An Improved SSA-Based Technique for EMG Removal from ECG

Mortezaee, Marziyeh; Mortezaie, Zahra; Abolghasemi, Vahid

doi:10.1016/j.irbm.2018.11.004

Cited by 24 publications

(5 citation statements)

References 8 publications

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“…[ 101 ] M. Mortezaee et al use singular spectrum analysis to eliminate ECG interference, whose method needs no parameters and is model‐free, which can be applied to decompose slowly time‐varying signals. [ 107 ]…”

Section: Emg Signal Processing Algorithmsmentioning

confidence: 99%

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

Gao

Gong

Chen

et al. 2022

Advanced Intelligent Systems

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Electromyography (EMG) is an integral part of many biomedical and healthcare applications. It has been used as a metric for tracking rehabilitation progress and identifying diseases that affect muscle activation patterns. Although it is widely used in many disciplines, conventional EMG recording and interpretation techniques lack in providing precise signal detection and robust classification accuracy. In recent years, thanks to advances in both material science and artificial intelligence, EMG detection techniques are improving at a rapid pace. Materials that allow for enhanced biocompatibility have improved the quality of data recorded by electrodes. The use of machine learning algorithms has paved new ways to understand complex EMG signals, triggering diverse, novel, and improved application scenarios within the healthcare framework. To help readers establish a clear picture of the two most important components in EMG technology, i.e., electrodes and algorithms, while catching up with the latest research outcomes, this review article is composed. The article starts by introducing conventional EMG electrode materials and architectures, then explains how state‐of‐the‐art works have improved electrode utilization. Subsequently, EMG signal conditioning and interpretation algorithms are investigated. Finally, current challenges in the research domain and authors’ perspectives are discussed.

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Section: Emg Signal Processing Algorithmsmentioning

confidence: 99%

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

Gao

Gong

Chen

et al. 2022

Advanced Intelligent Systems

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“…In [23], a brandnew 2D motion-compensated rebuilding technique for coronary arteries is suggested. To distinguish the ECG signals from EMG sounds, the authors of [24] presented a method dependent on SSA. The method for identifying high-risk plaque proposed in [25] involves the merging of three different types of data: global features of carotid ultrasound images, expert information from ultrasound reports, and echo features of regions of interests (ROI).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence based approach for categorization of COVID-19 ECG images in presence of other cardiovascular disorders

Chaitanya

Sharma

Rahul

et al. 2023

Biomed. Phys. Eng. Express

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Coronavirus disease (COVID-19) is a class of SARS-CoV-2 virus which is initially identified in the later half of the year 2019 and then evolved as a pandemic. If it is not identified in the early stage then the infection and mortality rates increase with time. A timely and reliable approach for COVID-19 identification has become important in order to prevent the disease from spreading rapidly. In recent times, many methods have been suggested for the detection of COVID-19 disease have various flaws, to increase diagnosis performance, fresh investigations are required. In this article, automatically diagnosing COVID-19 using ECG images and deep learning approaches like as Visual Geometry Group (VGG) and AlexNet architectures have been proposed. The proposed method is able to classify between COVID-19, myocardial infarction, normal sinus rhythm, and other abnormal heart beats using Lead-II ECG image only. The efficacy of the technique proposed is validated by using a publicly available ECG image database. We have achieved an accuracy of 77.42% using Alexnet model and 75% accuracy with the help of VGG19 model.

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“…Neurofeedback training converts the electroencephalogram (EEG) data into visual and auditory signals, and participants selectively enhance or inhibit EEG frequency during training to achieve brain regulation (Monderer et al, 2002). NFT has been successfully applied to brain function-related diseases such as attention deficit hyperactivity disorder (ADHD) (Deiber et al, 2020;Janssen et al, 2020), autistic spectrum disorder (ASD) (Kang et al, 2020), and epilepsy (Ouyang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Brain Function State During Mild Cognitive Impairment Based on Weighted Multiple Multiscale Entropy

Liu

et al. 2021

Front. Aging Neurosci.

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The mild cognitive impairment (MCI) stage plays an essential role in preventing the progression of older adults to Alzheimer's disease. In this study, neurofeedback training (NFT) is applied to improve MCI brain cognitive function. To assess the improvement effect, a novel algorithm called Weighted Multiple Multiscale Entropy (WMMSE) is proposed to extract and analyze the electroencephalogram (EEG) features of patients with MCI. To overcome the information loss problem of traditional multiscale entropy (MSE), WMMSE fully considered the correlation of the sequence and the contribution of each sequence to the total entropy. The experimental group composed of 39 patients with MCI was subjected to NFT for 10 days during two sessions. The control group included 21 patients with MCI without any intervention. The Lempel-Ziv complexity (LZC) was used for primary assessment, and WMMSE was used to accurately analyze the effect of NFT. The results show that the WMMSE values of F4, C3, C4, O1, and T5 channels post-NFT are higher compared with pre-NFT and significant differences (P < 0.05). Moreover, the cognitive subscale of the Montreal Cognitive Assessment (MoCA) results shows that the post-NFT score is higher than the pre-NFT in the vast majority of the patients with MCI and significant differences (P < 0.05). When compared with the control group, the WMMSE values of the experimental group increased in each channel. Therefore, the NFT intervention method contributes to brain cognitive functional recovery, and WMMSE can be used as a biomarker to evaluate the state of MCI brain cognitive function.

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An Improved SSA-Based Technique for EMG Removal from ECG

Cited by 24 publications

References 8 publications

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

Artificial intelligence based approach for categorization of COVID-19 ECG images in presence of other cardiovascular disorders

Evaluation of the Brain Function State During Mild Cognitive Impairment Based on Weighted Multiple Multiscale Entropy

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