Kabir, Muammar; Gavrilovic, Bojan; Zhu, Kevin; Yadollahi, Azadeh

doi:10.1111/jsr.13490

Cited by 5 publications

(2 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Changes in alpha band synchronization, which are inseparable from eye-conclusion and enlightening, are a notable illustration of this marvel. Montazeri Ghahjaverestan [ 15 ] investigated the sleep apnea severity estimation, as the apnea/hypopnea index (AHI) was quantified, but the tidal volume estimated and extracted snoring sounds from signals of trachea. Tsao, C. H., et.al established the upper airway presence by renovating the changed sensory and motor function by vibration of hypoxia or snore.…”

Section: Literature Surveymentioning

confidence: 99%

A Real-Time Medical Ventilation on Heart Failure Analysis Based on Sleep Apnea Snore and Meta-Analysis

Liu

Zhao

2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

An issue with cardiac ventilation can result in death at any moment throughout a person’s life. The apnea-hypopnea index (AHI) has historically been influenced by medical ventilation on heart failure; nevertheless, the sleep snore analysis is the best model to diagnose. The problems with ventilation are caused by problems with air pressure and blood circulation in the heart valves, where the pathological measures are continually detecting ventilation issues. Understanding the pathophysiology of OSA will have a direct impact on clinical treatment choices as well as the design of clinical studies. Treatments could be tailored to each patient's unique needs based on the fundamental reason to their OSA. Through the OSA treatment, patients could feel better, and understanding OSA symptoms and also outcomes will improve patient’s health; as a result, the study reveals that most of the population are likely to benefit from specific OSA treatment approaches. For achieving the benefits of OSA treatment the classification accuracy is needed to be improved. So, in this research work, an LeNet-100 CNN-based deep learning technology is used to get information and apply the classification approaches. We obtained the heart failure dataset from the Kaggle website for conducting a meta-analysis. An accuracy of 93.25%, sensitivity of 97.29%, recall of 96.34%, and F measure of 95.34% had been attained. This approach outperforms the technology and is comparable to the present heart failure meta-analysis..

show abstract

Section: Literature Surveymentioning

confidence: 99%

A Real-Time Medical Ventilation on Heart Failure Analysis Based on Sleep Apnea Snore and Meta-Analysis

Liu

Zhao

2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…According to the American Sleep Medicine Society (AASM) (2012), snorers are recognized as simple snorers, mild, moderate, and severe OSAHS patients with AHI 5, 5<AHI 15, 15 < AHI 30, and AHI > 30 respectively. Polysomnography (PSG) is the golden standard to diagnose OSAHS patients clinically (Nakano et al 2014, Kim et al 2018, Montazeri et al 2022. PSG monitors various sleep related biological signals throughout the night with a variety of sensors connected to subjects, including heart rate, brain wave, blood oxygen saturation, and oronasal airflow.…”

Section: Introductionmentioning

confidence: 99%

Automatic identifying OSAHS patients and simple snorers based on Gaussian mixture models

et al. 2023

View full text Add to dashboard Cite

Snoring is a typical symptom of obstructive sleep apnea-hypopnea syndrome (OSAHS). In this study, an effective OSAHS patient detection system based on subject independence using snoring sounds is presented. A series of acoustic features including Mel cepstral coefficient, bark sub-band feature, perceptual linear prediction coefficient, pitch frequency, formant, 800Hz power ratio, spectral entropy, and gammatone cepstral coefficient are extracted. The feature selection based on the Fisher ratio is conducted and the Gaussian mixture models (GMM) of OSAHS patients and simple snorers were established respectively. For the test subject, the difference in log-likelihood probabilities of the two GMMs is calculated to identify the OSAHS patients. The proposed model achieves 90% accuracy and 95.65% precision based on leave-one-subject-out cross-validation using selected features with a dimension of 100. The average prediction time of the proposed model is 0.134 ± 0.005s. The promising results demonstrate the effectiveness and low computational cost of diagnosing OSAHS patients using snoring sounds at home.

show abstract

Automatically detecting apnea-hypopnea snoring signal based on VGG19 + LSTM

Ding

Peng

Song

et al. 2023

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Sleep apnea severity based on estimated tidal volume and snoring features from tracheal signals

Cited by 5 publications

References 52 publications

A Real-Time Medical Ventilation on Heart Failure Analysis Based on Sleep Apnea Snore and Meta-Analysis

A Real-Time Medical Ventilation on Heart Failure Analysis Based on Sleep Apnea Snore and Meta-Analysis

Automatic identifying OSAHS patients and simple snorers based on Gaussian mixture models

Automatically detecting apnea-hypopnea snoring signal based on VGG19 + LSTM

Contact Info

Product

Resources

About