Estimating PSD characteristics of ECG in comparison between normal and supraventricular subjects
Conference proceedings article
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Publication Details
Author list: Yingthawornsuk T., Phetnuam S., Singkhal S., Pattarason W.
Publisher: Springer
Publication year: 2017
Volume number: 10004 LNAI
Start page: 178
End page: 185
Number of pages: 8
ISBN: 9783319606743
ISSN: 0302-9743
Languages: English-Great Britain (EN-GB)
Abstract
The aims of project are to develop an arithmetic program that can detect irregularity in electrocardiogram (ECG) and classify between two groups of normal and supraventricular ECG waveforms by using Auto regressive (AR) estimators with various model orders starting from 3rd to 9th. All AR estimators are associated with the PSD of ECG waveforms collected from a group of 30 subjects at 200 Hz sampling frequency. The best classification scores found on the 5th-order AR model are 95.99% and 72.17% obtained from training and testing the C4_5 classifier with the fifth-order coefficients. By classifying the 7th-order AR coefficients with Linear Least Squared (LS) classifier the accurate scores of 86.43% and 80.85% were obtained from training and testing cases respectively. These performance accuracies show that the proposed method is highly effective in parameterizing and classifying PSD feature as quantitative measure that can characterize the ECG signals of normal and supraventricular cardiac conditions. ฉ Springer International Publishing AG 2017.
Keywords
AR, ECG, PSD, Supraventricular
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