  In this study; in order to diagnose congestive heart failure (CHF) patients,
non-linear secondorder 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, and artificial
neural network are used as classifier. The results are considered in two step
validation methods as general kfold 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.
