OBJECTIVE: This study analyzes the peak resistance frequency (PRF) method described by Mercanzini et al., a method that can easily extract the tissue resistance from impedance spectroscopy for many neural engineering applications but has no analytical description thus far. METHODS: Mathematical analyses and computer simulations were used to explore underlying principles, accuracy, and limitations of the PRF method. RESULTS: The mathematical analyses demonstrated that the PRF method has an inherent but correctable deviation dependent on the idealness of the electrode-tissue interface, which is validated by simulations. Further simulations show that both frequency sampling and noise affect the accuracy of the PRF method, and in general, it performs less accurately than least squares methods. However, the PRF method achieves simplicity and reduced measurement and computation time at the expense of accuracy. CONCLUSION: From the qualitative results, the PRF method can work with reasonable precision and simplicity, although its limitation and the idealness of the electrode-tissue interface involved should be taken into consideration. SIGNIFICANCE: This paper provides a mathematical foundation for the PRF method and its practical implementation.
OBJECTIVE: This study analyzes the peak resistance frequency (PRF) method described by Mercanzini et al., a method that can easily extract the tissue resistance from impedance spectroscopy for many neural engineering applications but has no analytical description thus far. METHODS: Mathematical analyses and computer simulations were used to explore underlying principles, accuracy, and limitations of the PRF method. RESULTS: The mathematical analyses demonstrated that the PRF method has an inherent but correctable deviation dependent on the idealness of the electrode-tissue interface, which is validated by simulations. Further simulations show that both frequency sampling and noise affect the accuracy of the PRF method, and in general, it performs less accurately than least squares methods. However, the PRF method achieves simplicity and reduced measurement and computation time at the expense of accuracy. CONCLUSION: From the qualitative results, the PRF method can work with reasonable precision and simplicity, although its limitation and the idealness of the electrode-tissue interface involved should be taken into consideration. SIGNIFICANCE: This paper provides a mathematical foundation for the PRF method and its practical implementation.
Authors: Matthew R Behrend; Ashish K Ahuja; Mark S Humayun; Robert H Chow; James D Weiland Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2011-04-19 Impact factor: 3.802
Authors: Rio J Vetter; Justin C Williams; Jamille F Hetke; Elizabeth A Nunamaker; Daryl R Kipke Journal: IEEE Trans Biomed Eng Date: 2004-06 Impact factor: 4.538
Authors: Aditi Ray; Leanne Lai-Hang Chan; Alejandra Gonzalez; Mark S Humayun; James D Weiland Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2011-10-06 Impact factor: 3.802
Authors: Scott F Lempka; Svjetlana Miocinovic; Matthew D Johnson; Jerrold L Vitek; Cameron C McIntyre Journal: J Neural Eng Date: 2009-06-03 Impact factor: 5.379