Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Adicionar filtros








Intervalo de ano
1.
Chinese Journal of Medical Instrumentation ; (6): 210-215, 2020.
Artigo em Chinês | WPRIM | ID: wpr-942729

RESUMO

An intravascular ultrasound-enhanced thrombolysis excitation system with adjustable frequency, amplitude and duty cycle was designed based on FPGA (ZYNQ-7Z020). Firstly, the FPGA generated waveform amplitude binary data based on direct digital frequency synthesis (DDS) technology, and then the data was converted into burst signal through an external daughter card, which included D/A conversion circuit, active low-pass filter, power amplifier circuit and impedance matching circuit. The test results demonstrated that the output waveform reached the target with advantages of simple implementation and flexible control, the peak negative pressure generated from ultrasound transducer was doubled by means of an electrical impedance matching network. In vitro thrombus models were applied to verify the excitation system, it turned out that ultrasound cavitation effect generated could accelerate the penetration of urokinase and increase the thrombolysis rate by about 20%.


Assuntos
Amplificadores Eletrônicos , Impedância Elétrica , Terapia Trombolítica , Ultrassonografia , Ultrassonografia de Intervenção
2.
Journal of Biomedical Engineering ; (6): 8-12, 2015.
Artigo em Chinês | WPRIM | ID: wpr-266735

RESUMO

Studies have shown that the clinical manifestation of patients with neuropsychiatric disorders might be related to the abnormal connectivity of brain functions. Psychogenic non-epileptic seizures (PNES) are different from the conventional epileptic seizures due to the lack of the expected electroencephalographically epileptic changes in central nervous system, but are related to the presence of significant psychological factors. Diagnosis of PNES remains challenging. We found in the present work that the connectivity between the frontal and parieto-occipital in PNES was weaker than that of the controls by using network analysis based on electroencephalogram (EEG) signals. In addition, PNES were recognized by using the network properties as linear discriminant nalysis (LDA) input and classification accuracy was 85%. This study may provide a feasible tool for clinical diagnosis of PNES.


Assuntos
Humanos , Encéfalo , Eletroencefalografia , Epilepsia , Convulsões , Diagnóstico
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA