Electrical Characteristics of Injury Potentials

Cranefield, Paul F.; Eyster, J. A. E.; Gilson, Warren E.

doi:10.1152/ajplegacy.1951.167.2.450

Cited by 18 publications

(8 citation statements)

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“…While there is presumably some relationship between amplitudes of the two records which depends upon the diameter and passive electrical properties of the fiber, there seems to be little point in using the suction electrode to estimate the absolute value of transmembrane potentials. It is, however, interesting to note that the use of a complicated and difficult method of analyzing the potentials obtained with a suction electrode yielded a value of 97 mv for the transmembrane potential of turtle ventricle (15) which is comparable to the value of I IO mv obtained with microelectrodes.…”

Section: Discussionmentioning

confidence: 52%

Comparison of cardiac monophasic action potentials recorded by intracellular and suction electrodes

Hoffman

Cranefield

Lepeschkin

et al. 1959

American Journal of Physiology-Legacy Content

234

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The action potentials recorded from heart muscle with a suction electrode have been compared to those recorded with an intracellular microelectrode. It has been found that if the suction electrode is properly used the monophasic potentials recorded with it may be taken as a reliable index of the time of arrival of excitation at the electrode and as a reliable index of the shape of the action potential during the entire phase of repolarization. The suction electrode potentials differ from the microelectrode potentials in showing a lower rise velocity, a smaller amplitude, a quantitatively different reversal or overshoot and, in the beating heart, ‘afterpotentials’ caused by mechanical effects. When the shape of the action potential, as observed with the microelectrode, is changed by ions such as K+ or Ca++ a similar change is observed in the potential recorded with the suction electrodes.

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Section: Discussionmentioning

confidence: 52%

Comparison of cardiac monophasic action potentials recorded by intracellular and suction electrodes

Hoffman

Cranefield

Lepeschkin

et al. 1959

American Journal of Physiology-Legacy Content

234

View full text Add to dashboard Cite

show abstract

“…To reduce the influence of electrode movement on the computed NRMS, it is recommended to wait 2 seconds before recording. Another source of misleading information regarding the NRMS comes from neuronal injury potentials,17 produced when a neuron is damaged by the electrode. These cell injuries are characterized by high spike discharge that gradually fades, sometime over periods of more than 2 seconds.…”

Section: Discussionmentioning

confidence: 99%

“…An operating room is a noisy environment with respect to the recorded signal; therefore, it is crucial to ignore unstable, artifact‐containing sessions. Misleading RMS values that result from sources such as neuronal injury potential17 and other external artifacts were removed using a two‐step signal stability test. First, recorded sessions with maximum amplitudes exceeding 300 μV were rejected.…”

Section: Methodsmentioning

confidence: 99%

Real‐time refinement of subthalamic nucleus targeting using Bayesian decision‐making on the root mean square measure

et al. 2006

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The subthalamic nucleus (STN) is a major target for treatment of advanced Parkinson's disease patients undergoing deep brain stimulation surgery. Microelectrode recording (MER) is used in many cases to identify the target nucleus. A real-time procedure for identifying the entry and exit points of the STN would improve the outcome of this targeting procedure. We used the normalized root mean square (NRMS) of a short (5 seconds) MER sampled signal and the estimated anatomical distance to target (EDT) as the basis for this procedure. Electrode tip location was defined intraoperatively by an expert neurophysiologist to be before, within, or after the STN. Data from 46 trajectories of 27 patients were used to calculate the Bayesian posterior probability of being in each of these locations, given RMS-EDT pair values. We tested our predictions on each trajectory using a bootstrapping technique, with the rest of the trajectories serving as a training set and found the error in predicting the STN entry to be (mean +/- SD) 0.18 +/- 0.84, and 0.50 +/- 0.59 mm for STN exit point, which yields a 0.30 +/- 0.28 mm deviation from the expert's target center. The simplicity and computational ease of RMS calculation, its spike sorting-independent nature and tolerance to electrode parameters of this Bayesian predictor, can lead directly to the development of a fully automated intraoperative physiological procedure for the refinement of imaging estimates of STN borders.

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“…One possibility, therefore, is that potassium rectification does not occur at all in this preparation. It is difficult, however, to reconcile this suggestion with the fact that the resistance during the plateau of the turtle ventricle action potential is greater than the resting resistance (Eyster & Gilson, 1947;Cranefield, Eyster & Gilson, 1951). This observation is readily explained by supposing that gE is reduced during the action potential (see Fig.…”

Section: Ionic Fluxes In Cardiac Musclementioning

confidence: 99%

“…gE increases on depolarization of the membrane but does so very much more slowly than in normal squid nerve. For the purpose of describing the properties of cardiac muscle this modification is not sufficient, as it does not account for the high resistance during the plateau of the action potential which has been observed in kid Purkinje fibres (Weidmann, 1951), turtle ventricle fibres (Eyster & Gilson, 1947;Cranefield et at. 1951) and rabbit ventricular fibres (Johnson & Tille, 1960).…”

Section: Ionic Fluxes In Cardiac Musclementioning

confidence: 99%

A modification of the Hodgkin—Huxley equations applicable to Purkinje fibre action and pacemaker potentials

Noble¹

1962

The Journal of Physiology

863

544

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Electrical Characteristics of Injury Potentials

Cited by 18 publications

References 0 publications

Comparison of cardiac monophasic action potentials recorded by intracellular and suction electrodes

Comparison of cardiac monophasic action potentials recorded by intracellular and suction electrodes

Real‐time refinement of subthalamic nucleus targeting using Bayesian decision‐making on the root mean square measure

A modification of the Hodgkin—Huxley equations applicable to Purkinje fibre action and pacemaker potentials

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