Classification of data obtained from portable ECG devices using support vector machines

Author

Vijay, S. V. R. Krishna ; Lella, Ashoka Vardhan ; Paul, Narinder S. ; Prakash, J. Suriya ; Venkatraman, V. ; Morey, Gautam ; Pesala, Bala

Author_Institution

Central Electron. Eng. Res. Inst. (CEERI), Chennai, India

fYear

2013

fDate

21-23 Sept. 2013

Firstpage

235

Lastpage

237

Abstract

We have developed an algorithm to classify Electro-Cardio-Gram (ECG) data as Arrhythmic or Normal. The ECG graph obtained is processed and the positions of P, Q, R, S, T peaks, lengths of RR interval, P-wave, QRS complex, ST segment and T-wave in each beat are extracted. The extracted features are used for training the Support Vector Machines (SVM) which could be later employed for classifying unknown data. This method can be further extended to diagnose heart diseases. We anticipate that the classification algorithm when implemented in the portable ECG device can be extremely useful for preliminary diagnosis of heart diseases in rural areas where access to qualified medical professionals is limited.

Keywords

diseases; electrocardiography; feature extraction; medical signal processing; signal classification; support vector machines; ECG data classification; ECG graph; P-wave extraction; QRS complex extraction; RR interval; ST segment extraction; T-wave extraction; arrhythmic classification; beat classification; electrocardiography; feature extraction; heart diseases diagnosis; portable ECG device; rural area; support vector machines; Electrocardiogram (ECG); Support Vector Machines (SVM);

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Electronic Systems (ICAES), 2013 International Conference on

Conference_Location

Pilani

Print_ISBN

978-1-4799-1439-5

Type

conf

DOI

10.1109/ICAES.2013.6659399

Filename

6659399