OSA classification

Before classification, the SI was used to distinguish between normal and OSA suspected regions. Next, the OSA suspected region was analyzed via SVM using temporal and spectral features (Fig. 1). Finally, OSA was classified using an SVM. The aim of SVM is to determine an optimal separating hyperplane that shows the maximum margin between the apneic and non-apneic segments. First, the input data was transformed into a higher dimensional space by employing a kernel function, and then a linear optimal hyperplane was constructed between normal and OSA classes in the transformed space. These data vectors nearest to the constructed line in the transformed space are called support vectors. In this study, we applied a single binary SVM classifier with a radial basis function employed as the kernel function. The multiplier coefficient α and regularization parameter C were determined empirically (C = 1; α = 0.5). The extracted parameters of the PRV time series were used as input features in the SVM, and the output types were represented as follows: −1 = Normal and +1 = OSA. Then, the number of automatically detected apneic events was counted (per hour of recording) and compared with standard cutoffs (5, 15, and 30 events/hr) to designate the recording as a mild, moderate, or severe group. All SVMs were trained and tested on the SVM toolbox of MATLAB (Mathworks Inc.).