carried out by comparing each sample with a fixed threshold usually the median value is used to obtain a 0 1 sequence then this binary sequence is scanned from left to right and the complexity counter is increased by one unit every time a new subsequence of consecutive characters is encountered the presence of osas is related to irregularity variability and complexity of sao2 measured by apen ctm and lzc 308 respectively as a result high values of apen and lzc as well as low ctm values are expected for recordings from osas positive subjects according to a particular embodiment once the method obtains the spectral s t s b pa 306 and nonlinear features apen ctm lzc 308 during the feature extraction stage it performs pca 310 before the pattern classification stage 314 pca is usually applied to perform dimensionality reduction vectors x in a d dimensional space are mapped into a l dimensional space where 1 u2266 d is determined according to a given criterion samples of variable x in the original space are defined by the spectral and nonlinear features from sao2 recordings d 6 pca produces an uncorrelated set of d variables or components by projecting the input data onto the eigenvectors of the covariance matrix of variable x these eigenvectors constitute an orthonormal basis in the original space additionally the new d components are ranked by pca in decreasing importance since the amount of variance along a particular eigenvector is represented by its associated eigenvalue in this embodiment the method proceeds as follows 1 the mean of the vectors in the.