In patients with chronic heart failure (CHF), oscillatory breathing pattern predicts poor prognosis. This work proposes a method to identify the respiratory pattern to determine periodic breathing (PB), Cheyne-Stokes respiration (CSR) and non-periodic respiratory patterns (nPB) through the respiratory flow signal. 26 patients are studied, classified in G(1) (PB), G(2) (CSR) and G(3) (nPB). The flow signal is filtered and normalized, to obtain the positive envelope that describes the respiratory pattern. With this new signal some features are extracted through its power spectral density (PSD). An adaptive feature selection algorithm is applied before the linear and non linear classification applying Leave-one-out cross-validation technique. The result obtained with linear classification was 93% using the relation between total energy and frequency interval (I(1)), peak amplitude (ampp), peak frequency (fp), and the highest slope of the positive envelope's PSD (Slope(max)). And the best result was obtained with non linear technique, with 100% correctly classified patients, using only two parameters, fp and Slope(max).