From reactive to proactive: Machine learning models for continuous positive airway pressure adjustments using heart rate variability and oximetry-related parameters

Figures

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  Figure 1.. Flowchart of data collection and processing. Polysomnography (PSG) and continuous positive airway pressure (CPAP) titration data were collected from 374 patients with each recording lasting approximately 6 h. Heart rate variability (HRV) was extracted and analyzed from EKG signals, with continuous HRV features calculated using a 60-s window and a 1-s stride. Next, time series data were generated with a 60-s window and stride. To ensure data accuracy, 60-s intervals where the heart rate exceeded 90 or fell below 40 beats per minute were adjusted by interpolating with neighboring data. Finally, data were labeled according to whether there was a pressure level adjustment.

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  Figure 2.. Training and testing processes in the development of prediction models for pressure level adjustment during continuous positive airway pressure (CPAP) titration using a patient-independent dataset. Total data from 374 patients were labeled as having pressure level adjustment or not having pressure level adjustment during CPAP titration. Time-series data were divided into a patient-independent training dataset and a test dataset with a ratio of 80%:20%. The Synthetic Minority Oversampling Technique (SMOTE) was first applied to the training dataset to oversample the data with a ratio of 4.36. Ten-fold cross-validation was performed. Trained models included logistic regression (LR), k-nearest neighbor (kNN), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), long short-term memory (LSTM), and InceptionTime. The best model was selected by both the highest accuracy and the largest area under the receiver operating characteristic curve (AUROC) and used for testing. Finally, the model performance and the permutation feature importance were evaluated.

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  Figure 3.. Feature importance of the selected model (InceptionTime model) in the patient-independent test dataset (number of patients: 75). Feature importance is represented as the percentage decrease in area under the receiver operating characteristic curve (AUROC) when the values of an individual input feature are randomly shuffled, highlighting the relative contribution of each feature to the model's performance.