Efficient Fiducial Point Detection of ECG QRS Complex Based on Polygonal Approximation

Sensors. 2018;18(12):4502 DOI 10.3390/s18124502

 

Journal Homepage

Journal Title: Sensors

ISSN: 1424-8220 (Online)

Publisher: MDPI AG

LCC Subject Category: Technology: Chemical technology

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS

Seungmin Lee (School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea)
Yoosoo Jeong (School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea)
Daejin Park (School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea)
Byoung-Ju Yun (School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea)
Kil Houm Park (School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 11 weeks

 

Abstract | Full Text

Electrocardiogram signal analysis is based on detecting a fiducial point consisting of the onset, offset, and peak of each waveform. The accurate diagnosis of arrhythmias depends on the accuracy of fiducial point detection. Detecting the onset and offset fiducial points is ambiguous because the feature values are similar to those of the surrounding sample. To improve the accuracy of this paper&#8217;s fiducial point detection, the signal is represented by a small number of vertices through a curvature-based vertex selection technique using polygonal approximation. The proposed method minimizes the number of candidate samples for fiducial point detection and emphasizes these sample&#8217;s feature values to enable reliable detection. It is also sensitive to the morphological changes of various QRS complexes by generating an accumulated signal of the amplitude change rate between vertices as an auxiliary signal. To verify the superiority of the proposed algorithm, error distribution is measured through comparison with the QT-DB annotation provided by Physionet. The mean and standard deviation of the onset and the offset were stable as <inline-formula> <math display="inline"> <semantics> <mrow> <mo>&#8722;</mo> <mn>4.02</mn> <mo>&#177;</mo> <mn>7.99</mn> </mrow> </semantics> </math> </inline-formula> ms and <inline-formula> <math display="inline"> <semantics> <mrow> <mo>&#8722;</mo> <mn>5.45</mn> <mo>&#177;</mo> <mn>8.04</mn> </mrow> </semantics> </math> </inline-formula> ms, respectively. The results show that proposed method using small number of vertices is acceptable in practical applications. We also confirmed that the proposed method is effective through the clustering of the QRS complex. Experiments on the arrhythmia data of MIT-BIH ADB confirmed reliable fiducial point detection results for various types of QRS complexes.