DocumentCode :
2404183
Title :
SVM Classification of patients prone to atrial fibrillation
Author :
Graja, Salim ; Boucher, Jean Marc
Author_Institution :
GET, ENST de Bretagne, Brest, France
fYear :
2005
fDate :
1-3 Sept. 2005
Firstpage :
370
Lastpage :
374
Abstract :
A method is presented for automatic analysis of the P-wave, based on lead II of a 12-lead standard ECG, in resting conditions during a routine examination for the detection of patients prone to atrial fibrillation (AF), one of the most prevalent arrhythmias. After the P-wave delineation, a set of parameters to detect patients prone to AF was calculated from the P-wave. The detection efficiency was validated on an ECG database of 112 patients, including a control group of 40 people and a study group of 72 patients with documented AF. A support vector machine method (SVM) was applied, and the results obtained showed a specificity of 90% and a sensitivity of 85.7%. This represents an increase of more than 20% compared to two other classical classification methods: discriminant analysis and neural network.
Keywords :
bioelectric phenomena; electrocardiography; medical signal processing; signal classification; support vector machines; ECG; P-wave delineation; SVM classification; arrhythmias; patients atrial fibrillation detection; support vector machine method; Atrial fibrillation; Cardiac disease; Cardiovascular diseases; Databases; Electrocardiography; Electrophysiology; Neural networks; Senior citizens; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Signal Processing, 2005 IEEE International Workshop on
Print_ISBN :
0-7803-9030-X
Electronic_ISBN :
0-7803-9031-8
Type :
conf
DOI :
10.1109/WISP.2005.1531687
Filename :
1531687
Link To Document :
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