Beat to Beat Classification of Long Electrocardiograms Using Entropies and Hierarchical Clustering

Author

Bahmanyar, M.R. ; Balachandran, W.

Author_Institution

Sch. of Eng., Brunel Univ., Middlesex

fYear

2005

fDate

6/27/1905 12:00:00 AM

Firstpage

5579

Lastpage

5581

Abstract

This paper introduces an entropy based method for beat to beat classification of long electrocardiograms (ECGs). A state vector is reconstructed using Taken´s delay coordinates method and Shannon entropies are calculated for each beat to form feature vectors. Hierarchical clustering is applied to these vectors to classify the beats. The algorithm was used for detection of atrial premature beats and ventricular premature beats in long electrocardiograms

Keywords

electrocardiography; entropy; medical signal processing; signal classification; signal reconstruction; ECG; Shannon entropy; Taken delay coordinates; atrial premature beat detection; beat-to-beat classification; hierarchical clustering; long electrocardiograms; state vector reconstruction; ventricular premature beat detection; Clustering algorithms; Continuous wavelet transforms; Delay effects; Design engineering; Electrocardiography; Entropy; Feature extraction; Finite impulse response filter; Frequency; Smoothing methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the

Conference_Location

Shanghai

Print_ISBN

0-7803-8741-4

Type

conf

DOI

10.1109/IEMBS.2005.1615749

Filename

1615749