engleski

Processing and Analysis of Biomedical Nonlinear Signals by Data Mining Methods

The paper demonstrates a nonlinear signal processing method based on an approach found in intelligent data mining. ECG signals were used as an interesting and readily available representative nonlinear domain. These signals were fed in an innovative software platform for feature extraction based on chaos theory. The resultant files were loaded into an open source machine learning software for clustering and classification analysis. The results depict 78% clustering and around 90% classification accuracy rate, which is quite impressive considering the number of features involved in the study.

Nonlinear signal processing; Chaos theory; Data mining; Clustering; Classification

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