A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization
A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization
复制标题
DOI：
10.1186/s13634-017-0519-3
复制
发表时间：
2017-12
影响因子：
1.9
通讯作者：
Runnan He;Kuanquan Wang;Qince Li;Yongfeng Yuan;Na Zhao;Yang Liu;Henggui Zhang
中科院分区：
文献类型：
--
作者：
Runnan He;Kuanquan Wang;Qince Li;Yongfeng Yuan;Na Zhao;Yang Liu;Henggui Zhang
心血管疾病的发病率和死亡率都很高。然而，准确有效地诊断它们仍然是一个挑战。心电图（ECG）是心脏的一种生物电信号，提供了心脏动力学功能的重要信息，在心脏病诊断中起着重要的作用。由于心电图中的QRS波群与心室除极相关，因此，准确的QRS波群检测对于解释心电图特征至关重要。本文提出了一种实时、准确、有效的QRS波检测算法。在该算法中，提出了一个预处理器与带通滤波器，首先应用到消除基线漂移和电力线干扰的信号。在去噪后，采用K近邻法（KNN）和粒子群优化算法（PSO）相结合的方法对不同形态的心电图进行QRS波的准确检测。对MIT-BIH心律失常数据库（MITDB）中的48例ECG记录进行了测试和验证，平均检测准确率、灵敏度和阳性预测率分别为99.43%、99.69%和99.72%，与文献报道的现有算法相比有显著的改进。
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.