Literature DB >> 20371416

Evaluating the lower-body electromyogram signal acquired from the feet as a noise reference for standing ballistocardiogram measurements.

Omer T Inan1, Gregory T A Kovacs, Laurent Giovangrandi.   

Abstract

The ballistocardiogram (BCG) is a measure of the reaction force of the body to cardiac ejection of blood. A variety of systems can be used for BCG detection, including beds, tables, chairs, and weighing scales. Weighing scales, in particular, have several practical advantages over the alternatives: low cost, small size, unobtrusiveness, and familiarity to the user; one disadvantage is that the subject must stand during the recording, rather than sit or lay supine, resulting in a higher susceptibility to motion artifacts in the measured signal. This paper evaluates the electromyogram (EMG) signal acquired from the feet of the subject during BCG recording as a noise reference for standing BCG measurements. As a subject moves while standing on the scale, muscle contractions in the feet are detected by the EMG signal, and used to flag segments of the BCG signal that are corrupted by elevated noise. For the purposes of evaluating this method, estimates of the BCG noise-to-signal ratio (NSR) were independently calculated with an ensemble average method, using the R-wave of a simultaneously-acquired chest ECG as a timing reference. The linear correlation between EMG power alone and BCG NSR from 14 subjects was found to be moderate ( r = 0.58, F-statistic p -value 0.05); combined with body-mass index (BMI), multiple linear regression yielded a stronger correlation ( r = 0.73, F -statistic p-value = 0.01). Additionally, an example usage of the lower-leg EMG for improving BCG measurement robustness is provided.

Mesh:

Year:  2010        PMID: 20371416     DOI: 10.1109/TITB.2010.2044185

Source DB:  PubMed          Journal:  IEEE Trans Inf Technol Biomed        ISSN: 1089-7771


  4 in total

1.  Automatic detection of motion artifacts in the ballistocardiogram measured on a modified bathroom scale.

Authors:  Richard M Wiard; Omer T Inan; Brian Argyres; Mozziyar Etemadi; Gregory T A Kovacs; Laurent Giovangrandi
Journal:  Med Biol Eng Comput       Date:  2010-12-09       Impact factor: 2.602

2.  Toward continuous, noninvasive assessment of ventricular function and hemodynamics: wearable ballistocardiography.

Authors:  Andrew D Wiens; Mozziyar Etemadi; Shuvo Roy; Liviu Klein; Omer T Inan
Journal:  IEEE J Biomed Health Inform       Date:  2014-09-23       Impact factor: 5.772

3.  Wearable Cuff-Less Blood Pressure Estimation at Home via Pulse Transit Time.

Authors:  Venu G Ganti; Andrew M Carek; Brandi N Nevius; J Alex Heller; Mozziyar Etemadi; Omer T Inan
Journal:  IEEE J Biomed Health Inform       Date:  2021-06-03       Impact factor: 7.021

4.  Motion Artifact Quantification and Sensor Fusion for Unobtrusive Health Monitoring.

Authors:  Christoph Hoog Antink; Florian Schulz; Steffen Leonhardt; Marian Walter
Journal:  Sensors (Basel)       Date:  2017-12-25       Impact factor: 3.576

  4 in total

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