Biomedical subjects

J Stefan Karlsson

Publications and source records attributed to J Stefan Karlsson.

6 recordsLinked to original sources

PubMed2006

Location of innervation zone determined with multichannel surface electromyography using an optical flow technique.

Multichannel surface electromyography has developed towards more channels and higher spatial resolution. This allows the study of multichannel electromyograms as images of the potential distribution on the skin. In this paper, a method that estimates the motion of the potential distribution using an optical-flow-based technique is introduced. The optical flow is a vector field that describes how images change with time. The aim of this study was to introduce a new method for innervation zone (IZ) localization and to evaluate its performance. The new method was compared with a method that uses the position of the lowest root-mean-square (RMS) value in an electrode array as an estimate of the IZ localization. Comparisons were made with both simulated signals and with recorded multichannel electromyogram signals. Simulations showed that the methods performed similarly for high signal-to-noise ratio (SNR) and that the optical-flow-based method was superior for lower SNR. When the experimental signals were used, localization with the optical-flow-based method gave a mean absolute deviation of 2.4mm from the location given by an expert group. The lowest RMS method gave a significantly higher deviation (13.6mm). Due to the low computational complexity of the optical flow algorithm it is possible to get the estimations of the IZ localization in real time.

Action Potentials↗

PubMed2006

Adaptive spatio-temporal filtering of multichannel surface EMG signals.

A motor unit (MU) is defined as an anterior horn cell, its axon, and the muscle fibres innervated by the motor neuron. A surface electromyogram (EMG) is a superposition of many different MU action potentials (MUAPs) generated by active MUs. The objectives of this study were to introduce a new adaptive spatio-temporal filter, here called maximum kurtosis filter (MKF), and to compare it with existing filters, on its performance to detect a single MUAP train from multichannel surface EMG signals. The MKF adaptively chooses the filter coefficients by maximising the kurtosis of the output. The proposed method was compared with five commonly used spatial filters, the weighted low-pass differential filter (WLPD) and the marginal distribution of a continuous wavelet transform. The performance was evaluated using simulated EMG signals. In addition, results from a multichannel surface EMG measurement fro from a subject who had been previously exposed to radiation due to cancer were used to demonstrate an application of the method. With five time lags of the MKF, the sensitivity was 98.7% and the highest sensitivity of the traditional filters was 86.8%, which was obtained with the WLPD. The positive predictivities of these filters were 87.4 and 80.4%, respectively. Results from simulations showed that the proposed spatio-temporal filtration technique significantly improved performance as compared with existing filters, and the sensitivity and the positive predictivity increased with an increase in number of time lags in the filter.

Action Potentials↗

PubMed2004

Inhomogeneities in muscle activation reveal motor unit recruitment.

This paper presents a novel method to quantify spatial changes in muscle activation pattern by multi-channel surface electromyography (MCSEMG) in order to investigate motor unit recruitment variation. The method is based on non-uniform distributions of motor units that cause spatial inhomogeneous muscle activation. To evaluate the method, 15 subjects performed three isometric elbow flexion contractions consisting of slow sinusoidal changes in force ranging from 0% to 80% of the maximal voluntary contraction. MCSEMG electrodes were placed in a 10 x 13 grid over the biceps brachii muscle. From all channels, root mean square (RMS) values of bipolar leadings were computed over 0.5 s epochs over the whole recording. Thereafter, correlation coefficients were calculated between the RMS values at one epoch, with the RMS values at another epoch. Results showed consistent spatial changes in the distribution of RMS at different contraction levels up to 80% of maximal voluntary contraction and when comparing increasing and decreasing contractions at the same force level. These findings are reproducible within and between subjects, and in agreement with physiological phenomena and therefore indicate that the spatial inhomogeneities of motor unit properties in the biceps brachii muscle can be used to study changes in motor unit recruitment.

Adult↗

PubMed2004

Improved maximum frequency estimation with application to instantaneous mean frequency estimation of surface electromyography.

The purpose of this study was to improve the maximum-frequency estimation. Three methods to estimate the maximum frequency of a bandlimited signal with additive white noise were compared. Two existing methods, the threshold-crossing method (TCM) and the hybrid method, were modified for time-frequency representations. A novel approach, the running-block threshold method (RBTM), was introduced. Based on calculation of detection probability (sensitivity) the RBTM improved the maximum-frequency estimate as compared with the TCM. The maximum-frequency estimation methods were also used to determine the integration interval for instantaneous mean-frequency (IMNF) estimation from synthesized surface electromyography containing white noise. Results showed that the IMNF estimate was improved by using any of the three methods and that the RBTM gave the best IMNF estimate.

Action Potentials↗

PubMed2003

Classification of neck movement patterns related to whiplash-associated disorders using neural networks.

This paper presents a new method for classification of neck movement patterns related to Whiplash-associated disorders (WAD) using a resilient backpropagation neural network (BPNN). WAD are a common diagnosis after neck trauma, typically caused by rear-end car accidents. Since physical injuries seldom are found with present imaging techniques, the diagnosis can be difficult to make. The active range of the neck is often visually inspected in patients with neck pain, but this is a subjective measure, and a more objective decision support system, that gives a reliable and more detailed analysis of neck movement pattern, is needed. The objective of this study was to evaluate the predictive ability of a BPNN, using neck movement variables as input. Three-dimensional (3-D) neck movement data from 59 subjects with WAD and 56 control subjects were collected with a ProReflex system. Rotation angle and angle velocity were calculated using the instantaneous helical axis method and motion variables were extracted. A principal component analysis was performed in order to reduce data and improve the BPNN performance. BPNNs with six hidden nodes had a predictivity of 0.89, a sensitivity of 0.90 and a specificity of 0.88, which are very promising results. This shows that neck movement analysis combined with a neural network could build the basis of a decision support system for classifying suspected WAD, even though further evaluation of the method is needed.

Adult↗

PubMed2003

Chronic whiplash associated disorders and neck movement measurements: an instantaneous helical axis approach.

This paper presents an assessment tool for objective neck movement analysis of subjects suffering from chronic whiplash-associated disorders (WAD). Three-dimensional (3-D) motion data is collected by a commercially available motion analysis system. Head rotation, defined in this paper as the rotation angle around the instantaneous helical axis (IHA), is used for extracting a number of variables (e.g., angular velocity and range, symmetry of motion). Statistically significant differences were found between controls and subjects with chronic WAD in a number of variables.

Adult↗