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

Purpose To develop and evaluate a model for obstructive sleep apnea (OSA) detection using an artificial neural network (ANN) based on the combined features of body mass index (BMI), electrocardiogram (ECG), and pulse oxygen saturation (SpO2). Methods Polysomnography (PSG) data for 148 patients with OSA and 33 unaffected individuals were included. A multi-layer feed-forward neural network (FNN) was used based on the features obtained from ECG, SpO2, and BMI. The receiver operating characteristic (ROC) curve and the metrics of accuracy, sensitivity, and specificity were used to evaluate the performance of the overall classification. Some other machine learning methods including linear discriminant, linear Support Vector Machine (SVM), Complex Tree, RUSBoosted Trees, and Logistic Regression were also used to compare their performance with the FNN. Results The accuracy, sensitivity, and specificity of the proposed multi-layer FNN were 97.8%, 98.6%, and 93.9%, respectively, and the area under the ROC curve was 97.0%. Compared with the other machine learning methods mentioned above, the FNN achieved the highest performance. Conclusions The satisfactory performance of the proposed FNN model for OSA detection indicated that it is reliable to screen potential patients with OSA using the combined channels of ECG and SpO2 and also taking into account BMI. This strategy might be a viable alternative method for OSA diagnosis.