Screening of malignant glioma, brain tumors, and brain injuries using disturbance coefficient, differential impedances, and artificial neural network

Abstract

A system and method to screen for malignant gliomas, other brain tumors, and brain injuries use disturbance coefficient, differential impedances, and artificial neural networks. The system uses prescribed excitation signals with several system configurations to measure the differential impedances, calculate harmonic responses and nonlinearity of brain tissue, and estimate the disturbance coefficient that indicates the likelihood of malignant gliomas, other brain tumors, and brain injuries. The disturbance coefficient is a weighted sum of many parameters such as receiving differential impedances, transmission differential impedances, harmonic responses, frequency dispersion, and nonlinear responses using different system configurations and different excitation signals. The method includes arranging the transmitters, receivers, and transmission lines to maximize the sensitivity of detecting brain tissue condition. The artificial neural network is trained to estimate the disturbance coefficient using clinical data. The method provides a sensitive and cost effective approach for screening malignant gliomas, other brain tumors, and brain injuries.