Feature reduction method using self organizing maps

Author

Kutlu, Yakup ; Kuntalp, Damla

Author_Institution

Dokuz Eylul Univ., Izmir, Turkey

fYear

2009

fDate

5-8 Nov. 2009

Abstract

In this work, five main groups of arrhythmias in electrocardiograph (ECG) signals are tried to be classified using the features obtained from the output of a Self Organizing Map (SOM) network. The raw ECG signal consists of 81 sample points (60 point before and 20 point after the R peak point of the ECG). Consecutive sample values of a moving window (20 points of width) are used as the input vector of the SOM network. The output of the SOM network is used as the input vector to a classifier. K-nearest neighbor (k-NN) algorithm is chosen as the classifier. The performance of the classifier is evaluated by the average values of sensitivity, specificity, selectivity and overall accuracy. As a result, 96%, 91%, 99%, and 97% sensitivity, selectivity, specificity, and overall accuracy values are obtained.

Keywords

electrocardiography; feature extraction; medical signal processing; self-organising feature maps; signal classification; ECG; SOM network; arrhythmia; classifier; electrocardiograph signals; feature reduction method; k-nearest neighbor algorithm; overall accuracy value; selectivity value; self organizing map; sensitivity value; specificity value; Aging; Data acquisition; Electrocardiography; Filters; Microstrip; Self organizing feature maps; Signal processing; Spatial databases; Support vector machine classification; Support vector machines; Arrhythmia; ECG; K-NN; Self Organizing Maps; feature reduction;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Electronics Engineering, 2009. ELECO 2009. International Conference on

Conference_Location

Bursa

Print_ISBN

978-1-4244-5106-7

Electronic_ISBN

978-9944-89-818-8

Type

conf

Filename

5355238