Dimensionality reduction based on fuzzy rough sets oriented to ischemia detection.
This paper presents a dimensionality reduction study based on fuzzy rough sets with the aim of increasing the discriminant capability of the representation of normal ECG beats and those that contain ischemic events. A novel procedure is proposed to obtain the fuzzy equivalence classes based on entropy and neighborhood techniques and a modification of the Quick Reduct Algorithm is used to select the relevant features from a large feature space by a dependency function. The tests were carried out on a feature space made up by 840 wavelet features extracted from 900 ECG normal beats and 900 ECG beats with evidence of ischemia. Results of around 99% classification accuracy are obtained. This methodology provides a reduced feature space with low complexity and high representation capability. Additionally, the discriminant strength of entropy in terms of representing ischemic disorders from time-frequency information in ECG signals is highlighted.
SINERGIA Research Group of the Instituto Tecnologico Metropolitano ITM, Calle 73 No. 76A-354, Medellin, Colombia. dianaorrego@itm.edu.co
Diana A
DA
Orrego
Miguel A
MA
Becerra
Edilson
E
Delgado-Trejos
D004562:Electrocardiography; D017143:Fuzzy Logic; D006801:Humans; D008962:Models, Theoretical; D017202:Myocardial Ischemia / Q000175:diagnosis* / Q000503:physiopathology