Biomedical subjects

Kevin B Englehart

Publications and source records attributed to Kevin B Englehart.

8 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.

Action Potentials↗

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.

Action Potentials↗

PubMed2006

Multiexpert automatic speech recognition using acoustic and myoelectric signals.

Classification accuracy of conventional automatic speech recognition (ASR) systems can decrease dramatically under acoustically noisy conditions. To improve classification accuracy and increase system robustness a multiexpert ASR system is implemented. In this system, acoustic speech information is supplemented with information from facial myoelectric signals (MES). A new method of combining experts, known as the plausibility method, is employed to combine an acoustic ASR expert and a MES ASR expert. The plausibility method of combining multiple experts, which is based on the mathematical framework of evidence theory, is compared to the Borda count and score-based methods of combination. Acoustic and facial MES data were collected from 5 subjects, using a 10-word vocabulary across an 18-dB range of acoustic noise. As expected the performance of an acoustic expert decreases with increasing acoustic noise; classification accuracies of the acoustic ASR expert are as low as 11.5%. The effect of noise is significantly reduced with the addition of the MES ASR expert. Classification accuracies remain above 78.8% across the 18-dB range of acoustic noise, when the plausibility method is used to combine the opinions of multiple experts. In addition, the plausibility method produced classification accuracies higher than any individual expert at all noise levels, as well as the highest classification accuracies, except at the 9-dB noise level. Using the Borda count and score-based multiexpert systems, classification accuracies are improved relative to the acoustic ASR expert but are as low as 51.5% and 59.5%, respectively.

Algorithms↗

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.

Action Potentials↗

PubMed2005

Continuous myoelectric control for powered prostheses using hidden Markov models.

This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal. The scheme described within uses a hidden Markov model (HMM) to process four channels of myoelectric signal, with the task of discriminating six classes of limb movement. The HMM-based approach is shown to be capable of higher classification accuracy than previous methods based upon multilayer perceptrons. The method does not require segmentation of the myoelectric signal data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. The computational complexity of the HMM in its operational mode is low, making it suitable for a real-time implementation. The low computational overhead associated with training the HMM also enables the possibility of adaptive classifier training while in use.

Action Potentials↗

PubMed2005

A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses.

This paper introduces and evaluates the use of Gaussian mixture models (GMMs) for multiple limb motion classification using continuous myoelectric signals. The focus of this work is to optimize the configuration of this classification scheme. To that end, a complete experimental evaluation of this system is conducted on a 12 subject database. The experiments examine the GMMs algorithmic issues including the model order selection and variance limiting, the segmentation of the data, and various feature sets including time-domain features and autoregressive features. The benefits of postprocessing the results using a majority vote rule are demonstrated. The performance of the GMM is compared to three commonly used classifiers: a linear discriminant analysis, a linear perceptron network, and a multilayer perceptron neural network. The GMM-based limb motion classification system demonstrates exceptional classification accuracy and results in a robust method of motion classification with low computational load.

Algorithms↗

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↗

PubMed2003

Myoelectric signal compression using zero-trees of wavelet coefficients.

Recent progress in the diagnostic use of the myoelectric signal for neuromuscular diseases, coupled with increasing interests in telemedicine applications, mandate the need for an effective compression technique. The efficacy of the embedded zero-tree wavelet compression algorithm is examined with respect to some important analysis parameters (the length of the analysis segment and wavelet type) and measurement conditions (muscle type and contraction type). It is shown that compression performance improves with segment length, and that good choices of wavelet type include the Meyer wavelet and the fifth order biorthogonal wavelet. The effects of different muscle sites and contraction types on compression performance are less conclusive.A comparison of a number of lossy compression techniques has revealed that the EZW algorithm exhibits superior performance to a hard thresholding wavelet approach, but falls short of adaptive differential pulse code modulation. The bit prioritization capability of the EZW algorithm allows one to specify the compression factor online, making it an appealing technique for streaming data applications, as often encountered in telemedicine.

Adult↗