A Comparison of Two Methods for Determining the Extracellular Potential Field of an Isolated Purkinje Strand in a Volume Conductor

1975 ◽  
Vol BME-22 (3) ◽  
pp. 174-183 ◽  
Author(s):  
Thomas L. Harman ◽  
T. F. Liebfried ◽  
John W. Clark ◽  
C. Wayne Hibbs
Keyword(s):  
Potential Field ◽  
Volume Conductor ◽  
Extracellular Potential

Potential Field from an Active Nerve in an Inhomogeneous, Anisotropic Volume Conductor - The Forward Problem

1985 ◽  
Vol BME-32 (12) ◽  
pp. 1032-1041 ◽  
Author(s):  
Owen B. Wilson ◽  
John W. Clark ◽  
Nirmala Ganapathy ◽  
T. L. Harman
Keyword(s):  
Potential Field ◽  
Forward Problem ◽  
Volume Conductor

Extracellular potential field of antidromically activated CA1 pyramidal neurons

1967 ◽  
Vol 3 (4) ◽  
pp. 343-361 ◽  
Author(s):  
Luigi Sperti ◽  
Tiziana Gessi ◽  
Firmina Volta
Keyword(s):  
Potential Field ◽  
Pyramidal Neurons ◽  
Extracellular Potential ◽  
Ca1 Pyramidal Neurons

Potential Field from an Active Nerve in an Inhomogeneous, Anisotropic Volume Conductor - The Inverse Problem

1985 ◽  
Vol BME-32 (12) ◽  
pp. 1042-1051 ◽  
Author(s):  
Owen B. Wilson ◽  
John W. Clark ◽  
Nirmala Ganapathy ◽  
T. L. Harman
Keyword(s):  
Inverse Problem ◽  
Potential Field ◽  
Volume Conductor

Galactic orbits of stars

1966 ◽  
Vol 25 ◽  
pp. 93-97
Author(s):  
Richard Woolley
Keyword(s):  
Globular Cluster ◽  
Potential Field ◽  
High Velocity ◽  
Velocity Vector ◽  
Variable Stars ◽  
The Sun ◽  
Proper Motions ◽  
Rr Lyrae ◽  
The Galaxy

It is now possible to determine proper motions of high-velocity objects in such a way as to obtain with some accuracy the velocity vector relevant to the Sun. If a potential field of the Galaxy is assumed, one can compute an actual orbit. A determination of the velocity of the globular clusterωCentauri has recently been completed at Greenwich, and it is found that the orbit is strongly retrograde in the Galaxy. Similar calculations may be made, though with less certainty, in the case of RR Lyrae variable stars.

The Role of Computer Modeling in Electrocardiography

1990 ◽  
Vol 29 (04) ◽  
pp. 282-288 ◽  
Author(s):  
A. van Oosterom
Keyword(s):  
Electrical Activity ◽  
Computer Modeling ◽  
Body Surface ◽  
Electrical Current ◽  
The Body ◽  
Volume Conductor ◽  
Current Generator ◽  
Cardiac Electrical Activity ◽  
Source Models

AbstractThis paper introduces some levels at which the computer has been incorporated in the research into the basis of electrocardiography. The emphasis lies on the modeling of the heart as an electrical current generator and of the properties of the body as a volume conductor, both playing a major role in the shaping of the electrocardiographic waveforms recorded at the body surface. It is claimed that the Forward-Problem of electrocardiography is no longer a problem. Several source models of cardiac electrical activity are considered, one of which can be directly interpreted in terms of the underlying electrophysiology (the depolarization sequence of the ventricles). The importance of using tailored rather than textbook geometry in inverse procedures is stressed.

Single Cell Based Microelectrode Array Biosensors

2003 ◽  
Vol 773 ◽  
Author(s):  
Mo Yang ◽  
Shalini Prasad ◽  
Xuan Zhang ◽  
Mihrimah Ozkan ◽  
Cengiz S. Ozkan
Keyword(s):  
Electrical Activity ◽  
Single Cell ◽  
Cellular Response ◽  
Microelectrode Array ◽  
Fast Fourier Transformation ◽  
Chemical Agent ◽  
Extracellular Potential ◽  
Membrane Excitability ◽  
Specific Chemical ◽  
Chemical Agents

AbstractExtracellular potential is an important parameter which indicates the electrical activity of live cells. Membrane excitability in osteoblasts plays a key role in modulating the electrical activity in the presence of chemical agents. The complexity of cell signal makes interpretation of the cellular response to a chemical agent very difficult. By analyzing shifts in the signal power spectrum, it is possible to determine a frequency spectrum also known as Signature Pattern Vectors (SPV) specific to a chemical. It is also essential to characterize single cell sensitivity and response time for specific chemical agents for developing detect-to-warn biosensors. We used a 4x4 multiple Pt microelectrode array to spatially position single osteoblast cells, by using a gradient AC field. Fast Fourier Transformation (FFT) and Wavelet Transformation (WT) analyses were used to extract information pertaining to the frequency of firing from the extracellular potential.

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