PubMed · 6903685
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W Wright. 1980. Visual function: electrophysiological tests.. https://doi.org/10.1111/j.1746-1561.1980.tb08180.x
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Many clinical and research applications rely on detecting evoked potential (EP) signal or EP differences between conditions. Statistical methods for objective signal detection should be sensitive to the presence of signal, but must provide the user strict control on tolerated false alarm rate. The respective sensitivities of 6 signal detection methods were compared through several Monte Carlo simulations involving 2 autocorrelation structures, 5% and 1% significance levels, 8, 10 or 12 replications per study, and increasing signal to noise ratio. The signal detection methods compared were: (1) the Record Orthogonality Test by Permutations (ROT-p), a variant of the Residual Orthogonality Test (Achim et al., 1988), that provides an unbiased estimate of the energy of the signal present in the averaged data, (2) the Tsum2 permutation test of Karniski et al. (1994), (3) a Principal Component Analysis method (PC1) consisting of a t test on the weights of the first principal component, (4) multiple t tests on amplitudes with empirical adjustment for global false alarm rate, and (5-6) the test of Guthrie and Buchwald (1991) on length of consecutive t tests significant at P < 0.05 or 0.01 per-test. The first 3 methods did not exceed their nominal false alarm rate and clearly outperformed the last 3, with the ROT-p method being significantly more sensitive than all others under almost all conditions.
Instrumentation capable of handling 12 continuous hours of nine-channel real-time physiologic data sampled at 10Hz was needed to test within and between subject variability and preterm infant responses to skin-to-skin contact with the mother. A review of basic electrical components, electrical principles related to physiologic monitoring, and electrophysiology concepts generic to physiologic monitoring is presented. The development, specifications and applications of a new instrument to monitor premature infant cardiorespiratory adaptations are discussed.
This paper describes a new parameter estimation method applicable to experimental voltage-clamp records. The method is based on the Hodgkin-Huxley (HH) representation of a generic non-inactivating delayed rectifier current (IK) which can be assimilated to the delayed rectifier potassium current of cardiac cells. The model involves a single gating variable of activation (chi) of degree (lambda chi). Its parameters include the voltage-dependent steady-state characteristic (chi infinity), time constant tau chi, the degree lambda chi as a positive integer, and the maximal conductance gK. The method is based on linear optimization. It implements a series of least-squares minimization steps to calculate a first estimate of each model parameter, followed by global minimization to obtain final estimates. The required data, in the form of ionic current responses, correspond to standard voltage-clamp protocols. The effects of noise are minimized by avoiding the use of the time derivative of IK in the calculations. Simulated voltage-clamp data using either a HH model or a five-state Markov chain (MC) model served two purposes: (i) to test the performance of the HH parameter estimation method, and (ii) to study the suitability of the HH model to reproduce data generated by models other than HH. A nominal MC model was obtained by fitting its current responses to those of the HH model. Rate constants of the nominal MC model were then modified and voltage-clamp current responses were generated. Excellent results were obtained with HH and nominal MC data. Data sets generated by a 20% change in the rate constants of the nominal MC model showed that the closed-state rate constants have only a limited influence on the HH parameter estimates, whereas changes in the closed-to-open rate constants produce substantial effects. Nevertheless, a given MC data set can be fitted quite closely by a HH model. In the light of these simulation results it is indicated that an hybrid HH-MC representation of IK data would be more flexible than a straight HH model by removing some of the constraints between the rate constants, and less cumbersome than a straight MC model by substantially reducing the number of parameters to be estimated.