Influence of heat on fatigue and electromyographic activity of the biceps brachii muscle.

Electromyography enables registering muscle activity during contraction and can identify muscle fatigue. In the present study, 30 volunteers between 18 and 30 years of age were submitted to an exertion 1 min of maximal voluntary isometric contraction. The electromyographic signal of the biceps brachii muscle and the strength of the flexor muscles of the elbow were determined before and after the administration of microwave diathermy in order to analyze the influence of heat over the strength of the elbow flexor muscles and fatigue of the biceps brachii. The results demonstrate that the strength of the elbow flexor muscles diminished significantly following the application of heat (p<0.05). Heat also led to a significant reduction in the electrical activity of the muscle studied. The present study demonstrates that microwave diathermy on the biceps brachii muscle reduces the flexion strength of the elbow as well as signs of muscle fatigue in the biceps.

ProRaman: a program to classify Raman spectra.

The program ProRaman, developed for the Matlab platform, provides an interactive and flexible graphic interface to develop efficient algorithms to classify Raman spectra into two or three different classes. A set of preprocessing algorithms to decrease the variable dimensionality and to extract the main features which improve the correct classification ratio was implemented. The implemented classification algorithms were based on the Mahalanobis distance and neural network. To verify the functionality of the developed program, 72 spectra from human artery samples, 36 of which had been histopathologically diagnosed as non-diseased and 36 as having an atherosclerotic lesion, were processed using a combination of different preprocessing and classification techniques. The best result was accomplished when the variables were selected from the Raman spectrum shift range from 1200 to 1700 cm(-1), then preprocessed using wavelets for compression and principal component analysis for feature extraction and, finally, classified by a multilayer perceptron with one hidden layer with eight neurons.