Dataset Information

Comparison of Motion Artefact Reduction Methods and the Implementation of Adaptive Motion Artefact Reduction in Wearable Electrocardiogram Monitoring.

ABSTRACT: A motion artefact is a kind of noise that exists widely in wearable electrocardiogram (ECG) monitoring. Reducing motion artefact is challenging in ECG signal preprocessing because the spectrum of motion artefact usually overlaps with the very important spectral components of the ECG signal. In this paper, the performance of the finite impulse response (FIR) filter, infinite impulse response (IIR) filter, moving average filter, moving median filter, wavelet transform, empirical mode decomposition, and adaptive filter in motion artefact reduction is studied and compared. The results of this study demonstrate that the adaptive filter performs better than other denoising methods, especially in dealing with the abnormal ECG signal which is measured from a patient with heart disease. In the implementation of adaptive motion artefact reduction, the results show that the use of the impedance pneumography signal as the reference input signal for the adaptive filter can effectively reduce the motion artefact in the ECG signal.

SUBMITTER: An X

PROVIDER: S-EPMC7085712 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Comparison of Motion Artefact Reduction Methods and the Implementation of Adaptive Motion Artefact Reduction in Wearable Electrocardiogram Monitoring.

An Xiang X K Stylios George G

Sensors (Basel, Switzerland) 20200307 5

A motion artefact is a kind of noise that exists widely in wearable electrocardiogram (ECG) monitoring. Reducing motion artefact is challenging in ECG signal preprocessing because the spectrum of motion artefact usually overlaps with the very important spectral components of the ECG signal. In this paper, the performance of the finite impulse response (FIR) filter, infinite impulse response (IIR) filter, moving average filter, moving median filter, wavelet transform, empirical mode decomposition ...[more]

PMID: 32155984

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Project description:BackgroundMobility impairment is common in people with multiple sclerosis (PwMS) and there is a need to assess mobility in remote settings. Here, we apply a novel wireless, skin-mounted, and conformal inertial sensor (BioStampRC, MC10 Inc.) to examine gait characteristics of PwMS under controlled conditions. We determine the accuracy and precision of BioStampRC in measuring gait kinematics by comparing to contemporary research-grade measurement devices.MethodsA total of 45 PwMS, who presented with diverse walking impairment (Mild MS = 15, Moderate MS = 15, Severe MS = 15), and 15 healthy control subjects participated in the study. Participants completed a series of clinical walking tests. During the tests participants were instrumented with BioStampRC and MTx (Xsens, Inc.) sensors on their shanks, as well as an activity monitor GT3X (Actigraph, Inc.) on their non-dominant hip. Shank angular velocity was simultaneously measured with the inertial sensors. Step number and temporal gait parameters were calculated from the data recorded by each sensor. Visual inspection and the MTx served as the reference standards for computing the step number and temporal parameters, respectively. Accuracy (error) and precision (variance of error) was assessed based on absolute and relative metrics. Temporal parameters were compared across groups using ANOVA.ResultsMean accuracy±precision for the BioStampRC was 2±2 steps error for step number, 6±9ms error for stride time and 6±7ms error for step time (0.6-2.6% relative error). Swing time had the least accuracy±precision (25±19ms error, 5±4% relative error) among the parameters. GT3X had the least accuracy±precision (8±14% relative error) in step number estimate among the devices. Both MTx and BioStampRC detected significantly distinct gait characteristics between PwMS with different disability levels (p<0.01).ConclusionBioStampRC sensors accurately and precisely measure gait parameters in PwMS across diverse walking impairment levels and detected differences in gait characteristics by disability level in PwMS. This technology has the potential to provide granular monitoring of gait both inside and outside the clinic.

Project description:BackgroundAs more people are surviving stroke, there is a growing need for services and programs that support the long-term needs of people living with the effects of stroke. Exercise has many benefits; however, most people with stroke do not have access to specialized exercise programs that meet their needs in their communities. To catalyze the implementation of these programs, our team developed the Stroke Recovery in Motion Implementation Planner, an evidence-informed implementation guide for teams planning a community-based exercise program for people with stroke.ObjectiveThis study aimed to conduct a user evaluation to elicit user perceptions of the usefulness and acceptability of the Planner to inform revisions.MethodsThis mixed methods study used a concurrent triangulation design. We used purposive sampling to enroll a diverse sample of end users (program managers and coordinators, rehabilitation health partners, and fitness professionals) from three main groups: those who are currently planning a program, those who intend to plan a program in the future, and those who had previously planned a program. Participants reviewed the Planner and completed a questionnaire and interviews to identify positive features, areas of improvement, value, and feasibility. We used descriptive statistics for quantitative data and content analysis for qualitative data. We triangulated the data sources to identify Planner modifications.ResultsA total of 39 people participated in this study. Overall, the feedback was positive, highlighting the value of the Planner's comprehensiveness, tools and templates, and real-world examples. The identified areas for improvement included clarifying the need for specific steps, refining navigation, and creating more action-oriented content. Most participants reported an increase in knowledge and confidence after reading the Planner and reported that using the resource would improve their planning approach.ConclusionsWe used a rigorous and user-centered process to develop and evaluate the Planner. End users indicated that it is a valuable resource and identified specific changes for improvement. The Planner was subsequently updated and is now publicly available for community planning teams to use in the planning and delivery of evidence-informed, sustainable, community-based exercise programs for people with stroke.

Dataset Information

Comparison of Motion Artefact Reduction Methods and the Implementation of Adaptive Motion Artefact Reduction in Wearable Electrocardiogram Monitoring.

Publications

Comparison of Motion Artefact Reduction Methods and the Implementation of Adaptive Motion Artefact Reduction in Wearable Electrocardiogram Monitoring.

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