ÇEKİK, Rasım Anadolu Üniversitesi - Mühendislik Fakültesi - Bilgisayar Mühendisliği Bölümü, İki Eylül Kampüsü, Turkey , TELÇEKEN, Sedat Anadolu Üniversitesi - Mühendislik Fakültesi - Bilgisayar Mühendisliği Bölümü, İki Eylül Kampüsü, Turkey

CLASSIFICATION OF EKG SIGNALS USING ROUGH SETS THEORY

34127

Rough sets theory (RST) is a rule-based method used for the analysis and data mining in expert systems such as fuzzy sets. Rough sets organize data sets with missing, inconsistent and ambiguous data and make them suitable for analysis and evaluation. This paper proposes a new rough sets theory - based model for the classification of EKG signals. Missing, unnecessary and inconsistent data sets are encountered mostly in patient data. For correct diagnosis, it is very important to correctly classify and extract rules from these data sets. The application of the proposed method to a data set containing EKG signals improves the running time performance of classification. Additionally, the proposed method requires minimal number of parameters and can be used as an aid for doctors for faster and early diagnosis. EKG signals are classified correctly up to 85% by this model.

125

Rough sets theory , EKG , Classification , Expert systems , Big data analysis , Data mining.

Anadolu University Journal of Science and Technology. A : Applied Sciences and Engineering

135