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In this study; in order to diagnose congestive heart failure (CHF) patients,non-linear secondorder difference plot (SODP) obtained from raw 256 Hz sampledfrequency and windowed record with different time of ECG records are used. Allof the data rows are labelled with their belongings to classify much morerealistically. SODPs are divided into different radius of quadrant regions andnumbers of the points fall in the quadrants are computed in order to extractfeature vectors. Fisher's linear discriminant, Naive Bayes, and artificialneural network are used as classifier. The results are considered in two stepvalidation methods as general kfold cross-validation and patient basedcross-validation. As a result, it is shown that using neural network classifierwith features obtained from SODP, the constructed system could distinguishnormal and CHF patients with 100% accuracy rate.

Raspberry Pi Based Intelligent Robot that Recognizes and Places Puzzle Objects