A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization

EURASIP Journal on Advances in Signal Processing. 2017;2017(1):1-14 DOI 10.1186/s13634-017-0519-3

 

Journal Homepage

Journal Title: EURASIP Journal on Advances in Signal Processing

ISSN: 1687-6172 (Print); 1687-6180 (Online)

Publisher: SpringerOpen

Society/Institution: European Association for Signal Processing (EURASIP)

LCC Subject Category: Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication | Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Runnan He (School of Computer Science and Technology, Harbin Institute of Technology (HIT))
Kuanquan Wang (School of Computer Science and Technology, Harbin Institute of Technology (HIT))
Qince Li (School of Computer Science and Technology, Harbin Institute of Technology (HIT))
Yongfeng Yuan (School of Computer Science and Technology, Harbin Institute of Technology (HIT))
Na Zhao (School of Computer Science and Technology, Harbin Institute of Technology (HIT))
Yang Liu (School of Computer Science and Technology, Harbin Institute of Technology (HIT))
Henggui Zhang (School of Computer Science and Technology, Harbin Institute of Technology (HIT))

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 13 weeks

 

Abstract | Full Text

Abstract Cardiovascular diseases are associated with high morbidity and mortality. However, it is still a challenge to diagnose them accurately and efficiently. Electrocardiogram (ECG), a bioelectrical signal of the heart, provides crucial information about the dynamical functions of the heart, playing an important role in cardiac diagnosis. As the QRS complex in ECG is associated with ventricular depolarization, therefore, accurate QRS detection is vital for interpreting ECG features. In this paper, we proposed a real-time, accurate, and effective algorithm for QRS detection. In the algorithm, a proposed preprocessor with a band-pass filter was first applied to remove baseline wander and power-line interference from the signal. After denoising, a method combining K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) was used for accurate QRS detection in ECGs with different morphologies. The proposed algorithm was tested and validated using 48 ECG records from MIT-BIH arrhythmia database (MITDB), achieved a high averaged detection accuracy, sensitivity and positive predictivity of 99.43, 99.69, and 99.72%, respectively, indicating a notable improvement to extant algorithms as reported in literatures.