  In this study; in order to diagnose congestive heart failure (CHF) patients,
non-linear second-order difference plot (SODP) obtained from raw 256 Hz sampled
frequency and windowed record with different time of ECG records are used. All
of the data rows are labelled with their belongings to classify much more
realistically. SODPs are divided into different radius of quadrant regions and
numbers of the points fall in the quadrants are computed in order to extract
feature vectors. Fisher's linear discriminant, Naive Bayes, Radial basis
function, and artificial neural network are used as classifier. The results are
considered in two step validation methods as general k-fold cross-validation
and patient based cross-validation. As a result, it is shown that using neural
network classifier with features obtained from SODP, the constructed system
could distinguish normal and CHF patients with 100% accuracy rate. Keywords
