Paper Info
Title
A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization.
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.
Year
DOI
Venue
2017
10.1186/s13634-017-0519-3
EURASIP J. Adv. Sig. Proc.
Keywords
Field
DocType
Electrocardiogram,QRS detection,K-Nearest Neighbor,Particle Swarm Optimization
k-nearest neighbors algorithm,Particle swarm optimization,Noise reduction,Computer vision,Pattern recognition,Computer science,Preprocessor,QRS complex,Interference (wave propagation),Extant taxon,Artificial intelligence,Ventricular depolarization
Journal
Volume
Issue
ISSN
2017
1
1687-6180
Citations 
PageRank 
References 
3
0.41
18
Authors
7
Name
Order
Citations
PageRank
Runnan He174.84
Kuanquan Wang21617141.11
Qince Li379.91
Yongfeng Yuan4117.40
Na Zhao53716.03
Yang Liu619417.42
Henggui Zhang710551.88