Deep Learning Analyses of Brain MRI to Identify Sustained Attention Deficit in Treated Obstructive Sleep Apnea: A Pilot Study

2022 ◽  
Author(s):  
Chirag Agarwal ◽  
Saransh Gupta ◽  
Muhammad Najjar ◽  
Terri E. Weaver ◽  
Xiaohong Joe Zhou ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Pilot Study ◽  
Sustained Attention ◽  
Attention Deficit ◽  
Brain Mri ◽  
Obstructive Sleep

scholarly journals Accurate Deep Learning-Based Sleep Staging in a Clinical Population with Suspected Obstructive Sleep Apnea

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Henri Korkalainen ◽  
Timo Leppanen ◽  
Juhani Aakko ◽  
Sami Nikkonen ◽  
Samu Kainulainen ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Clinical Population ◽  
Sleep Staging ◽  
Obstructive Sleep

A Test for the Determination of Sustained Attention in Patients with Obstructive Sleep Apnea Syndrome

Respiration ◽  
2000 ◽  
Vol 67 (5) ◽  
pp. 526-532 ◽  
Author(s):  
Winfried J. Randerath ◽  
Carsten Gerdesmeyer ◽  
Karsten Siller ◽  
Galina Gil ◽  
Bernd Sanner ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Obstructive Sleep Apnea Syndrome ◽  
Sustained Attention ◽  
Sleep Apnea Syndrome ◽  
Obstructive Sleep ◽  
Apnea Syndrome

scholarly journals Evaluation of Respiratory Resistance as a Predictor for Oral Appliance Treatment Response in Obstructive Sleep Apnea: A Pilot Study

2021 ◽  
Vol 10 (6) ◽  
pp. 1255
Author(s):  
Hiroyuki Ishiyama ◽  
Masayuki Hideshima ◽  
Shusuke Inukai ◽  
Meiyo Tamaoka ◽  
Akira Nishiyama ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Pilot Study ◽  
Reduction Rate ◽  
Oral Appliance ◽  
Respiratory Resistance ◽  
Obstructive Sleep ◽  
Respiratory Event ◽  
Significant Difference ◽  
Treatment Responses

The aim of this study was to determine the utility of respiratory resistance as a predictor of oral appliance (OA) response in obstructive sleep apnea (OSA). Twenty-seven patients with OSA (mean respiratory event index (REI): 17.5 ± 6.5 events/h) were recruited. At baseline, the respiratory resistance (R20) was measured by impulse oscillometry (IOS) with a fitted nasal mask in the supine position, and cephalometric radiographs were obtained to analyze the pharyngeal airway space (SPAS: superior posterior airway space, MAS: middle airway space, IAS: inferior airway space). The R20 and radiographs after the OA treatment were evaluated, and the changes from the baseline were analyzed. A sleep test with OA was carried out using a portable device. The subjects were divided into Responders and Non-responders based on an REI improvement ≥ 50% from the baseline, or REI < 5 after treatment, and the R20 reduction rate between the two groups were compared. The subjects comprised 20 responders and 7 non-responders. The R20 reduction rate with OA in responders was significantly greater than it was in non-responders (14.4 ± 7.9 % versus 2.4 ± 9.8 %, p < 0.05). In responders, SPAS, MAS, and IAS were significantly widened and R20 was significantly decreased with OA (p < 0.05). There was no significant difference in non-responders (p > 0.05). A logistic multiple regression analysis showed that the R20 reduction rate was predictive for OA treatment responses (2% incremental odds ratio (OR), 24.5; 95% CI, 21.5–28.0; p = 0.018). This pilot study confirmed that respiratory resistance may have significant clinical utility in predicting OA treatment responses.

Identification and treatment of obstructive sleep apnea in adults and children with epilepsy: a prospective pilot study

2003 ◽  
Vol 4 (6) ◽  
pp. 509-515 ◽  
Author(s):  
Beth A. Malow ◽  
Kevin J. Weatherwax ◽  
Ronald D. Chervin ◽  
Timothy F. Hoban ◽  
Mary L. Marzec ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Pilot Study ◽  
Prospective Pilot Study ◽  
Obstructive Sleep ◽  
Adults And Children

407 Explanatory analysis of polysomnography for the identification of sleep apnea hypopnea events using deep learning neural network

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A161-A162
Author(s):  
Soonhyun Yook ◽  
Chaitanya Gupte ◽  
Zhixian Han ◽  
Eun Yeon Joo ◽  
Hea Ree Park ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Learning Algorithms ◽  
Sleep Apnea Syndrome ◽  
Learning Approaches ◽  
Explanatory Analysis ◽  
Technology Advancement ◽  
Obstructive Sleep ◽  
Deep Learning Neural Network

Abstract Introduction Using deep learning algorithms, we investigated univariate and multivariate effects of four polysomnography features including heart rate (HR), electrocardiogram (ECG), oxygen saturation (SpO2) and nasal air flow (NAF) on the identification of sleep apnea and hypopnea events. This explanatory analysis that may clarify the sensitivity and specificity of those features to SAs and SHs have not been probed. Methods We studied 804 polysomonography samples from 704 patients with obstructive sleep apnea and 100 controls. The input data were converted into scalograms as 4-channel 2D images to train Xception networks. For training, 77,638 patches were sampled from the original 6-hour sleep data with 30-second time width. A 10% of these patches were segregated as the test-set. With each feature sets, we tested the following classifications: 1) normal vs apnea vs hypopnea; 2) normal vs. apnea+hypopnea; 3) normal vs. apnea; and 4) normal vs. hypopnea. Results SpO2 classified normal vs. apnea most accurately (98%), followed by NAF (85%), ECG (77%), and HR (63%). SpO2 also showed the highest accuracy in classifying normal vs. hypopnea (87%), and normal vs. apnea+hypopnea (96%) and three groups (82%). When the combination of four features were used, the classification accuracies were generally improved compared to use of SpO2 only (normal vs. apnea 99%; vs. hypopnea 89%; vs. apnea+hypopnea: 94%; three groups: 86%). Conclusion Deep learning with SpO2 or NAF feature most accurately classified apneas from normal sleep events, suggesting these features’ characterization of sleep apnea events. Oxygen desaturation, which is a typical pattern of hypopnea, was only the feature showing reliable accuracy in classifying hypopnea vs. normal. Nevertheless, combination of four polysomnography features could improve the identification of sleep apnea and hypopnea. Furthermore, classifying normal vs. apnea+hypopnea was more accurate than separately classifying three groups, suggesting deep learning approaches as the primary screen tool. Since the classification accuracy of using SpO2 was higher than any other features, developing a portable equipment measuring SpO2 and running deep learning algorithms has the potential for inexpensive, accurate diagnostics of obstructive sleep apnea syndrome. Support (if any) This study was supported by USC STEVENS CENTER FOR INNOVATION TECHNOLOGY ADVANCEMENT GRANTS (TAG), BrightFocus Foundation Award (A2019052S).

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