人工耳蝸（CI）是一種治療患有嚴重聽力損失的手術。該過程需要在耳蝸內植入刺激裝置。由於每位患者俱有獨特的耳蝸形態和不同的電極位置，這將對我們控制耳蝸內電流的能力造成一些限制，進而可能導致不良的電刺激性能。為了解決這個問題，需要患者的特定模型來促進設備裝配和參數調整。然而，電極周圍的電阻抗和電流具體的分佈是未知的。直到今天，臨床上已經能夠使用電場成像（EFI）來偵測可能發生的問題，但是此方法只能粗略地檢查問題。 本論文的目的是提供一種患者特定的電阻抗網絡。過去曾有些方法可以估計組織電阻抗模型的參數（rLlongitudinal，rTranversal和ZInterface）。但這些方法無法找到所有所需的參數。在本文中，為了估計最優化參數，我們提出了一種迭代算法。本論文還推導出一種基於臨床試驗的新規則，該規則能夠對優化效果有顯著的提升。為了提高準確性，我們使用遺傳演算法，再次進行優化。最後，我們還提出了一種新的優化算法NESEM，可以從EFI中求解非線性方程，並取得最優參數。最後，我們可以根據優化的參數（rLongitudinal，rTransversal和ZInterface）創建患者特定的電阻抗網絡。

Cochlear implant (CI) is a procedure to treat people with profound hearing loss. This procedure required to implant a stimulating device inside the cochlea. As each patient has unique cochlear morphology and different electrodes position, its causing some limitations in our ability to control the intracochlear current flow. This problem could result into a poor electrical stimulation performance. To tackle this, a patient’s specific model is needed to facilitate device fitting and parameter adjustment. However, the specific configuration of electrical impedances and current distributions around the electrode is unknown. Until this day, Electrical field imaging (EFI) have been used by the clinicians to find any occurred problems, but this approach only gave a rough finding of the problem.

The purpose of this thesis is to provide a patient specific electrical impedance network. Previously, there are some method that can estimate the parameters (rLongitudinal, rTranversal, and ZInterface) of tissue electrical impedance model. However, these methods cannot find all the parameters needed. In this thesis, to estimate optimal parameters, we propose an iterative

algorithm. A new rule based on clinical trials were derived and significantly improve the optimization. To improve the accuracy more, a global optimization, Genetic Algorithm, were used to explore more optimum space. Lastly, we also proposed a new optimization algorithm called NESEM, which solved the nonlinear equations from EFI to acquire the optimal parameters. With these optimal parameters (rLongitudinal, rTransversal, and ZInterface) we can create a patient specific electrical impedance network.

摘要 ..................................................................................................................................... i
Abstract ...............................................................................................................................iii
Acknowledgments .............................................................................................................. v
Contents ............................................................................................................................. vi
List of Figures .................................................................................................................... ix
List of Tables .................................................................................................................... xiii

Chapter 1 Introduction ........................................................................................................ 1
1.1 Motivation............................................................................................................. 1
1.2 Thesis Organization .............................................................................................. 2

Chapter 2 Background Research ........................................................................................ 3
2.1 Normal Hearing Mechanism................................................................................. 3
2.2 Cochlear Implant (CI) ........................................................................................... 5
2.3 Equipment of Clinical Measurement .................................................................... 6
2.4 Electrode Configurations ...................................................................................... 8
2.5 Electrical Field Imaging and Modeling (EFIM) ................................................... 9
2.6 Tissue Impedance Model .................................................................................... 11
2.7 Potential Extraction ............................................................................................ 12
2.8 Impedance Model ............................................................................................... 13
2.9 Error calculation ................................................................................................. 14
2.10 Problem Statement ............................................................................................ 17

Chapter 3 Previous Method .............................................................................................. 18
3.1 Electrical Field Imaging Optimization Method (EFIOM) .................................. 18
3.1.1 Mathematical Matrix of Tissue Impedance Network .............................................. 18
3.1.2 Optimization........................................................................................................ 19
3.2 Bipolar Subtraction Method (BSM) ................................................................... 20
3.2.1 Bipolar-plus-two Impedance ................................................................................. 20
3.2.2 Bipolar-plus-one Impedance ................................................................................. 21
3.2.3 Current in Longitudinal and Transversal Direction .................................................. 21
3.3 EFIOM – BSM Hybrid Method.......................................................................... 23
3.3.1 Optimization........................................................................................................ 24

Chapter 4 Proposed Method ............................................................................................. 25
4.1 Local 1 Sensitivity Iterative Algorithm .............................................................. 25
4.2 Local Recursive 1-Sensititivity Iterative Algorithm ........................................... 35
4.3 Local 1 Sensitivity Iterative Algorithm with Upper Bound................................ 37
4.3.1 Upper Bound ....................................................................................................... 37
4.3.2 Optimization........................................................................................................ 39
4.4 Local Recursive 1-Sensitivity Iterative Algorithm with Upper Bound .............. 39
4.5 Nonlinear Equation based on Selective EFI Measurement (NESEM) ............... 39
4.6 Genetic Algorithm .............................................................................................. 43
4.7 Summary of methods .......................................................................................... 45

Chapter 5 Results .............................................................................................................. 49
5.1 RMSE comparison .............................................................................................. 49
5.2 EFIOM and HM.................................................................................................. 49
5.2.1 Tissue Impedance ................................................................................................ 49
5.2.2 EFI Error .............................................................................................................. 52
5.3 Iterative algorithm .............................................................................................. 54
5.3.1 Tissue Impedance ................................................................................................ 54
5.3.2 EFI Error .............................................................................................................. 56
5.4 Iterative Algorithm and Upper Bound ................................................................ 59
5.4.1 Tissue Impedance ................................................................................................ 59
5.4.2 EFI Error .............................................................................................................. 61
5.5 Genetic Algorithm, NESEM and upper bound ................................................... 63
5.5.1 Tissue Impedance ................................................................................................ 63
5.5.2 EFI Error .............................................................................................................. 65
5.6 Tissue impedance and EFI Comparison ............................................................. 68
5.7 Genetic Algorithm with Customs Current Levels .............................................. 72
5.7.1 Tissue Impedance – Compare Mutation Method.................................................... 74
5.7.2 Tissue Impedance – Compare Adaptfeasible .......................................................... 77

Chapter 6 Discussion ........................................................................................................ 81

Chapter 7 Conclusion ....................................................................................................... 83
7.1 Conclusion .......................................................................................................... 83
7.2 Future Work ........................................................................................................ 84
References......................................................................................................................... 85
Appendix. A Information of Subject................................................................................. 88
Appendix. B Genetic Algorithm Environment Experiment Result .................................. 89

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