scholarly journals Differentiation Model for Insomnia Disorder and the Respiratory Arousal Threshold Phenotype in Obstructive Sleep Apnea in the Taiwanese Population Based on Oximetry and Anthropometric Features

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 50
Author(s):  
Cheng-Yu Tsai ◽  
Yi-Chun Kuan ◽  
Wei-Han Hsu ◽  
Yin-Tzu Lin ◽  
Chia-Rung Hsu ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Population Based ◽  
Support Vector ◽  
Learning Approaches ◽  
Anthropometric Parameters ◽  
Arousal Threshold ◽  
Insomnia Disorder ◽  
Obstructive Sleep ◽  
Testing Stage

Insomnia disorder (ID) and obstructive sleep apnea (OSA) with respiratory arousal threshold (ArTH) phenotypes often coexist in patients, presenting similar symptoms. However, the typical diagnosis examinations (in-laboratory polysomnography (lab-PSG) and other alternatives methods may therefore have limited differentiation capacities. Hence, this study established novel models to assist in the classification of ID and low- and high-ArTH OSA. Participants reporting insomnia as their chief complaint were enrolled. Their sleep parameters and body profile were accessed from the lab-PSG database. Based on the definition of low-ArTH OSA and ID, patients were divided into three groups, namely, the ID, low- and high-ArTH OSA groups. Various machine learning approaches, including logistic regression, k-nearest neighbors, naive Bayes, random forest (RF), and support vector machine, were trained using two types of features (Oximetry model, trained with oximetry parameters only; Combined model, trained with oximetry and anthropometric parameters). In the training stage, RF presented the highest cross-validation accuracy in both models compared with the other approaches. In the testing stage, the RF accuracy was 77.53% and 80.06% for the oximetry and combined models, respectively. The established models can be used to differentiate ID, low- and high-ArTH OSA in the population of Taiwan and those with similar craniofacial features.

414 Deep Neural Networks: A Survey Tool for Obstructive Sleep Apnea Prediction

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A164-A164
Author(s):  
Pahnwat Taweesedt ◽  
JungYoon Kim ◽  
Jaehyun Park ◽  
Jangwoon Park ◽  
Munish Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Deep Neural Networks ◽  
Support Vector ◽  
Learning Models ◽  
Obstructive Sleep ◽  
Screening Questionnaires ◽  
Machine Learning Models

Abstract Introduction Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder with an estimation of one billion people. Full-night polysomnography is considered the gold standard for OSA diagnosis. However, it is time-consuming, expensive and is not readily available in many parts of the world. Many screening questionnaires and scores have been proposed for OSA prediction with high sensitivity and low specificity. The present study is intended to develop models with various machine learning techniques to predict the severity of OSA by incorporating features from multiple questionnaires. Methods Subjects who underwent full-night polysomnography in Torr sleep center, Texas and completed 5 OSA screening questionnaires/scores were included. OSA was diagnosed by using Apnea-Hypopnea Index ≥ 5. We trained five different machine learning models including Deep Neural Networks with the scaled principal component analysis (DNN-PCA), Random Forest (RF), Adaptive Boosting classifier (ABC), and K-Nearest Neighbors classifier (KNC) and Support Vector Machine Classifier (SVMC). Training:Testing subject ratio of 65:35 was used. All features including demographic data, body measurement, snoring and sleepiness history were obtained from 5 OSA screening questionnaires/scores (STOP-BANG questionnaires, Berlin questionnaires, NoSAS score, NAMES score and No-Apnea score). Performance parametrics were used to compare between machine learning models. Results Of 180 subjects, 51.5 % of subjects were male with mean (SD) age of 53.6 (15.1). One hundred and nineteen subjects were diagnosed with OSA. Area Under the Receiver Operating Characteristic Curve (AUROC) of DNN-PCA, RF, ABC, KNC, SVMC, STOP-BANG questionnaire, Berlin questionnaire, NoSAS score, NAMES score, and No-Apnea score were 0.85, 0.68, 0.52, 0.74, 0.75, 0.61, 0.63, 0,61, 0.58 and 0,58 respectively. DNN-PCA showed the highest AUROC with sensitivity of 0.79, specificity of 0.67, positive-predictivity of 0.93, F1 score of 0.86, and accuracy of 0.77. Conclusion Our result showed that DNN-PCA outperforms OSA screening questionnaires, scores and other machine learning models. Support (if any):

scholarly journals 0424 MODERATE OBSTRUCTIVE SLEEP APNEA IS ASSOCIATED WITH INCIDENT DIABETES: A LONGITUDINAL, POPULATION-BASED STUDY

SLEEP ◽  
2017 ◽  
Vol 40 (suppl_1) ◽  
pp. A158-A158
Author(s):  
Y Li ◽  
AN Vgontzas ◽  
J Fernandez-Mendoza ◽  
F He ◽  
J Gaines ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Population Based ◽  
Incident Diabetes ◽  
Population Based Study ◽  
Obstructive Sleep

scholarly journals Risk of obstructive sleep apnea in patients with bipolar disorder: A nationwide population‐based cohort study in Taiwan

2018 ◽  
Vol 73 (4) ◽  
pp. 163-168
Author(s):  
En‐Ting Chang ◽  
Shih‐Fen Chen ◽  
Jen‐Huai Chiang ◽  
Ling‐Yi Wang ◽  
Chung‐Y Hsu ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Obstructive Sleep Apnea ◽  
Cohort Study ◽  
Sleep Apnea ◽  
Population Based ◽  
Obstructive Sleep

scholarly journals O013 The pathogenesis of obstructive sleep apnea in individuals with comorbid insomnia and obstructive sleep apnoea (COMISA)

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A6-A7
Author(s):  
E Brooker ◽  
L Thomson ◽  
S Landry ◽  
B Edwards ◽  
S Drummond
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Upper Airway ◽  
Severity Index ◽  
Loop Gain ◽  
Comorbid Insomnia ◽  
Arousal Threshold ◽  
Obstructive Sleep ◽  
High Loop ◽  
Insomnia Severity

Abstract Obstructive sleep apnea (OSA) and Insomnia are prevalent sleep disorders which are highly comorbid. This frequent co-occurrence suggests a shared etiology may exist. OSA is caused by the interaction of four pathophysiological traits: a highly collapsible upper airway, elevated loop gain, a low arousal threshold, and poor muscle compensation. No study has ascertained whether these traits are influenced by insomnia. We aimed to quantify the four traits which contribute to OSA in individuals diagnosed with comorbid insomnia and OSA (COMISA). We non-invasively determined these traits in 52 COMISA patients (Age: 56±14 years) with mild-to-severe OSA (AHI=21.2±10.63 events/h) using polysomnography. Our results indicated that 83% of COMISA patients had a low arousal threshold and only 2% of patients exhibited a highly collapsible airway using previously defined thresholds. Multiple linear regression revealed the arousal threshold (b=0.24, 95%CI[0.11, 0.37], β=0.47, p<0.001) and loop gain (b=23.6, 95%CI[7.02, 40.18], β=0.33, p<0.01) were the strongest predictors of OSA severity in our sample. There was no significant relationship between the arousal threshold and insomnia severity measured by the insomnia severity index (ISI). Further work is being performed to compare these findings with a matched sample of OSA only participants. Our preliminary findings demonstrate OSA in COMISA is characterized by a mildly collapsible airway/low arousal threshold phenotype and is largely driven by non-anatomical factors including a low arousal threshold and high loop gain. OSA treatments which are effective in patients with mild anatomical compromise and raise the arousal threshold may provide therapeutic benefit in COMISA patients.

scholarly journals Obstructive Sleep Apnea and the Risk of Atopic Dermatitis: A Population-Based Case Control Study

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e89656 ◽  
Author(s):  
Kai-Jen Tien ◽  
Chien-Wen Chou ◽  
Shang-Yu Lee ◽  
Nai-Cheng Yeh ◽  
Chwen-Yi Yang ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Atopic Dermatitis ◽  
Sleep Apnea ◽  
Case Control Study ◽  
Population Based ◽  
Case Control ◽  
Obstructive Sleep ◽  
Control Study

Association between obstructive sleep apnea and atopic dermatitis in children: A nationwide, population-based cohort study

2018 ◽  
Vol 29 (3) ◽  
pp. 260-266 ◽  
Author(s):  
Je-Ming Hu ◽  
Chin-Sheng Lin ◽  
Sy-Jou Chen ◽  
Chao-Yang Chen ◽  
Cheng-Li Lin ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Atopic Dermatitis ◽  
Cohort Study ◽  
Sleep Apnea ◽  
Population Based ◽  
Obstructive Sleep

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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