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.

Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

2020 ◽  
Vol 2020 (14) ◽  
pp. 357-1-357-6
Author(s):  
Luisa F. Polanía ◽  
Raja Bala ◽  
Ankur Purwar ◽  
Paul Matts ◽  
Martin Maltz
Keyword(s):  
Independent Component Analysis ◽  
Spectral Reflectance ◽  
Source Separation ◽  
Principal Component ◽  
Component Analysis ◽  
Previous Method ◽  
Independent Component ◽  
Light Transport ◽  
Camera Model ◽  
Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

scholarly journals Evaluation of Regional Pulmonary Ventilation in Spontaneously Breathing Patients with Idiopathic Pulmonary Fibrosis (IPF) Employing Electrical Impedance Tomography (EIT): A Pilot Study from the European IPF Registry (eurIPFreg)

2021 ◽  
Vol 10 (2) ◽  
pp. 192
Author(s):  
Ekaterina Krauss ◽  
Daniel van der Beck ◽  
Isabel Schmalz ◽  
Jochen Wilhelm ◽  
Silke Tello ◽  
...  
Keyword(s):  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Electrical Impedance Tomography ◽  
Spontaneous Breathing ◽  
Electrical Impedance ◽  
Statistical Significance ◽  
Pulmonary Ventilation ◽  
Healthy Controls ◽  
Impedance Tomography ◽  
Spontaneously Breathing

Objectives: In idiopathic pulmonary fibrosis (IPF), alterations in the pulmonary surfactant system result in an increased alveolar surface tension and favor repetitive alveolar collapse. This study aimed to assess the usefulness of electrical impedance tomography (EIT) in characterization of regional ventilation in IPF. Materials and methods: We investigated 17 patients with IPF and 15 healthy controls from the University of Giessen and Marburg Lung Center (UGMLC), Germany, for differences in the following EIT parameters: distribution of ventilation (TID), global inhomogeneity index (GI), regional impedance differences through the delta of end-expiratory lung impedance (dEELI), differences in surface of ventilated area (SURF), as well as center of ventilation (CG) and intratidal gas distribution (ITV). These parameters were assessed under spontaneous breathing and following a predefined escalation protocol of the positive end-expiratory pressure (PEEP), applied through a face mask by an intensive care respirator (EVITA, Draeger, Germany). Results: Individual slopes of dEELI over the PEEP increment protocol were found to be highly significantly increased in both groups (p < 0.001) but were not found to be significantly different between groups. Similarly, dTID slopes were increasing in response to PEEP, but this did not reach statistical significance within or between groups. Individual breathing patterns were very heterogeneous. There were no relevant differences of SURF, GI or CGVD over the PEEP escalation range. A correlation of dEELI to FVC, BMI, age, or weight did not forward significant results. Conclusions: In this study, we did see a significant increase in dEELI and a non-significant increase in dTID in IPF patients as well as in healthy controls in response to an increase of PEEP under spontaneous breathing. We propose the combined measurements of EIT and lung function to assess regional lung ventilation in spontaneously breathing subjects.

scholarly journals Improving gene function predictions using independent transcriptional components

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Carlos G. Urzúa-Traslaviña ◽  
Vincent C. Leeuwenburgh ◽  
Arkajyoti Bhattacharya ◽  
Stefan Loipfinger ◽  
Marcel A. T. M. van Vugt ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
High Throughput Sequencing ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Component ◽  
Sequencing Data ◽  
New Members ◽  
High Throughput Sequencing Data ◽  
Gene Sets ◽  
Functional Understanding

AbstractThe interpretation of high throughput sequencing data is limited by our incomplete functional understanding of coding and non-coding transcripts. Reliably predicting the function of such transcripts can overcome this limitation. Here we report the use of a consensus independent component analysis and guilt-by-association approach to predict over 23,000 functional groups comprised of over 55,000 coding and non-coding transcripts using publicly available transcriptomic profiles. We show that, compared to using Principal Component Analysis, Independent Component Analysis-derived transcriptional components enable more confident functionality predictions, improve predictions when new members are added to the gene sets, and are less affected by gene multi-functionality. Predictions generated using human or mouse transcriptomic data are made available for exploration in a publicly available web portal.

Design and Validation of a New Programmable Current Source for Electrical Impedance Tomography Applications

2019 ◽  
Vol 17 (9) ◽  
pp. 688-695
Author(s):  
Ramesh Kumar ◽  
Sharvan Kumar ◽  
A. Sengupta
Keyword(s):  
Image Reconstruction ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Current Source ◽  
Reconstruction Algorithm ◽  
Simulation Software ◽  
Constant Current ◽  
Impedance Tomography ◽  
Constant Current Source ◽  
Wide Range

This paper proposed an advanced digital voltage-controlled multi-frequency based constant current source, which is a wide range of loads and high SNR ratio for Electrical Impedance Tomography (EIT) application. In EIT a constant current source is required for injecting a sinusoidal current pulse to the phantom boundary. The boundary potentials are measured by inserting content current from the phantom boundary according to the variation in frequency and current levels. For studying the wide range of tissue conductivity among different type of subjects (the multi-frequency scanning) is desired in medical Electrical impedance tomography. The proposed Current source, which shows that the simulation has good performance at multi-frequency range with accuracy and stability. In proteus simulation software, the results show that the proposed circuit presents a more stable impedance output and the obtained boundary data at multi-frequency for the validation of the obtained data has been shown using suitable image reconstruction algorithm and is found suitable for image reconstruction much easier.

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