scholarly journals Separation of Subcutaneous Fat From Muscle in Surface Electrical Impedance Myography Measurements Using Model Component Analysis

2019 ◽  
Vol 66 (2) ◽  
pp. 354-364 ◽  
Author(s):  
Hyeuknam Kwon ◽  
Wasim Q. Malik ◽  
Seward B. Rutkove ◽  
Benjamin Sanchez
Keyword(s):  
Electrical Impedance ◽  
Subcutaneous Fat ◽  
Component Analysis ◽  
Model Component

scholarly journals Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Kyounghun Lee ◽  
Minha Yoo ◽  
Ariungerel Jargal ◽  
Hyeuknam Kwon
Keyword(s):  
Deep Learning ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Subcutaneous Fat ◽  
Estimation Method ◽  
Training Dataset ◽  
Strong Nonlinearity ◽  
Learning Method ◽  
Impedance Tomography ◽  
Reconstruction Methods

This paper proposes a deep learning method based on electrical impedance tomography (EIT) to estimate the thickness of abdominal subcutaneous fat. EIT for evaluating the thickness of abdominal subcutaneous fat is an absolute imaging problem that aims at reconstructing conductivity distributions from current-to-voltage data. Existing reconstruction methods based on EIT have difficulty handling the inherent drawbacks of strong nonlinearity and severe ill-posedness of EIT; hence, absolute imaging may not be possible using linearized methods. To handle nonlinearity and ill-posedness, we propose a deep learning method that finds useful solutions within a restricted admissible set by accounting for prior information regarding abdominal anatomy. We determined that a specially designed training dataset used during the deep learning process significantly reduces ill-posedness in the absolute EIT problem. In the preprocessing stage, we normalize current-voltage data to alleviate the effects of electrodeposition and body geometry by exploiting knowledge regarding electrode positions and body geometry. The performance of the proposed method is demonstrated through numerical simulations and phantom experiments using a 10 channel EIT system and a human-like domain.

scholarly journals Extraction of cardiac and respiration signals in electrical impedance tomography based on independent component analysis

2019 ◽  
Vol 4 (1) ◽  
pp. 38-44 ◽  
Author(s):  
T. Rahman ◽  
M.M Hasan ◽  
A. Farooq ◽  
M. Z. Uddin
Keyword(s):  
Independent Component Analysis ◽  
Electrical Impedance Tomography ◽  
Spontaneous Breathing ◽  
Electrical Impedance ◽  
Random Noise ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Component ◽  
Impedance Tomography ◽  
Wide Range

Abstract Electrical Impedance Tomography (EIT) has successive wide range in impedance imaging, but still it is difficult to extract cardiac-related conductivity changes and respiratory-related conductivity changes in spontaneous breathing subjects. Quite a few methods are attempted to extract these two signals such as electrocardiogram gated averaging, frequency domain filtering and principal component analysis. However, such methods are not able to take apart these components properly or put some effort in real time imaging and have their own limitations. The purpose of this paper is to introduce a new method in the EIT clinical application field, Independent Component Analysis (ICA) to extract cardiac and respiratory related signals in electrical impedance tomography. Independent component analysis has been introduced to use in electrical impedance tomography but this is the first attempt ever to implement this method to separate these two signals and image those independent conductivity distribution of respiration and cardiac changes independently. Data has been collected from a spontaneous breathing subject. Filtration technique has been used to remove random noise and multi level spatial ICA has been applied to obtain independent component signals which has been later used in reconstruction algorithm for imaging.

scholarly journals The effect of subcutaneous fat on electrical impedance myography when using a handheld electrode array: The case for measuring reactance

2013 ◽  
Vol 124 (2) ◽  
pp. 400-404 ◽  
Author(s):  
Minhee Sung ◽  
Andrew J. Spieker ◽  
Pushpa Narayanaswami ◽  
Seward B. Rutkove
Keyword(s):  
Electrical Impedance ◽  
Subcutaneous Fat ◽  
Electrode Array

Towards Zero Retraining for Myoelectric Control Based on Common Model Component Analysis

2016 ◽  
Vol 24 (4) ◽  
pp. 444-454 ◽  
Author(s):  
Jianwei Liu ◽  
Xinjun Sheng ◽  
Dingguo Zhang ◽  
Ning Jiang ◽  
Xiangyang Zhu
Keyword(s):  
Component Analysis ◽  
Myoelectric Control ◽  
Model Component

scholarly journals The Effect of Subcutaneous Fat on Electrical Impedance Myography: Electrode Configuration and Multi-Frequency Analyses

PLoS ONE ◽  
2016 ◽  
Vol 11 (5) ◽  
pp. e0156154 ◽  
Author(s):  
Le Li ◽  
Xiaoyan Li ◽  
Huijing Hu ◽  
Henry Shin ◽  
Ping Zhou
Keyword(s):  
Electrical Impedance ◽  
Subcutaneous Fat ◽  
Electrode Configuration
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