Biomedical subjects

Philip A Parker

Publications and source records attributed to Philip A Parker.

6 recordsLinked to original sources

PubMed2006

A simulation method for the firing sequences of motor units.

The firing sequences of motoneurons contain important information with regard to the underlying neural processes. Several methods have been proposed in the literature to simulate these sequences, however, one of the limitations is that they are not capable of simulating the complex neural dynamics of motor neurons, especially those of concurrently active ones, such as motor unit synchrony and motor unit common drive. In this paper, a novel model based on the Hodgkin-Huxley (HH) system is proposed, which has the ability to simulate the complex neurodynamics of the firing sequences of motor neurons. The model is presented at the cellular level and network level, and some simulation results from a simple 3-neuron network are presented to demonstrate its applications.

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PubMed2006

Fatigue estimation with a multivariable myoelectric mapping function.

A novel approach to muscle fatigue assessment is proposed. A function is used to map multiple myoelectric parameters representing segments of myoelectric data to a fatigue estimate for that segment. An artificial neural network is used to tune the mapping function and time-domain features are used as inputs. Two fatigue tests were conducted on five participants in each of static, cyclic and random conditions. The function was tuned with one data set and tested on the other. Performance was evaluated based on a signal to noise metric which compared variability due to fatigue factors with variability due to nonfatiguing factors. Signal to noise ratios for the mapping function ranged from 7.89 under random conditions to 9.69 under static conditions compared to 3.34-6.74 for mean frequency and 2.12-2.63 for instantaneous mean frequency indicating that the mapping function tracks the myoelectric manifestations of fatigue better than either mean frequency or instantaneous mean frequency under all three contraction conditions.

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PubMed2006

Modeling of muscle motor unit innervation process correlation and common drive.

Concurrently active motor units (MUs) of a given muscle can exhibit a certain degree of synchronous firings, and a certain degree of common variation in their firing rates. The former property is referred to as motor unit synchrony in the literature, which is termed motor unit innervation process (MUIP) correlation in this study. The latter is referred to as motor unit common drive and can be quantified by the common drive coefficient, which is the correlation coefficient between the smoothed firing rates of the two MUs. Both properties have important roles and implications in the generation and resulting characteristics of the myoelectric signal and for the development of signal processing algorithms in myoelectric signal (MES) applications. In order to study these implications and characteristics, in this paper estimation procedures are developed to quantify the degree of MUIP correlation and common drive as functions of physiological parameters. Also, the interaction between MUIP correlation and motor unit common drive is studied in a physiologically realistic simulation model. Neurons modeled by Hodgkin-Huxley systems form the framework of the simulation model in which excitation and synaptic characteristics can be modified. MUIP correlation and common drive degree and interaction are studied through a number of simulations. To support the simulation results, experimental in vivo motor unit trains were collected at low levels of contraction from 11 subjects, and decomposed into the constituent unit trains giving 50 concurrently active motor unit pairs. The simulation demonstrated that the innervation process correlation coefficient is controlled primarily by the postsynaptic conductance, gsyn, and was less than 0.05 mS/cm2 for realistic values of gsyn. The common drive was found to be controlled by the exciting neuron input with no statistically significant interaction between it and the MUIP correlation. The experimental data gave results in close agreement with those of the simulation.

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PubMed2004

Reduction of stimulus artifact in somatosensory evoked potentials: segmented versus subthreshold training.

A new approach to stimulus artifact cancellation is introduced, which attempts to model the process of stimulus artifact generation. This is done by training an estimator with multiple exemplars of the stimulus artifact at levels below the threshold of evoked response stimulation. Two estimators are formulated: one using a dynamic neural network and another using a linear estimator. The performance of these new approaches is compared to the segmented training approach, which has been previously demonstrated to be one of the most capable methods available. Performance assessment is carried out using a novel metric introduced in this paper, which focuses upon the relevant portion of the recorded waveform. The new cancellation schemes show distinct performance advantages over the segmented training approach.

Adult↗

PubMed2002

Deconvolution estimation of nerve conduction velocity distribution.

A conduction velocity distribution (CVD) estimator that incorporates volume conductor modeling of the nerve-evoked response is introduced in this paper. The CVD estimates are obtained from two compound nerve action potentials (CNAP) recorded at the skin surface. A third channel is introduced in order to assess the estimator performance in the experimental case. The relevance of using an accurate signal model is shown by comparing the performance of the proposed estimator with a previous approach based on a different CNAP model. The performance of the proposed estimator is evaluated for simulated and experimental data. The study assesses signal-to-noise ratio immunity and sensitivity to errors in the model parameters.

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PubMed2002

Deconvolution estimation of motor unit conduction velocity distribution.

A conduction velocity distribution (CVD) estimator that incorporates volume conductor modeling of the muscle voluntary response is introduced in this paper. The CVD estimates are obtained from two correlation functions, an autocorrelation and a cross, computed from myoelectric signal recorded at the skin surface. The performance of the proposed estimator is evaluated for simulated and experimental data. The study includes assessment of the estimator bias and standard deviation, as well as its sensitivity to errors in the model parameters. Simulations show its good performance in terms of estimator bias. A filtering technique also helps reduce its variance. However, the inaccuracy introduced in the estimation of model parameters considerably deteriorates the estimator performance.

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