Myoelectric Signal Monitoring System

The Electromyography (EMG) is an important tool for gait analyzes and disorders diagnoses. Traditional methods involve equipment that can disturb the analyses, being gradually substituted by different approaches, like wearable and wireless systems. The cable replacement for autonomous systems demands for technologies capable of meeting the power constraints. This work presents the development of an EMG and kinematic data capture wireless module, designed taking into account power consumption issues. This module captures and converts the analog myoeletric signal to digital, synchronously with the capture of kinetic information. Both data are time multiplexed and sent to a PC via Bluetooth link. The work carried out comprised the development of the hardware, the firmware and a graphical interface running in an external PC. The hardware was developed using the PIC18F14K22, a low power family of microcontrollers. The link was established via Bluetooth, a protocol designed for low power communication. An application was also developed to recover and trace the signal to a Graphic User Interface (GUI), coordinating the message exchange with the firmware. Results were obtained which allowed validating the conceived system in static and with the subject performing short movements. Although it was not possible to perform the tests within more dynamic movements, it is shown that it is possible to capture, transmit and display the captured data as expected. Some suggestions to improve the system performance also were made.

A Clinical Support System Based on Quality of Life Estimation

Quality of life is a concept influenced by social, economic, psychological, spiritual or medical state factors. More specifically, the perceived quality of an individual's daily life is an assessment of their well-being or lack of it. In this context, information technologies may help on the management of services for healthcare of chronic patients such as estimating the patient quality of life and helping the medical staff to take appropriate measures to increase each patient quality of life. This paper describes a Quality of Life estimation system developed using information technologies and the application of data mining algorithms to access the information of clinical data of patients with cancer from Otorhinolaryngology and Head and Neck services of an oncology institution. The system was evaluated with a sample composed of 3013 patients. The results achieved show that there are variables that may be significant predictors for the Quality of Life of the patient: years of smoking (p value 0.049) and size of the tumor (p value < 0.001). In order to assign the variables to the classification of the quality of life the best accuracy was obtained by applying the John Platt's sequential minimal optimization algorithm for training a support vector classifier. In conclusion data mining techniques allow having access to patients additional information helping the physicians to be able to know the quality of life and produce a well-informed clinical decision.

Municipal solid waste management system: decision support through systems analysis

Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia for the degree of Doctor of Philosophy in Environmental Engineering

Regional change in the Algarve: A Geographic Information System approach

Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information Systems

COMPARISON BETWEEN AUTOMATED SYSTEM AND PCR-BASED METHOD FOR IDENTIFICATION AND ANTIMICROBIAL SUSCEPTIBILITY PROFILE OF CLINICAL Enterococcus spp

Enterococci are increasingly responsible for nosocomial infections worldwide. This study was undertaken to compare the identification and susceptibility profile using an automated MicrosScan system, PCR-based assay and disk diffusion assay of Enterococcus spp. We evaluated 30 clinical isolates of Enterococcus spp. Isolates were identified by MicrosScan system and PCR-based assay. The detection of antibiotic resistance genes (vancomycin, gentamicin, tetracycline and erythromycin) was also determined by PCR. Antimicrobial susceptibilities to vancomycin (30 µg), gentamicin (120 µg), tetracycline (30 µg) and erythromycin (15 µg) were tested by the automated system and disk diffusion method, and were interpreted according to the criteria recommended in CLSI guidelines. Concerning Enterococcus identification the general agreement between data obtained by the PCR method and by the automatic system was 90.0% (27/30). For all isolates of E. faecium and E. faecalis we observed 100% agreement. Resistance frequencies were higher in E. faecium than E. faecalis. The resistance rates obtained were higher for erythromycin (86.7%), vancomycin (80.0%), tetracycline (43.35) and gentamicin (33.3%). The correlation between disk diffusion and automation revealed an agreement for the majority of the antibiotics with category agreement rates of > 80%. The PCR-based assay, the van(A) gene was detected in 100% of vancomycin resistant enterococci. This assay is simple to conduct and reliable in the identification of clinically relevant enterococci. The data obtained reinforced the need for an improvement of the automated system to identify some enterococci.

User friendly knowledge acquisition system for medical devices actuation

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Information flow analysis using data-dependent logical propositions

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Kinetic, Structural, and EPR Studies Reveal That Aldehyde Oxidoreductase from Desulfovibrio gigas Does Not Need a Sulfido Ligand for Catalysis and Give Evidence for a Direct Mo-C Interaction in a Biological System

J. Am. Chem. Soc., 2009, 131 (23), pp 7990–7998 DOI: 10.1021/ja809448r