A neural network technique for feature selection and identification of obstructive sleep apnea

2013 ◽  
Author(s):  
Abdulnasir Hossen
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Feature Selection ◽  
Sleep Apnea ◽  
Obstructive Sleep

scholarly journals Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study

2021 ◽  
Author(s):  
Satoru Tsuiki ◽  
Takuya Nagaoka ◽  
Tatsuya Fukuda ◽  
Yuki Sakamoto ◽  
Fernanda R. Almeida ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Apnea Hypopnea Index ◽  
Cephalometric Analysis ◽  
Obstructive Sleep ◽  
Main Region

Abstract Purpose In 2-dimensional lateral cephalometric radiographs, patients with severe obstructive sleep apnea (OSA) exhibit a more crowded oropharynx in comparison with non-OSA. We tested the hypothesis that machine learning, an application of artificial intelligence (AI), could be used to detect patients with severe OSA based on 2-dimensional images. Methods A deep convolutional neural network was developed (n = 1258; 90%) and tested (n = 131; 10%) using data from 1389 (100%) lateral cephalometric radiographs obtained from individuals diagnosed with severe OSA (n = 867; apnea hypopnea index > 30 events/h sleep) or non-OSA (n = 522; apnea hypopnea index < 5 events/h sleep) at a single center for sleep disorders. Three kinds of data sets were prepared by changing the area of interest using a single image: the original image without any modification (full image), an image containing a facial profile, upper airway, and craniofacial soft/hard tissues (main region), and an image containing part of the occipital region (head only). A radiologist also performed a conventional manual cephalometric analysis of the full image for comparison. Results The sensitivity/specificity was 0.87/0.82 for full image, 0.88/0.75 for main region, 0.71/0.63 for head only, and 0.54/0.80 for the manual analysis. The area under the receiver-operating characteristic curve was the highest for main region 0.92, for full image 0.89, for head only 0.70, and for manual cephalometric analysis 0.75. Conclusions A deep convolutional neural network identified individuals with severe OSA with high accuracy. Future research on this concept using AI and images can be further encouraged when discussing triage of OSA.

scholarly journals Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258040
Author(s):  
Eric Yeh ◽  
Eileen Wong ◽  
Chih-Wei Tsai ◽  
Wenbo Gu ◽  
Pai-Lien Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Physiological Data ◽  
Apnea Hypopnea Index ◽  
Clinical Practices ◽  
Sleep Study ◽  
Link Type ◽  
Obstructive Sleep ◽  
Sensitivity Specificity

Many wearables allow physiological data acquisition in sleep and enable clinicians to assess sleep outside of sleep labs. Belun Sleep Platform (BSP) is a novel neural network-based home sleep apnea testing system utilizing a wearable ring device to detect obstructive sleep apnea (OSA). The objective of the study is to assess the performance of BSP for the evaluation of OSA. Subjects who take heart rate-affecting medications and those with non-arrhythmic comorbidities were included in this cohort. Polysomnography (PSG) studies were performed simultaneously with the Belun Ring in individuals who were referred to the sleep lab for an overnight sleep study. The sleep studies were manually scored using the American Academy of Sleep Medicine Scoring Manual (version 2.4) with 4% desaturation hypopnea criteria. A total of 78 subjects were recruited. Of these, 45% had AHI < 5; 18% had AHI 5–15; 19% had AHI 15–30; 18% had AHI ≥ 30. The Belun apnea-hypopnea index (bAHI) correlated well with the PSG-AHI (r = 0.888, P < 0.001). The Belun total sleep time (bTST) and PSG-TST had a high correlation coefficient (r = 0.967, P < 0.001). The accuracy, sensitivity, specificity in categorizing AHI ≥ 15 were 0.808 [95% CI, 0.703–0.888], 0.931 [95% CI, 0.772–0.992], and 0.735 [95% CI, 0.589–0.850], respectively. The use of beta-blocker/calcium-receptor antagonist and the presence of comorbidities did not negatively affect the sensitivity and specificity of BSP in predicting OSA. A diagnostic algorithm combining STOP-Bang cutoff of 5 and bAHI cutoff of 15 events/h demonstrated an accuracy, sensitivity, specificity of 0.938 [95% CI, 0.828–0.987], 0.944 [95% CI, 0.727–0.999], and 0.933 [95% CI, 0.779–0.992], respectively, for the diagnosis of moderate to severe OSA. BSP is a promising testing tool for OSA assessment and can potentially be incorporated into clinical practices for the identification of OSA. Trial registration: ClinicalTrial.org NCT03997916 https://clinicaltrials.gov/ct2/show/NCT03997916?term=belun+ring&draw=2&rank=1

Neural Network Prediction of Obstructive Sleep Apnea From Clinical Criteria

CHEST Journal ◽  
1999 ◽  
Vol 116 (2) ◽  
pp. 409-415 ◽  
Author(s):  
Simon D. Kirby ◽  
Wayne Danter ◽  
Charles F.P. George ◽  
Tanya Francovic ◽  
Kathleen A. Ferguson ◽  
...  
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Clinical Criteria ◽  
Obstructive Sleep ◽  
Neural Network Prediction ◽  
Network Prediction

scholarly journals IDENTIFICATION OF OBSTRUCTIVE SLEEP APNEA USING ARTIFICIAL NEURAL NETWORKS AND WAVELET PACKET DECOMPOSITION OF THE HRV SIGNAL

2020 ◽  
Vol 17 (1) ◽  
pp. 24
Author(s):  
Abdulnasir Hossen ◽  
Sarah Qasim
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Normal Subjects ◽  
University Hospital ◽  
Feedforward Network ◽  
Obstructive Sleep

The advancement of telecommunication technologies has provided us with new promising alternatives for remote diagnosis and possible treatment suggestions for patients of diverse health disorders, among which is the ability to identify Obstructive Sleep Apnea (OSA) syndrome by means of Electrocardiograph (ECG) signal analysis. In this paper, the standard spectral bands’ powers and statistical interval-based parameters of the Heart Rate Variability (HRV) signal were considered as a form of features for classifying the Sultan Qaboos University Hospital (SQUH) database for OSA syndrome into 4 different levels. Wavelet packet analysis was applied to obtain and estimate the standard frequency bands of the HRV signal. Further, the single perceptron neural network, the feedforward with back-propagation neural network and the probabilistic neural network have been implemented in the classification task. The classification between normal subjects versus severe OSA patients achieved 95% accuracy with the probabilistic neural network. While the classification between normal subjects versus mild OSA subjects reached accuracy of 95% also. When grouping mild, moderate and severe OSA subjects in one group compared to normal subjects as a second group, the classification with the feedforward network achieved an accuracy of 87.5%. Finally, when classifying subjects directly into one of the four classes (normal or mild or moderate or severe), a 77.5% accuracy was achieved with the feedforward network.

Comparison of SFS and mRMR for oximetry feature selection in obstructive sleep apnea detection

2018 ◽  
Vol 32 (20) ◽  
pp. 15711-15731 ◽  
Author(s):  
Sheikh Shanawaz Mostafa ◽  
Fernando Morgado-Dias ◽  
Antonio G. Ravelo-García
Keyword(s):  
Obstructive Sleep Apnea ◽  
Feature Selection ◽  
Sleep Apnea ◽  
Obstructive Sleep ◽  
Apnea Detection

Modeling Obstructive Sleep Apnea Voices Using Deep Neural Network Embeddings and Domain-Adversarial Training

2020 ◽  
Vol 14 (2) ◽  
pp. 240-250
Author(s):  
Juan M. Perero-Codosero ◽  
Fernando Espinoza-Cuadros ◽  
Javier Anton-Martin ◽  
Miguel A. Barbero-Alvarez ◽  
Luis A. Hernandez-Gomez
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Deep Neural Network ◽  
Obstructive Sleep ◽  
Adversarial Training
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