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

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.

2020 ◽  
Vol 90 (4) ◽  
pp. 556-563
Author(s):  
Yoon-Ji Kim ◽  
Hyung-Kyun Shin ◽  
Dong-Yul Lee ◽  
Jae-Jun Ryu ◽  
Tae Hoon Kim

ABSTRACT Objectives To investigate the associations between nasal airway volume and the presence and severity of obstructive sleep apnea (OSA) in adults. Materials and Methods The medical records of adult patients who visited the sleep clinic at University Hospital between June 2013 and April 2017 and underwent overnight polysomnography for the diagnosis of obstructive sleep apnea were reviewed retrospectively. Using computed tomography, the volumes of the nasal airways and maxillary sinuses were measured, and associations with the presence and severity of OSA were analyzed while controlling for the effects of possible confounders such as lateral cephalometric variables, maxillary widths, tongue/hyoid position, and soft palate dimensions. Results Comparison between normal subjects and patients with OSA revealed that the latter had decreased ratios of maxillary sinus volume to whole nasal airway volume (P = .029) than normal subjects. OSA severity was greater in those with inferior positions of the hyoid (P = .010), in older patients (P = .011), and in those with high body mass index (P = .001). The volume of the total nasal airway or maxillary sinuses were not associated with OSA severity. Conclusions A decreased ratio of maxillary sinus volume to whole nasal airway volume is associated with adult OSA. However, OSA severity is not associated with either maxillary sinus volume or whole nasal airway volume.


Author(s):  
Satoru Tsuiki ◽  
Takuya Nagaoka ◽  
Tatsuya Fukuda ◽  
Yuki Sakamoto ◽  
Fernanda R. Almeida ◽  
...  

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.


PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258040
Author(s):  
Eric Yeh ◽  
Eileen Wong ◽  
Chih-Wei Tsai ◽  
Wenbo Gu ◽  
Pai-Lien Chen ◽  
...  

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


2020 ◽  
pp. 1-6
Author(s):  
Haiyun Ye ◽  
Chenjin Jin ◽  
Xiaoyan Li ◽  
Limin Zhao ◽  
Yuan Li ◽  
...  

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A218-A218
Author(s):  
L Xu ◽  
B T Keenan ◽  
A S Wiemken ◽  
A I Pack ◽  
R J Schwab

Abstract Introduction Previous studies have shown that obese patients with obstructive sleep apnea (OSA) have a significantly greater percentage of fat tissue in soft palate than normal subjects. However, the influence of soft palate fat is not clear in non-obese adults with OSA. This study compared the volume of fat in the soft palate between lean adults with OSA and lean controls. Methods We examined soft palate fat in 21 lean OSA cases and 16 lean controls with body mass index (BMI) &lt;25 kg/m2. All subjects underwent a magnetic resonance imaging (MRI) with three-point Dixon scan. We used volumetric reconstruction algorithms to quantify the amount of soft palate fat, which was compared between apnecis and controls. Analysis reproducibility was quantified using intraclass correlation coefficients (ICC) from repeated analyses of 20 randomly-chosen MRIs. Results Analysis of soft palate fat was highly reproducible, with an ICC (95% confidence interval) of 0.968 (0.923, 0.987). Lean apneics were younger than lean controls (45.3±13.0 vs. 62.1±10.4 years; p&lt;0.0001). No significant differences between apneics and controls were observed in the average BMI (23.4±2.2 vs. 23.5 ± 2.6 kg/m2; p=0.824), the fat pads volume (4198±1728 vs. 3880±1544 mm3; p=0.646), and the proportion of males (61.9% vs. 68.8%; p=0.666). In unadjusted analyses, the lean OSA group showed significantly higher soft palate fat volume than lean controls (7605±2109 vs. 5327±1783 mm3; p=0.003). When adjusting for age, gender and BMI, no differences was observed between groups in soft palate fat volume (p=0.122) and fat pads volume (p=0.702). Conclusion Analysis of soft palate fat volume from Dixon MRI is highly reproducible. Our results indicate no significant difference in deposition of fat at soft palate between lean patients with OSA and lean controls when accounting for age, gender and BMI. Support This study is supported by National Institutes of Health Grant: 2P01HL094307-06A1. LX is supported by Young Elite Scientists Sponsorship Program of China Association for Science and Technology.


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 ◽  
...  

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