Raspberry Pi Based Intelligent Robot that Recognizes and Places Puzzle Objects
This work addresses the diagnosis of congestive heart failure for medical applications, but it appears incremental as it applies existing methods like SODP and neural networks to ECG data.
The study tackled the problem of diagnosing congestive heart failure (CHF) by analyzing ECG records using non-linear second-order difference plots (SODP) and achieved a 100% accuracy rate in distinguishing normal and CHF patients with a neural network classifier.
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.