Abstract
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.
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Acknowledgement
The European Union Regional Development Fund and the Swedish Research Council supported this study. Experimental EMG recordings are part of a pilot study taken part in co-operation with Ass. Prof. Jack Lindh and MD. Per Bergström at the Department of Oncology, University Hospital, Umeå, Sweden. The pilot study is supported by Lions Cancer Research Foundation, Umeå University.
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Östlund, N., Yu, J. & Karlsson, J.S. Adaptive spatio-temporal filtering of multichannel surface EMG signals. Med Bio Eng Comput 44, 209–215 (2006). https://doi.org/10.1007/s11517-006-0029-1
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DOI: https://doi.org/10.1007/s11517-006-0029-1