Harmonic separation from grid voltage using ensemble empirical-mode decomposition and independent component analysis

2017 ◽  
Vol 27 (11) ◽  
pp. e2405 ◽  
Author(s):  
Kewei Cai ◽  
Zhiqiang Wang ◽  
Guofeng Li ◽  
Donggang He ◽  
Jinyan Song
Keyword(s):  
Independent Component Analysis ◽  
Empirical Mode Decomposition ◽  
Component Analysis ◽  
Ensemble Empirical Mode Decomposition ◽  
Independent Component ◽  
Grid Voltage ◽  
Mode Decomposition

scholarly journals Estimation of Heart Rate and Respiratory Rate from PPG Signal Using Complementary Ensemble Empirical Mode Decomposition with both Independent Component Analysis and Non-Negative Matrix Factorization

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3238
Author(s):  
Ruisheng Lei ◽  
Bingo Wing-Kuen Ling ◽  
Peihua Feng ◽  
Jinrong Chen
Keyword(s):  
Heart Rate ◽  
Independent Component Analysis ◽  
Respiratory Rate ◽  
Empirical Mode Decomposition ◽  
Matrix Factorization ◽  
Component Analysis ◽  
Ensemble Empirical Mode Decomposition ◽  
Independent Component ◽  
Mode Decomposition ◽  
Non Negative Matrix Factorization

This paper proposes a framework combining the complementary ensemble empirical mode decomposition with both the independent component analysis and the non-negative matrix factorization for estimating both the heart rate and the respiratory rate from the photoplethysmography (PPG) signal. After performing the complementary ensemble empirical mode decomposition on the PPG signal, a finite number of intrinsic mode functions are obtained. Then, these intrinsic mode functions are divided into two groups to perform the further analysis via both the independent component analysis and the non-negative matrix factorization. The surrogate cardiac signal related to the heart activity and another surrogate respiratory signal related to the respiratory activity are reconstructed to estimate the heart rate and the respiratory rate, respectively. Finally, different records of signals acquired from the Medical Information Mart for Intensive Care database downloaded from the Physionet Automated Teller Machine (ATM) data bank are employed for demonstrating the outperformance of our proposed method. The results show that our proposed method outperforms both the digital filtering approach and the conventional empirical mode decomposition based methods in terms of reconstructing both the surrogate cardiac signal and the respiratory signal from the PPG signal as well as both achieving the higher accuracy and the higher reliability for estimating both the heart rate and the respiratory rate.

scholarly journals A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition

2018 ◽  
Vol 23 (3) ◽  
pp. 269-280 ◽  
Author(s):  
S. Sengottuvel ◽  
Pathan Fayaz Khan ◽  
N. Mariyappa ◽  
Rajesh Patel ◽  
S. Saipriya ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Empirical Mode Decomposition ◽  
Slow Wave ◽  
Cardiac Activity ◽  
Component Analysis ◽  
Ensemble Empirical Mode Decomposition ◽  
Motion Artifacts ◽  
Independent Component ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition

Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.

scholarly journals A Novel Method Based on Combination of Independent Component Analysis and Ensemble Empirical Mode Decomposition for Removing Electrooculogram Artifacts From Multichannel Electroencephalogram Signals

2021 ◽  
Vol 15 ◽  
Author(s):  
Chao-Lin Teng ◽  
Yi-Yang Zhang ◽  
Wei Wang ◽  
Yuan-Yuan Luo ◽  
Gang Wang ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Empirical Mode Decomposition ◽  
High Performance ◽  
Component Analysis ◽  
Ensemble Empirical Mode Decomposition ◽  
Independent Component ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Mode Decomposition ◽  
Multichannel Eeg

Electrooculogram (EOG) is one of common artifacts in recorded electroencephalogram (EEG) signals. Many existing methods including independent component analysis (ICA) and wavelet transform were applied to eliminate EOG artifacts but ignored the possible impact of the nature of EEG signal. Therefore, the removal of EOG artifacts still faces a major challenge in EEG research. In this paper, the ensemble empirical mode decomposition (EEMD) and ICA algorithms were combined to propose a novel EEMD-based ICA method (EICA) for removing EOG artifacts from multichannel EEG signals. First, the ICA method was used to decompose original EEG signals into multiple independent components (ICs), and the EOG-related ICs were automatically identified through the kurtosis method. Then, by performing the EEMD algorithm on EOG-related ICs, the intrinsic mode functions (IMFs) linked to EOG were discriminated and eliminated. Finally, artifact-free IMFs were projected to obtain the ICs without EOG artifacts, and the clean EEG signals were ultimately reconstructed by the inversion of ICA. Both EOGs correction from simulated EEG signals and real EEG data were studied, which verified that the proposed method could achieve an improved performance in EOG artifacts rejection. By comparing with other existing approaches, the EICA obtained the optimal performance with the highest increase in signal-to-noise ratio and decrease in root mean square error and correlation coefficient after EOG artifacts removal, which demonstrated that the proposed method could more effectively eliminate blink artifacts from multichannel EEG signals with less error influence. This study provided a novel promising method to eliminate EOG artifacts with high performance, which is of great importance for EEG signals processing and analysis.

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