Evaluation of characteristic frequency features in healthy and diseased ECG via k-NN classifier

Author

Othman, A.N. ; Mohd Sapuddin, M.E. ; Saaid, M.F. ; Megat Ali, M.S.A.

Author_Institution

Fac. of Electr. Eng., Univ. Teknol. MARA, Shah Alam, Malaysia

fYear

2014

Firstpage

117

Lastpage

120

Abstract

This paper presents an evaluation of characteristic frequency features in healthy and diseased ECG via k-NN classifier. Initially, a total of 264 segment samples are obtained for healthy, bundle branch blocks, dysrhythmia cardiomyopathy conditions from the PTB Diagnostic ECG database. The signal is preprocessed to obtain the power spectral density. The characteristic frequency for each segment sample is then extracted. Six distinct characteristic frequencies have been observed with varying pattern of power distribution in each ECG condition. Power related to specific characteristic frequencies is then successfully implemented for feature classification via k-NN with 100% accuracy during training and testing. Reliability of the characteristic frequencies as ECG descriptors has also been confirmed via k-fold cross-validation.

Keywords

electrocardiography; medical disorders; medical signal processing; signal classification; spectral analysis; ECG condition; ECG descriptor; PTB diagnostic ECG database; bundle branch block; characteristic frequency feature; diseased ECG; dysrhythmia cardiomyopathy condition; feature classification; healthy ECG; k-NN classifier; k-fold cross-validation; power distribution; power spectral density; reliability; Accuracy; Conferences; Electrocardiography; Feature extraction; Process control; Testing; Training; ECG; characteristic frequency; k-NN; k-fold cross-validation; power spectral density;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Process and Control (ICSPC), 2014 IEEE Conference on

Print_ISBN

978-1-4799-6105-4

Type

conf

DOI

10.1109/SPC.2014.7086241

Filename

7086241