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      Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter

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          Abstract

          The presence of motion artefacts in ECG signals can cause misleading interpretation of cardiovascular status. Recently, reducing the motion artefact from ECG signal has gained the interest of many researchers. Due to the overlapping nature of the motion artefact with the ECG signal, it is difficult to reduce motion artefact without distorting the original ECG signal. However, the application of an adaptive noise canceler has shown that it is effective in reducing motion artefacts if the appropriate noise reference that is correlated with the noise in the ECG signal is available. Unfortunately, the noise reference is not always correlated with motion artefact. Consequently, filtering with such a noise reference may lead to contaminating the ECG signal. In this paper, a two-stage filtering motion artefact reduction algorithm is proposed. In the algorithm, two methods are proposed, each of which works in one stage. The weighted adaptive noise filtering method (WAF) is proposed for the first stage. The acceleration derivative is used as motion artefact reference and the Pearson correlation coefficient between acceleration and ECG signal is used as a weighting factor. In the second stage, a recursive Hampel filter-based estimation method (RHFBE) is proposed for estimating the ECG signal segments, based on the spatial correlation of the ECG segment component that is obtained from successive ECG signals. Real-World dataset is used to evaluate the effectiveness of the proposed methods compared to the conventional adaptive filter. The results show a promising enhancement in terms of reducing motion artefacts from the ECG signals recorded by a cost-effective single lead ECG sensor during several activities of different subjects.

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          Most cited references 34

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          Adaptive noise cancelling: Principles and applications

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            Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection.

            Several adaptive filter structures are proposed for noise cancellation and arrhythmia detection. The adaptive filter essentially minimizes the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: baseline wander, 60 Hz power line interference, muscle noise, and motion artifact. An adaptive recurrent filter structure is proposed for acquiring the impulse response of the normal QRS complex. The primary input of the filter is the ECG signal to be analyzed, while the reference input is an impulse train coincident with the QRS complexes. This method is applied to several arrhythmia detection problems: detection of P-waves, premature ventricular complexes, and recognition of conduction block, atrial fibrillation, and paced rhythm.
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              Source separation using single channel ICA

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: Supervision
                Role: Funding acquisitionRole: Project administrationRole: Supervision
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                20 November 2018
                2018
                : 13
                : 11
                Affiliations
                [1 ] Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia, Johor, Malaysia
                [2 ] Department of Engineering, Computer and Electronics Engineering, Sana’a Community College, Sana’a, Yemen
                Indiana University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                PONE-D-17-34407
                10.1371/journal.pone.0207176
                6245678
                30457996
                © 2018 Ghaleb et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 15, Tables: 6, Pages: 31
                Product
                Funding
                Funded by: Ministry of Higher Education (MOHE) and Research Management Centre (RMC) at the Universiti Teknologi Malaysia (UTM)
                Award ID: VOT R.J130000.7828.4L852
                Award Recipient :
                This work is supported by the Ministry of Higher Education (MOHE) and Research Management Centre (RMC) at the Universiti Teknologi Malaysia (UTM), under the Trans-disciplinary Research Grant Scheme (TRGS) with VOT R.J130000.7828.4L852.* The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Engineering and Technology
                Signal Processing
                Signal Filtering
                Research and Analysis Methods
                Bioassays and Physiological Analysis
                Electrophysiological Techniques
                Cardiac Electrophysiology
                Electrocardiography
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Physical Sciences
                Physics
                Classical Mechanics
                Motion
                Engineering and Technology
                Signal Processing
                Noise Reduction
                Physical Sciences
                Mathematics
                Statistics
                Statistical Noise
                Engineering and Technology
                Signal Processing
                Signal Filtering
                Bandpass Filters
                Physical Sciences
                Chemistry
                Electrochemistry
                Electrode Potentials
                Custom metadata
                All relevant data are within the paper and its Supporting Information files are available in the following links. MHEALTH Dataset: can be downloaded from machine learning g repository in the following links https://archive.ics.uci.edu/ml/datasets/MHEALTH+Dataset. Source: Oresti Banos, Department of Computer Architecture and Computer Technology, University of Granada; Rafael Garcia, Department of Computer Architecture and Computer Technology, University of Granada; Alejandro Saez, Department of Computer Architecture and Computer Technology, University of Granada.

                Uncategorized

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