J Korean Med Sci.  2023 Feb;38(7):e49. 10.3346/jkms.2023.38.e49.

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

• Park P
• Kim JW

• KMID: 2539534
• DOI: http://doi.org/10.3346/jkms.2023.38.e49

Abstract

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.

Keyword

Sleep Apnea; Obstructive; Heart Rate; Polysomnography; Machine Learning

Figure

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  Fig. 1 Flow chart of participant selection process. Data on outpatients and inpatients who visited our sleep center due to snoring or sleep apnea between 2013 and 2017 were collected. Patients who were under 18 years old and underwent split-night polysomnography were excluded. To reduce selection bias, patients were randomly allocated proportionally according to OSA severity. The final study group was selected by excluding additional patients with factors likely to influence the classifying performance for OSA using heart rate variability parameters.HRV = heart rate variability, OSA = obstructive sleep apnea, PSG = polysomnography.

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  Fig. 2 Study framework of machine learning process. (A) Fourteen features including age, sex, body mass index, and 11 measures of heart rate variability were used in the machine learning process for binary classification of obstructive sleep apnea by predicting the apnea-hypopnea index score. (B) Sixty percent of the data was used for training and a 10-fold cross-validation process. The remaining forty percent of data was used for the final training model. The classifying result was expressed as a confusion matrix. Sensitivity, specificity, and accuracy were calculated by the confusion matrix.AHI = apnea-hypopnea index.

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