Nils Östlund

An estimation of the influence of force decrease on the mean power spectral frequency shift of the EMG during repetitive maximum dynamic knee extensions

Frequency analysis of myoelectric (ME) signals, using the mean power spectral frequency (MNF), has been widely used to characterize peripheral muscle fatigue during isometric contractions assuming constant force. However, during repetitive isokinetic contractions performed with maximum effort, output (force or torque) will decrease markedly during the initial 40-60 contractions, followed by a phase with little or no change. MNF shows a similar pattern. In situations where there exist a significant relationship between MNF and output, part of the decrease in MNF may per se be related to the decrease in force during dynamic contractions. This study estimated force effects on the MNF shifts during repetitive dynamic knee extensions. Twenty healthy volunteers participated in the study and both surface ME signals (from the right vastus lateralis, vastus medialis, and rectus femoris muscles) and the biomechanical signals (force, position, and velocity) of an isokinetic dynamometer were measured. Two tests were performed: (i) 100 repetitive maximum isokinetic contractions of the right knee extensors, and (ii) five gradually increasing static knee extensions before and after (i). The corresponding ME signal time-frequency representations were calculated using the continuous wavelet transform. Compensation of the MNF variables of the repetitive contractions was performed with respect to the individual MNF-force relation based on an average of five gradually increasing contractions. Whether or not compensation was necessary was based on the shape of the MNF-force relationship. A significant compensation of the MNF was found for the repetitive isokinetic contractions. In conclusion, when investigating maximum dynamic contractions, decreases in MNF can be due to mechanisms similar to those found during sustained static contractions (force-independent component of fatigue) and in some subjects due to a direct effect of the change in force (force-dependent component of fatigue). In order to compare MNF shifts during sustained static and repetitive dynamic contractions it is necessary to estimate the force-dependent component of fatigue of dynamic contractions. Our results are preliminary and have to be confirmed in larger experiments using single dynamic contractions when determining the MNF-force relationship of the unfatigued situation.

Signal processing of the surface electromyogram to gain insight into neuromuscular physiology

A surface electromyogram (sEMG) contains information about physiological and morphological characteristics of the active muscle and its neural strategies. Because the electrodes are situated on the skin above the muscle, the sEMG is an easily obtainable source of information. However, different combinations of physiological and morphological characteristics can lead to similar sEMG signals and sEMG recordings contain noise and other artefacts. Therefore, many sEMG signal processing methods have been developed and applied to allow insight into neuromuscular physiology. This paper gives an overview of important advances in the development and applications of sEMG signal processing methods, including spectral estimation, higher order statistics and spatio-temporal processing. These methods provide information about muscle activation dynamics and muscle fatigue, as well as characteristics and control of single motor units (conduction velocity, firing rate, amplitude distribution and synchronization).

Effects of inflow and radiofrequency spoiling on the arterial input function in dynamic contrast-enhanced MRI: A combined phantom and simulation study

The arterial input function is crucial in pharmacokinetic analysis of dynamic contrast‐enhanced MRI data. Among other artifacts in arterial input function quantification, the blood inflow effect and nonideal radiofrequency spoiling can induce large measurement errors with subsequent reduction of accuracy in the pharmacokinetic parameters. These errors were investigated for a 3D spoiled gradient‐echo sequence using a pulsatile flow phantom and a total of 144 typical imaging settings. In the presence of large inflow effects, results showed poor average accuracy and large spread between imaging settings, when the standard spoiled gradient‐echo signal equation was used in the analysis. For example, one of the investigated inflow conditions resulted in a mean error of about 40% and a spread, given by the coefficient of variation, of 20% for Ktrans. Minimizing inflow effects by appropriate slice placement, combined with compensation for nonideal radiofrequency spoiling, significantly improved the results, but they remained poorer than without flow (e.g., 3–4 times larger coefficient of variation for Ktrans). It was concluded that the 3D spoiled gradient‐echo sequence is not optimal for accurate arterial input function quantification and that correction for nonideal radiofrequency spoiling in combination with inflow minimizing slice placement should be used to reduce the errors. Magn Reson Med, 2011.

Adaptive spatial filtering of multichannel surface electromyogram signals

Spatial filtering of surface electromyography (EMG) signal can be used to enhance single motor unit action potentials (MUAPs). Traditional spatial filters for surface EMG do not take into consideration that some electrodes could have poor skin contact. In contrast to the traditional a priori defined filters, this study introduces an adaptive spatial filtering method that adapts to the signal characteristics. The adaptive filter, the maximum kurtosis filter (MKF), was obtained by using the linear combination of surrounding channels that maximises kurtosis. The MKF and conventional filters were applied to simulated EMG signals and to real EMG signals recorded with an electrode grid to evaluate their performance in detecting single motor units. The MKF was compared with conventional spatial filtering methods. Simulated signals, with different levels of spatially correlated noise, were used for comparison. The influence of one electrode with poor skin contact was also investigated. The MKF was found to be considerably better at enhancing a single MUAP than conventional methods for all levels of spatial correlation of the noise. For a spatial correlation of 0.97 of the noise, the improvement in the signal-to-noise ratio, where a MUAP could be detected, was at least 6 dB. With a simulated poor skin contact for one electrode, the improvement over the other methods was at least 19 dB.

A Novel Estimation Method for Physiological Parameters in Dynamic Contrast-Enhanced MRI: Application of a Distributed Parameter Model Using Fourier-Domain Calculations

Dynamic contrast-enhanced magnetic resonance imaging (MRI) is a promising tool in the evaluation of tumor physiology. From rapidly acquired images and a model for contrast agent pharmacokinetics, physiological parameters are derived. One pharmacokinetic model, the tissue homogeneity model, enables estimation of both blood flow and vessel permeability together with parameters that describe blood volume and extracellular extravascular volume fraction. However, studies have shown that parameter estimation with this model is unstable. Therefore, several initial guesses are needed for accurate estimates, which makes the estimation slow. In this study a new estimation algorithm for the tissue homogeneity model, based on Fourier domain calculations, was derived and implemented as a Matlab program. The algorithm was tested with Monte-Carlo simulations and the results were compared to an existing method that uses the adiabatic approximation. The algorithm was also tested on data from a metastasis in the brain. The comparison showed that the new algorithm gave more accurate results on the 2.5th and 97.5th percentile levels, for instance the error in blood volume was reduced by 21%. In addition, the time needed for the computations was reduced with a factor 25. It was concluded that the new algorithm can be used to speed up parameter estimation while accuracy can be gained at the same time.

Simultaneous estimation of muscle fibre conduction velocity and muscle fibre orientation using 2D multichannel surface electromyogram.

The paper presents a new approach for simultaneous estimation of muscle fibre conduction velocity (MFCV) and muscle fibre orientation (MFO) for motor units (MUs) in two-dimensional (2D) multichannel surface electromyography recordings. This is an important tool for detecting changes and abnormalities in muscle function and structure. In addition, simultaneous estimation of MFO and MFCV avoids the necessity of manual electrode alignment. The proposed method detected propagating MU action potentials (MUAPs) in a running time window as moving components in amplitude maps. Thereafter, estimations were obtained by fitting a three-dimensional function to these maps. The performance was evaluated using synthetic MU signals at 10 dB SNR and authentic biceps brachii measurements. Results demonstrated MFCV and MFO estimates with standard deviations of less than 0.05 m s−1 and 1° for simulated signals, and less than 0.2 m s−1 and 4° for experimental data. However, standard deviations as low as 0.12 m s−1 and 1.6° from real signals were demonstrated. It was concluded that the method performs as well as, or better than, linear array multichannel methods when individual propagating MUAPs can be identified, even if electrodes are not aligned with fibre direction.

Adaptive spatio-temporal filtering of disturbed ECGs: a multi-channel approach to heartbeat detection in smart clothing

Intermittent disturbances are common in ECG signals recorded with smart clothing: this is mainly because of displacement of the electrodes over the skin. We evaluated a novel adaptive method for spatio-temporal filtering for heartbeat detection in noisy multi-channel ECGs including short signal interruptions in single channels. Using multi-channel database recordings (12-channel ECGs from 10 healthy subjects), the results showed that multi-channel spatio-temporal filtering outperformed regular independent component analysis. We also recorded seven channels of ECG using a T-shirt with textile electrodes. Ten healthy subjects performed different sequences during a 10-min recording: resting, standing, flexing breast muscles, walking and pushups. Using adaptive multi-channel filtering, the sensitivity and precision was above 97% in nine subjects. Adaptive multi-channel spatio-temporal filtering can be used to detect heartbeats in ECGs with high noise levels. One application is heartbeat detection in noisy ECG recordings obtained by integrated textile electrodes in smart clothing.

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.

Wavelet Coherence Detects Non-autonomic Heart Rate Fluctuations in Familial Amyloidotic Polyneuropathy

Heart rate variability (HRV) is often used to study disturbances in the autonomic nervous system. Respiratory related HRV is seen as an indicator of a working autonomic mechanism. However, sometimes a high HRV may be caused by non-autonomic mechanisms. This study investigated if the wavelet coherence could be used to study respiratory related fluctuations in heart rate. The wavelet coherence method was applied to two Familial amyloidotic polyneuropathy patients and one healthy control. The results showed that wavelet coherence is a promising method for studying respiratory related fluctuations in heart rate.

Multichannel filter for heartbeat detection in noisy ECG recordings

In ECG signals recorded with smart clothes disturbances as intermittent loss of signal from electrodes, movement artefacts, and electromyographic interference are common. In this study a multichannel method for spatio-temporal filtering is evaluated using ECG signals from a database and with three recordings made with a T-shirt with integrated textile electrodes. The sensitivity and precision of the signals from the database were 99.6% and 98.5%, respectively, if 12 channels were used and the signal-to-noise ratio was -10 dB. The filter gave a sensitivity of 99.6% and a precision of 99.5% in the recordings from the textile electrodes. In conclusion, the results obtained indicated that multichannel spatio-temporal filtration could be a suitable method for heartbeat detection in ECG measurements with textile electrodes.