The Algorithm of R Peak Detection in ECG Based on Empirical Mode Decomposition

Tang, Jingtian; Yang, Xiaoli; Xu, Jun-chao; Tang, Yan; Zou, Qing; Zhang, Xiaokai

doi:10.1109/icnc.2008.337

Cited by 29 publications

(14 citation statements)

References 4 publications

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“…Due to nonlinear and non-stationary nature of most of physiological signals, EMD is an excellent choice to process physiological signals like EEG, which is one of the most complicated signals. EMD has been used in many fields such as de-noising and signal enhancement [29][30][31], and feature extraction [27,28,32,33].…”

Section: Introductionmentioning

confidence: 99%

An Improved Empirical Mode Decomposition of Electroencephalogram Signals for Depression Detection

Shen

Zhang

et al. 2022

IEEE Trans. Affective Comput.

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Depression is a mental disorder characterized by persistent low mood that affects a person's thoughts, behavior, feelings, and sense of well-being. According to the World Health Organization (WHO), depression will become the second major life-threatening illness in 2020. Electroencephalogram (EEG) signals, which reflect the working status of human brain, are regarded as the best physiological tool for depression detection. Previous studies used the Empirical Mode Decomposition (EMD) method, which can deal with the highly complex, nonlinear and non-stationary nature of EEG, to extract features from EEG signals. However, for some special data, the neighboring components extracted through EMD could certainly have sections of data carrying the same frequency at different time durations. Thus, the Intrinsic Mode Functions (IMFs) of the data could be linearly dependent and the features coefficients of expansion based on IMFs could not be extracted, which can make the pre-proposed EMD-based feature extraction method impractical. In order to solve this problem, an improved EMD applying Singular Value Decomposition (SVD)-based feature extraction method was proposed in this study, which can extract the features coefficients of expansion based on all IMFs as accurately as possible, ignoring potentially linear dependence of IMFs. Experiments were conducted on four EEG databases for detecting depression. The improved EMD-based feature extraction method can extract feature from all three channels (Fp1, Fpz, and Fp2) on the four EEG databases. The average classification results of the proposed method on the four EEG databases including depressed patients and healthy subjects reached 83.27%,85.19%, 81.98% and 88.07%, respectively, which were comparable with the pre-proposed EMD-based feature extraction method.

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Section: Introductionmentioning

confidence: 99%

An Improved Empirical Mode Decomposition of Electroencephalogram Signals for Depression Detection

Shen

Zhang

et al. 2022

IEEE Trans. Affective Comput.

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“…Therefore, EMD is ideally suited for analyzing non-stationary and nonlinear data sets. Given the merits mentioned above, EMD has been extensively applied to detect signal trends, singular points, filtering and characteristic frequencies in recent years, such as the acquisition of gravity wave characteristics [13], the extraction of solar cycle [14] and ECG peak detection [15,16]. EMD filtering is adaptive, therefore highly efficient at restricting the noise and identifying useful information in SBVS, even those with small amplitudes.…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

Performance improved method for subtracted blood volume spectrometry using empirical mode decomposition

Gao

Ding

2014

Bio-Medical Materials and Engineering

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Subtracted blood volume spectrometry (SBVS) can eliminate the background information in near infrared spectroscopy (NIRS) noninvasive biochemical sensing. However, the spectrum obtained by this method is accompanied by serious noises which are to the disadvantage of the calibration models. Empirical mode decomposition (EMD) was applied to restrict the noises in order to improve the performance of subtracted blood volume spectrometry. Certain criteria were used to evaluate the performance of the method, such as the average correlation coefficient, and the average and standard deviation of the Euclidean distance. EMD was applied to three subtracted spectra with different ǻL, and the criteria were calculated accordingly. All of the criteria were improvement. Especially for the subtracted spectra with ǻL=0.5mm, the correlation coefficient increased from 0.9970 to 0.9999, the average Euclidean distance decreased from 0.0265 to 0.0118, and the standard deviation of the Euclidean distance decreased from 0.0148 to 0.0033 after EMD filtering. The PLS models of the processed spectra were promoted as well. These preliminary results suggest that EMD is a promising means of improving the performance of subtracted blood volume spectrometry.

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“…With the mathematical morphology algorithms the signal noise is partially addressed. According to the literature, authors have also been approaching the QRS enhancement by applying Empirical Mode Decomposition (EMD) filtering (Tang et al, 2008). In Tang et al (2008), the EMD is applied to ECG signal followed by threshold.…”

Section: Introductionmentioning

confidence: 99%

“…According to the literature, authors have also been approaching the QRS enhancement by applying Empirical Mode Decomposition (EMD) filtering (Tang et al, 2008). In Tang et al (2008), the EMD is applied to ECG signal followed by threshold. In this sense, the first several Intrinsic Mode Functions can preserve the QRS content; they are able to filter out the noise, and improve the SNR.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Electrocardiogram Signals by Applying Adaptive Threshold and Principal Component Analysis

Rodriguez¹,

Mexicano²,

Bíla³

et al. 2015

Journal of Applied Research and Technology

103

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This paper presents a novel approach for QRS complex detection and extraction of electrocardiogram signals for different types of arrhythmias. Firstly, the ECG signal is filtered by a band pass filter, and then it is differentiated. After that, the Hilbert transform and the adaptive threshold technique are applied for QRS detection. Finally, the Principal Component Analysis is implemented to extract features from the ECG signal. Nineteen different records from the MIT-BIH arrhythmia database have been used to test the proposed method. A 96.28% of sensitivity and a 99.71% of positive predictivity are reported in this testing for QRS complexity detection, being a positive result in comparison with recent researches. All Rights Reserved

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The Algorithm of R Peak Detection in ECG Based on Empirical Mode Decomposition

Cited by 29 publications

References 4 publications

An Improved Empirical Mode Decomposition of Electroencephalogram Signals for Depression Detection

An Improved Empirical Mode Decomposition of Electroencephalogram Signals for Depression Detection

Performance improved method for subtracted blood volume spectrometry using empirical mode decomposition

Feature Extraction of Electrocardiogram Signals by Applying Adaptive Threshold and Principal Component Analysis

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