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Adaptive spatio-temporal filtering of multichannel surface EMG signals
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
SLU, Centre of Biostochastics.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
2006 (English)In: Medical and Biological Engineering and Computing, ISSN 0140-0118, E-ISSN 1741-0444, Vol. 44, no 3, 209-215 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2006. Vol. 44, no 3, 209-215 p.
Keyword [en]
Electromyography, EMG, Multichannel, Spatio-temporal filter, Kurtosis
National Category
Medical Engineering
Research subject
Signal Processing
URN: urn:nbn:se:umu:diva-13355DOI: 10.1007/s11517-006-0029-1ISI: 000240031900006PubMedID: 16937162OAI: diva2:153026
Available from: 2006-11-06 Created: 2006-11-06 Last updated: 2013-10-02Bibliographically approved
In thesis
1. Adaptive signal processing of surface electromyogram signals
Open this publication in new window or tab >>Adaptive signal processing of surface electromyogram signals
2006 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Electromyography is the study of muscle function through the electrical signals from the muscles. In surface electromyography the electrical signal is detected on the skin. The signal arises from ion exchanges across the muscle fibres’ membranes. The ion exchange in a motor unit, which is the smallest unit of excitation, produces a waveform that is called an action potential (AP). When a sustained contraction is performed the motor units involved in the contraction will repeatedly produce APs, which result in AP trains. A surface electromyogram (EMG) signal consists of the superposition of many AP trains generated by a large number of active motor units. The aim of this dissertation was to introduce and evaluate new methods for analysis of surface EMG signals.

An important aspect is to consider where to place the electrodes during the recording so that the electrodes are not located over the zone where the neuromuscular junctions are located. A method that could estimate the location of this zone was presented in one study.

The mean frequency of the EMG signal is often used to estimate muscle fatigue. For signals with low signal-to-noise ratio it is important to limit the integration intervals in the mean frequency calculations. Therefore, a method that improved the maximum frequency estimation was introduced and evaluated in comparison with existing methods.

The main methodological work in this dissertation was concentrated on finding single motor unit AP trains from EMG signals recorded with several channels. In two studies single motor unit AP trains were enhanced by using filters that maximised the kurtosis of the output. The first of these studies used a spatial filter, and in the second study the technique was expanded to include filtration in time. The introduction of time filtration resulted in improved performance, and when the method was evaluated in comparison with other methods that use spatial and/or temporal filtration, it gave the best performance among them. In the last study of this dissertation this technique was used to compare AP firing rates and conduction velocities in fibromyalgia patients as compared with a control group of healthy subjects.

In conclusion, this dissertation has resulted in new methods that improve the analysis of EMG signals, and as a consequence the methods can simplify physiological research projects.

Place, publisher, year, edition, pages
Umeå: Strålningsvetenskaper, 2006. 52 p.
Umeå University medical dissertations, ISSN 0346-6612 ; 1009
Signalbehandling, electromyography, signal processing, Signalbehandling
Research subject
Biomedical Radiation Science
urn:nbn:se:umu:diva-743 (URN)91-7264-033-2 (ISBN)
Public defence
2006-04-28, 244, 7, Norrlands universitetssjukhus, Umeå, 13:00 (English)
Available from: 2006-04-05 Created: 2006-04-05 Last updated: 2010-01-18Bibliographically approved

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