Improved ICA algorithm for ECG feature extraction and R‐peak detection

Jayasanthi, M.; Ramamoorthy, V.; Parthiban, A.

doi:10.1002/acs.3186

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…Mean Subtraction is subtracting its mean from the observed vector. That is, transform the observation vector ( Dong et al, 2020 ; Jayasanthi et al, 2020 ).

into zero mean vector which can be represented by the following formula:

…”

Section: Methodsmentioning

confidence: 99%

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Lu,

Gong,

Xue

et al. 2024

Front. Bioeng. Biotechnol.

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Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method.Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination.Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

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“…Mean Subtraction is subtracting its mean from the observed vector. That is, transform the observation vector ( Dong et al, 2020 ; Jayasanthi et al, 2020 ).

into zero mean vector which can be represented by the following formula:

…”

Section: Methodsmentioning

confidence: 99%

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Lu,

Gong,

Xue

et al. 2024

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…erefore, using some physiological signals to identify and classify emotions has become a research hotspot for experts and scholars, such as ECG, pulse, blood pressure, respiration, skin impedance, and other physiological signals. American researchers led their experimental team to prove that physiological signals can identify and classify emotions first [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Key Feature Extraction Method of Electroencephalogram Signal by Independent Component Analysis for Athlete Selection and Training

Huang

Cheng

Liu

2022

Computational Intelligence and Neuroscience

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Emotion is an important expression generated by human beings to external stimuli in the process of interaction with the external environment. It affects all aspects of our lives all the time. Accurate identification of human emotional states and further application in artificial intelligence can better improve and assist human life. Therefore, the research on emotion recognition has attracted the attention of many scholars in the field of artificial intelligence in recent years. Brain electrical signal conversion becomes critical, and it needs a brain electrical signal processing method to extract the effective signal to realize the human-computer interaction However, nonstationary nonlinear characteristics of EEG signals bring great challenge in characteristic signal extraction. At present, although there are many feature extraction methods, none of them can reflect the global feature of the signal. The following solutions are used to solve the above problems: (1) this paper proposed an ICA and sample entropy algorithm-based framework for feature extraction of EEG signals, which has not been applied for EEG and (2) simulation signals were used to verify the feasibility of this method, and experiments were carried out on two real-world data sets, to show the advantages of the new algorithm in feature extraction of EEG signals.

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Comparative Assessment of BSS Techniques for Non-Invasive Extraction of Fetal ECG from Abdominal ECG Signal

Kaur

Dewan

2023

J. Inst. Eng. India Ser. B

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Improved ICA algorithm for ECG feature extraction and R‐peak detection

Cited by 6 publications

References 22 publications

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Key Feature Extraction Method of Electroencephalogram Signal by Independent Component Analysis for Athlete Selection and Training

Comparative Assessment of BSS Techniques for Non-Invasive Extraction of Fetal ECG from Abdominal ECG Signal

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