A model for obstructive sleep apnea detection using a multi-layer feed-forward neural network based on electrocardiogram, pulse oxygen saturation, and body mass index

Li, Zufei; Li, Yanru; Zhao, Guoqiang; Zhang, Xiaoqing; Xu, Wen; Han, Demin

doi:10.1007/s11325-021-02302-6

Cited by 21 publications

(9 citation statements)

References 31 publications

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“…In contrast, another study attempted to develop a model for OSA detection using machine learning methods based on HRV, pulse oxygen saturation, and BMI in 148 patients with OSA and 33 non-OSA participants. 18 A proposed model showed high prediction accuracy. However, the accuracy was not reliable in that oxygen desaturation index was used as a predictor of AHI score, which is nearly the same thing as providing the correct answer in machine learning.…”

Section: Discussionmentioning

confidence: 90%

“…The present study was conducted on a larger scale (including 792 subjects) than previous studies that have investigated the relationship between HRV parameters and PSG indices. 13 17 18 Furthermore, considering that OSA severity differs by race, 19 our study has significance in that it was conducted in an Asian population (in particular, Koreans) with physical characteristics that differ those of Westerners.…”

Section: Discussionmentioning

confidence: 96%

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A Classifying Model of Obstructive Sleep Apnea Based on Heart Rate Variability in a Large Korean Population

Park

Kim

2023

J Korean Med Sci

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Background The majority of patients with obstructive sleep apnea do not receive timely diagnosis and treatment because of the complexity of a diagnostic test. We aimed to predict obstructive sleep apnea based on heart rate variability, body mass index, and demographic characteristics in a large Korean population. Methods Models of binary classification for predicting obstructive sleep apnea severity were constructed using 14 features including 11 heart rate variability variables, age, sex, and body mass index. Binary classification was conducted separately using apnea-hypopnea index thresholds of 5, 15, and 30. Sixty percent of the participants were randomly allocated to training and validation sets while the other forty percent were designated as the test set. Classifying models were developed and validated with 10-fold cross-validation using logistic regression, random forest, support vector machine, and multilayer perceptron algorithms. Results A total of 792 (651 men and 141 women) subjects were included. The mean age, body mass index, and apnea-hypopnea index score were 55.1 years, 25.9 kg/m 2 , and 22.9, respectively. The sensitivity of the best performing algorithm was 73.6%, 70.7%, and 78.4% when the apnea-hypopnea index threshold criterion was 5, 10, and 15, respectively. The prediction performances of the best classifiers at apnea-hypopnea indices of 5, 15, and 30 were as follows: accuracy, 72.2%, 70.0%, and 70.3%; specificity, 64.6%, 69.2%, and 67.9%; area under the receiver operating characteristic curve, 77.2%, 73.5%, and 80.1%, respectively. Overall, the logistic regression model using the apnea-hypopnea index criterion of 30 showed the best classifying performance among all models. Conclusion Obstructive sleep apnea was fairly predicted by using heart rate variability, body mass index, and demographic characteristics in a large Korean population. Prescreening and continuous treatment monitoring of obstructive sleep apnea may be possible simply by measuring heart rate variability.

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Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 96%

A Classifying Model of Obstructive Sleep Apnea Based on Heart Rate Variability in a Large Korean Population

Park

Kim

2023

J Korean Med Sci

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“…Due to the overall scarcity and financial burden of PSG, methods combining multidimensional clinical parameters and machine learning have been widely used to distinguish OSA, OSA severity, and OSA prognosis ( 2 , 38 , 39 ). However, studies using DC to distinguish OSA are relatively rare.…”

Section: Discussionmentioning

confidence: 99%

Classification of severe obstructive sleep apnea with cognitive impairment using degree centrality: A machine learning analysis

Liu

Shu

et al. 2022

Front. Neurol.

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In this study, we aimed to use voxel-level degree centrality (DC) features in combination with machine learning methods to distinguish obstructive sleep apnea (OSA) patients with and without mild cognitive impairment (MCI). Ninety-nine OSA patients were recruited for rs-MRI scanning, including 51 MCI patients and 48 participants with no mild cognitive impairment. Based on the Automated Anatomical Labeling (AAL) brain atlas, the DC features of all participants were calculated and extracted. Ten DC features were screened out by deleting variables with high pin-correlation and minimum absolute contraction and performing selective operator lasso regression. Finally, three machine learning methods were used to establish classification models. The support vector machine method had the best classification efficiency (AUC = 0.78), followed by random forest (AUC = 0.71) and logistic regression (AUC = 0.77). These findings demonstrate an effective machine learning approach for differentiating OSA patients with and without MCI and provide potential neuroimaging evidence for cognitive impairment caused by OSA.

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“…We only used those in Table 3 for early referral, including obesity subjects with snoring signs, so our primary goal was to identify a cut‐off point that primary care professionals could conveniently recall and apply in their clinic. Fifth, we have not attempted to include all of them extensively, including those of human‐in‐loop (Holzinger et al, 2019) or multilayer feed‐forward NN (Li et al, 2021) in the algorithms, because more flexible algorithms are emerging nowadays for machine learning. Sixth, although there exist anatomical differences between Chinese and Caucasian populations related to snoring (Xu et al, 2020), there was no significant difference in the severity of questionnaire‐based symptoms of snoring.…”

Section: Discussionmentioning

confidence: 99%

“…Fifth, we have not attempted to include all of them extensively, including those of human-in-loop (Holzinger et al, 2019) or multilayer feed-forward NN (Li et al, 2021) in the algorithms, because more flexible algorithms are emerging nowadays for machine learning.…”

Section: Limitationsmentioning

confidence: 99%

Integrating domain knowledge with machine learning to detect obstructive sleep apnea: Snore as a significant bio‐feature

Hsu

Wang

Huang

et al. 2021

Journal of Sleep Research

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Our study's main purpose is to emphasise the significance of medical knowledge of pathophysiology before machine learning. We investigated whether combining domain knowledge with machine learning results might increase accuracy and minimise the number of bio-features used to detect obstructive sleep apnea (OSA). The present study analysed data on 36 self-reported symptoms and 24 clinical features obtained from 3,495 patients receiving polysomnography at a regional hospital and a medical centre. The area under the receiver operating characteristic (AUC) curve was used to evaluate patients with and without moderate or severe OSA using three prediction models on the basis of various estimation methods: the multiple logistic regression (MLR), support vector machine (SVM), and neural network (NN) methods. Odds ratios stratified by gender and age were also measured to account for clinicians' common sense. We discovered that adding the self-reported snoring item improved the AUC by 0.01-0.10 and helped us to rapidly achieve the optimum level. The performance of four items (gender, age, body mass index [BMI], and snoring) was comparable with that of adding two or more items (neck and waist circumference) for predicting moderate to severe OSA (Apnea-Hypopnea Index ≥15 events/hr) in all three prediction models, demonstrating the medical knowledge value of pathophysiology. The four-item test sample AUCs were 0.83, 0.84, and 0.83 for MLR, SVM, and NN, respectively.Participants with regular snoring and a BMI of ≥25 kg/m 2 had a greater chance of moderate to severe OSA according to the stratified adjusted odds ratios. Combining domain knowledge into machine learning could increase efficiency and enable primary care physicians to refer for an OSA diagnosis earlier.

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A model for obstructive sleep apnea detection using a multi-layer feed-forward neural network based on electrocardiogram, pulse oxygen saturation, and body mass index

Cited by 21 publications

References 31 publications

A Classifying Model of Obstructive Sleep Apnea Based on Heart Rate Variability in a Large Korean Population

A Classifying Model of Obstructive Sleep Apnea Based on Heart Rate Variability in a Large Korean Population

Classification of severe obstructive sleep apnea with cognitive impairment using degree centrality: A machine learning analysis

Integrating domain knowledge with machine learning to detect obstructive sleep apnea: Snore as a significant bio‐feature

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