BRAIN TISSUE IMPEDANCE ESTIMATION Improve the Brain’s Evoked Potential’s source Temporal and Spatial Inverse Problem Improve the Brain Tissue Impedance.

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Presentation transcript:

BRAIN TISSUE IMPEDANCE ESTIMATION

Improve the Brain’s Evoked Potential’s source Temporal and Spatial Inverse Problem Improve the Brain Tissue Impedance value ranges

ASSUMPTION S Linearity of the System All channels have the same frequency The input current signal is sinusoidal

ASSUMPTIONS (cont.) The systems noise is a white noise at a maximum SNR of 10[dB] The tissue impedance can be characterized by a RC filter

ASSUMPTIONS (cont.) The tissue impedance is complex The tissue conductivity is anisotropy

ASSUMPTIONS (cont.) The tissue conductivity remains the same through out the experiment. The passing time and the heat, caused as a result of the current flow through the tissue, do not effect the conductivity.

I(t) Impedance = Noise Perpendicular Conductivity V(t) System Identification Optimization Tissue Simulation Tissue Simulation

I(t)V(t) Tissue Simulation Tissue Simulation Generating current - the system input Calculating voltage - the measured output Based on the mathematical model:

Mathematical Model

Input & Output Signals SignalNoise - SNR 10 [dB] Channel 1 Channel 2 Channel 3 InputsOutputs

I(t) Impedance = Noise V(t) System Identification Black box deciphering

A good abstract model, which can describe the system, is: UY e

Impedance Amplitude Channel 1 Channel 2 Channel 3 Impedance Phase Simulated valueEstimated valueUpper ToleranceLower Tolerance Impedance Estimation

Impedance = Perpendicular Conductivity Optimization The optimization concerns finding a parameters that minimize the mathematical model function

Optimization Sensitivity Points Unless the function is continuous and has one minimum only, there is no guarantee that the minimum achieved is the global minimum. Starting points dispersal

Estimated Parameters Simulated ValueEstimated Value Parameter AmplitudeParameter Phase

Perpendicular Conductivity Optimization I(t)V(t) Tissue Simulation Tissue Simulation Impedance = Noise System Identification

NOISE TIME HEAT HARDWARE

Sampling Noise I(t) Impedance = Perpendicular Conductivity V(t) System Identification Optimization Experimental System Experimental System

R1R1 R2R2 R3R3 AmplitudePhase Short Results

Parameter AmplitudeParameter Phase Short impedance estimation

Impedance AmplitudeImpedance Phase R3 R1 R2

Parameter AmplitudeParameter Phase Tissue impedance estimation

Conclusions and Future Improvements : There is a problem some where along the estimation process. The problem could be at any of the stages of signal collection or signal analysis. Future option: replacing the input sinusoidal signal with a white noise signal. The Optimization is the weakest link in the estimation chain. The estimation time and complexity may be reduced with a different algorithm.