Biomedical Signal Analysis based on AR model and Support vector machine for a wireless remote health monitoring system

本篇論文以國立台灣大學的無線奈米生醫檢測系統為發展藍圖，開發出一嵌入式生醫資訊監控裝置。無線奈米生醫檢測系統包含有植入式的體內感測器、生理監控裝置以及健康監控伺服中心。本文中實現了生理監控裝置以及健康監控伺服中心。此生理監控裝置架構在ARM9嵌入式系統上，其作業系統為Windows CE嵌入式作業系統。在健康監控伺服中心還建構了一訊號分析檢測系統於其內。本篇論文使用心電圖訊號作為生理訊號分析之範本，其中訊號分析主要以頻譜估測為基礎；醫學訊號檢測的判斷依據則是以支援向量機理論為基礎。This thesis describes the embedded system technology in the monitoring device for the National Taiwan University Wireless Health Advanced Monitoring Bio-Diagnosis System (WHAM-bios). The WHAM-bios consists of body-embedded sensors, a monitoring device, and a center health care server. A prototype of the monitoring device and the health care center is implemented in this thesis. The monitoring device is an ARM9 embedded system running Windows CE operating system. The biomedical diagnosis system is implemented in the center health care server. The thesis uses Electrocardiogram (ECG or EKG) as example. The ECG signal analysis is based on spectral estimation and the diagnosis is based on support vector machine algorithm.Contents謝 Ⅰ要 Ⅱbstract Ⅲ錄 Ⅳ目錄 Ⅵ目錄 Ⅸhapter 1 Introduction 1.1 Motivation and Objective 1.2 Related Works 3.3 Organization of this thesis 6.4 Contributions 7hapter 2 System Architecture and Environment 9.1 System Architecture 9.2 Hardware Architecture 10.2.1 Architecture introduction 10.2.2 Module A9M2410 11.3 Software Architecture 14.3.1 Windows CE operating system 14.3.2 Application Development Environment for Windows CE 14.3.3 Application Development Environment for Wireless LAN 19.3.4 Application Development Environment for Connection 20hapter 3 Windows CE 21.1 Windows CE Features 21.2 Windows CE Platform Development Architecture 23.3 Platform Builder 24hapter 4 Signal Analysis Theorem 33.1 Random Process 33.2 Spectral Estimation 40.2.1 Classical Spectral Estimation 40.2.2 Parametric Spectral Estimation 42.3 Support Vector Machine 50.3.1 Support Vector 50.3.2 Lagrange’s multiplier 53.3.3 Feature Space and Kernel [33] 56.3.4 Decision Function 57hapter 5 System Implementation 59.1 Hardware Implementation 59.2 Software Implementation 62.3 Signal Processing 68.3.1 ECG Signal Concept 69.3.2 ECG Signal Analysis 71.3.3 Feature Extraction 75.3.4 SVM Classification 78hapter 6 Conclusion and Future Work 89eference 9