A Novel Method for R-peak Detection in Noisy ECG Signals Using EEMD and ICA

被引：0
|
作者：
Safari, Amirhossein [1 ]
Hesar, Hamed Danandeh [1 ]
Mohebbi, Maryam [1 ]
Faradji, Farhad [1 ]
机构：
[1] KN Toosi Univ Technol, Biomed Engn, Tehran, Iran
来源：
2016 23RD IRANIAN CONFERENCE ON BIOMEDICAL ENGINEERING AND 2016 1ST INTERNATIONAL IRANIAN CONFERENCE ON BIOMEDICAL ENGINEERING (ICBME) | 2016年
关键词：
independent component analysis; blind source seperation; Ensemble Empirical Mode Decomposition; R-peak detection; QRS complex detection; EMPIRICAL-MODE DECOMPOSITION; ALGORITHMS;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
In this paper, we present a novel algorithm for R-peak detection in noisy ECG signals using ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA). First, we decompose the ECG signal into several Intrinsic Mode Functions (IMFs). Preprocessing is performed to select the number of IMFs (i.e., 8). These IMFs are used as the mixture signals for the ICA algorithm. Since the QRS complex is usually the strongest component in the ECG signal, if one independent component (IC) is only separated using ICA, it most probably contains the QRS complex. Pan-Tompkins algorithm is then applied to the separated IC for R-Peak detection. The QT database was used for the performance evaluation of the algorithm in different noise levels. The results show an average mean squared error (MSE) of 3.025 samples (12.1 ms), an average mean absolute error of 1.22 samples (4.87 ms), and an average MSE variance of 0.61 samples (2.44 ms) in 3 different noise levels compared with the manually annotated beats.
引用
收藏
页码：150 / 153
页数：4
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