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EMG frequency during isometric, submaximal activity : a statistical model for biceps brachii

Solnik S., De Vita P., Grzegorczyk K., Koziatek A., Bober T.

Acta of Bioengineering and Biomechanics

2010

Vol. 12, no. 3

21-28

The purpose of this study was to develop a statistical model to describe the electromyography (EMG) signal frequency changes during a submaximal isometric contraction. Thirty subjects performed a 30-second isometric contraction of the biceps brachii muscle at 80% of the maximal voluntary isometric force. Surface EMG electrodes recorded electrical activity of the biceps brachii. Zero-Crossing-Rate was calculated to identify EMG frequency shifts. The mean frequencies for every one-second period were used to calculate a linear relationship between frequency and time. A significant relationship ( p < 0.05) between slope and initial frequency value was identified. The model described EMG frequency changes during submaximal effort of biceps brachii up to 15 seconds. The prediction error was 9.8%. Modifying this equation to initial values of frequency of each participant decreased prediction error to 7.2%. These results demonstrate that despite individual differences between subjects it is possible to derive single equation that describes EMG alterations during submaximal, isometric contractions across a homogeneous group of people.

Different Models of Chemotherapy Taking Into Account Drug Resistance Stemming from Gene Amplification

Śmieja J., Świerniak A.

International Journal of Applied Mathematics and Computer Science

2003

Vol. 13, no 3

297-305

This paper presents an analysis of some class of bilinear systems that can be applied to biomedical modelling. It combines models that have been studied separately so far, taking into account both the phenomenon of gene amplification and multidrug chemotherapy in their different aspects. The mathematical description is given by an infinite dimensional state equation with a system matrix whose form allows decomposing the model into two interacting subsystems. While the first one, of a finite dimension, can have any form, the other is infinite dimensional and tridiagonal. A methodology of the analysis of such models, based on system decomposition, is presented. An optimal control problem is defined in the l1 space. In order to derive necessary conditions for optimal control, the model description is transformed into an integro-differential form. Finally, biomedical implications of the obtained results are discussed.