Correlação entre o sinal mecanomiográfico e a escala modificada de Ashworth durante avaliação clínica da espasticidade

Spasticity is a common disorder in people who have upper motor neuron injury. The involvement may occur at different levels. The Modified Ashworth Scale (MAS) is the most used method to measure involvement levels. But it corresponds to a subjective evaluation. Mechanomyography (MMG) is an objective technique that quantifies the muscle vibration during the contraction and stretching events. So, it may assess the level of spasticity accurately. This study aimed to investigate the correlation between spasticity levels determined by MAS with MMG signal in spastic and not spastic muscles. In the experimental protocol, we evaluated 34 members of 22 volunteers, of both genders, with a mean age of 39.91 ± 13.77 years. We evaluated the levels of spasticity by MAS in flexor and extensor muscle groups of the knee and/or elbow, where one muscle group was the agonist and one antagonist. Simultaneously the assessment by the MAS, caught up the MMG signals. We used a custom MMG equipment to register and record the signals, configured in LabView platform. Using the MatLab computer program, it was processed the MMG signals in the time domain (median energy) and spectral domain (median frequency) for the three motion axes: X (transversal), Y (longitudinal) and Z (perpendicular). For bandwidth delimitation, we used a 3rd order Butterworth filter, acting in the range of 5-50 Hz. Statistical tests as Spearman's correlation coefficient, Kruskal-Wallis test and linear correlation test were applied. As results in the time domain, the Kruskal-Wallis test showed differences in median energy (MMGME) between MAS groups. The linear correlation test showed high linear correlation between MAS and MMGME for the agonist muscle as well as for the antagonist group. The largest linear correlation occurred between the MAS and MMG ME for the Z axis of the agonist muscle group (R2 = 0.9557) and the lowest correlation occurred in the X axis, for the antagonist muscle group (R2 = 0.8862). The Spearman correlation test also confirmed high correlation for all axes in the time domain analysis. In the spectral domain, the analysis showed an increase in the median frequency (MMGMF) in MAS’ greater levels. The highest correlation coefficient between MAS and MMGMF signal occurred in the Z axis for the agonist muscle group (R2 = 0.4883), and the lowest value occurred on the Y axis for the antagonist group (R2 = 0.1657). By means of the Spearman correlation test, the highest correlation occurred between the Y axis of the agonist group (0.6951; p <0.001) and the lowest value on the X axis of the antagonist group (0.3592; p <0.001). We conclude that there was a significantly high correlation between the MMGME and MAS in both muscle groups. Also between MMG and MAS occurred a significant correlation, however moderate for the agonist group, and low for the antagonist group. So, the MMGME proved to be more an appropriate descriptor to correlate with the degree of spasticity defined by the MAS.

Diagnóstico automático de eventos e distúrbios em unidades geradoras e usinas utilizando lógica fuzzy

The electric power systems are getting more complex and covering larger areas day by day. This fact has been contribuiting to the development of monitoring techniques that aim to help the analysis, control and planning of power systems. Supervisory Control and Data Acquisition (SCADA) systems, Wide Area Measurement Systems and disturbance record systems. Unlike SCADA and WAMS, disturbance record systems are mainly used for offilne analysis in occurrences where a fault resulted in tripping of and apparatus such as a transimission line, transformer, generator and so on. The device responsible for record the disturbances is called Digital Fault Recorder (DFR) and records, basically, electrical quantities as voltage and currents and also, records digital information from protection system devices. Generally, in power plants, all the DFRs data are centralized in the utility data centre and it results in an excess of data that difficults the task of analysis by the specialist engineers. This dissertation shows a new methodology for automated analysis of disturbances in power plants. A fuzzy reasoning system is proposed to deal with the data from the DFRs. The objective of the system is to help the engineer resposnible for the analysis of the DFRs’s information by means of a pre-classification of data. For that, the fuzzy system is responsible for generating unit operational state diagnosis and fault classification.