Estimating PSD characteristics of ECG in comparison between normal and supraventricular subjects

Conference proceedings article

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

ไม่พบข้อมูลที่เกี่ยวข้อง

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Yingthawornsuk T., Phetnuam S., Singkhal S., Pattarason W.

ผู้เผยแพร่: Springer

ปีที่เผยแพร่ (ค.ศ.): 2017

Volume number: 10004 LNAI

หน้าแรก: 178

หน้าสุดท้าย: 185

จำนวนหน้า: 8

ISBN: 9783319606743

นอก: 0302-9743

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022328147&doi=10.1007%2f978-3-319-60675-0_16&partnerID=40&md5=e51b0d09516f1bd58f33bc2014fdb386

ภาษา: English-Great Britain (EN-GB)

ดูบนเว็บไซต์ของสำนักพิมพ์

บทคัดย่อ

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.

คำสำคัญ

AR, ECG, PSD, Supraventricular