Literature DB >> 24723634

Epsilon-tube filtering: reduction of high-amplitude motion artifacts from impedance plethysmography signal.

Sardar Ansari, Kevin Ward, Kayvan Najarian.   

Abstract

The impedance plethysmography (IP) has long been used to monitor respiration. The IP signal is also suitable for portable monitoring of respiration due to its simplicity. However, this signal is very susceptible to motion artifact (MA). As a result, MA reduction is an indispensable part of portable acquisition of the IP signal. Often, the amplitude of the MA is much larger than the amplitude of the respiratory component in the IP signal. This study proposes a novel filtering method to remove the high-amplitude MA's from the IP signal. The proposed method combines the idea of ε-tube loss function and an autoregressive exogenous model to estimate the MA while leaving the periodic respiratory component of the IP signal intact. Also, a regularization method is used to find the best filter coefficients that maximize the regularity of the output signal. The results indicate that the proposed method can effectively remove the MA, outperforming the popular MA reduction methods. Several different performance measures are used for the comparison and the differences are found to be statistically significant.

Entities:  

Mesh:

Year:  2014        PMID: 24723634     DOI: 10.1109/JBHI.2014.2316287

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  4 in total

1.  Virtual Spirometry and Activity Monitoring Using Multichannel Electrical Impedance Plethysmographs in Ambulatory Settings.

Authors:  Hassan Aqeel Khan; Amit Gore; Jeffrey Ashe; Shantanu Chakrabartty
Journal:  IEEE Trans Biomed Circuits Syst       Date:  2017-05-23       Impact factor: 3.833

2.  Automatic Detection of Ventilations During Mechanical Cardiopulmonary Resuscitation.

Authors:  Xabier Jaureguibeitia; Unai Irusta; Elisabete Aramendi; Pamela C Owens; Henry E Wang; Ahamed H Idris
Journal:  IEEE J Biomed Health Inform       Date:  2020-01-17       Impact factor: 5.772

3.  Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia.

Authors:  Elyas Sabeti; Jonathan Gryak; Harm Derksen; Craig Biwer; Sardar Ansari; Howard Isenstein; Anna Kratz; Kayvan Najarian
Journal:  Entropy (Basel)       Date:  2019-04-28       Impact factor: 2.524

4.  Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach.

Authors:  Jonathan Moeyersons; John Morales; Nick Seeuws; Chris Van Hoof; Evelien Hermeling; Willemijn Groenendaal; Rik Willems; Sabine Van Huffel; Carolina Varon
Journal:  Sensors (Basel)       Date:  2021-04-08       Impact factor: 3.576

  4 in total

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