A machine learning model combining heart rate variability, oxygen saturation, and anthropometric data achieved an AUROC of 0.83 for predicting moderate-to-severe obstructive sleep apnea.
Observational (n=291)
No
Does a machine learning model using HRV, oxygen saturation, and anthropometric data accurately predict the presence and severity of obstructive sleep apnea?
Machine learning models combining HRV, oxygen saturation, and anthropometric data show high accuracy in screening for the presence and severity of obstructive sleep apnea.
Absolute Event Rate: 0.83% vs 0.77%
p-value: p=<0.05
Introduction Obstructive sleep apnea (OSA) is a prevalent sleep disorder with a high rate of undiagnosed patients, primarily due to the complexity of its diagnosis made by polysomnography (PSG). Considering the severe comorbidities associated with OSA, especially in the cardiovascular system, the development of early screening tools for this disease is imperative. Heart rate variability (HRV) is a simple and non-invasive approach used as a probe to evaluate cardiac autonomic modulation, with a variety of newly developed indices lacking studies with OSA patients. Objectives We aimed to evaluate numerous HRV indices, derived from linear but mainly nonlinear indices, combined or not with oxygen saturation indices, for detecting the presence and severity of OSA using machine learning models. Methods ECG waveforms were collected from 291 PSG recordings to calculate 34 HRV indices. Minimum oxygen saturation value during sleep (SatMin), the percentage of total sleep time the patient spent with oxygen saturation below 90% (T90), and patient anthropometric data were also considered as inputs to the models. The Apnea-Hypopnea Index (AHI) was used to categorize into severity classes of OSA (normal, mild, moderate, severe) to train multiclass or binary (normal-to-mild and moderate-to-severe) classification models, using the Random Forest (RF) algorithm. Since the OSA severity groups were unbalanced, we used the Synthetic Minority Over-sampling Technique (SMOTE) to oversample the minority classes. Results Multiclass models achieved a mean area under the ROC curve (AUROC) of 0.92 and 0.86 in classifying normal individuals and severe OSA patients, respectively, when using all attributes. When the groups were dichotomized into normal-to-mild OSA vs. moderate-to-severe OSA, an AUROC of 0.83 was obtained. As revealed by RF, the importance of features indicates that all feature modalities (HRV, SpO 2 , and anthropometric variables) contribute to the top 10 ranks. Conclusion The present study demonstrates the feasibility of using classification models to detect the presence and severity of OSA using these indices. Our findings have the potential to contribute to the development of rapid screening tools aimed at assisting individuals affected by this condition, to expedite diagnosis and initiate timely treatment.
Santos et al. (Fri,) conducted a observational in Obstructive sleep apnea (n=291). Machine learning model using HRV, SpO2, and anthropometric data vs. Model using SpO2 and anthropometric data was evaluated on Area under the ROC curve (AUROC) for binary classification of moderate-to-severe OSA (95% CI 0.78-0.88, p=<0.05). A machine learning model combining heart rate variability, oxygen saturation, and anthropometric data achieved an AUROC of 0.83 for predicting moderate-to-severe obstructive sleep apnea.
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