本論文主要是建立一套人體血管容積訊號(Photoplethysmography)檢測之光學分析系統。此系統可以成功擷取人體血管容積變動時之訊號，並針對所量測到的訊號探討運動前後和心率變異性之相互關係，其訊號處理的方式是先採用獨立成份分析法(Independent component analysis ,ICA)將所量測的訊號與雜訊做分離，再利用小波轉換分析(Wavelet transform analysis)的方式來對運動前後的生理訊號做時域-頻域分析，並與傳統的傅立葉轉換之方法做比較，以期望能夠比傳統方法更加準確判別心率的變異行為。
由分析結果得知，利用獨立成份分析法可將訊號中之低頻雜訊成功分離，去除雜訊的干擾將使得訊號分析的結果更加準確。使用小波轉換的確可以代替傅立葉轉換來分析心率變異，可同時可看出時域與頻域之間的關聯性。最後針對所觀察到的現象做歸納分析，希望未來能應用臨床上，提供醫生作為輔助診斷之工具。

A complete study of the optical analysis system via detecting the correlation for the variation of the photoplethysmography signal and the heartbeat signal between motionless and motion is presented in this dissertation. By designing an optical analysis system circuit, the photoplethysmography signal can be sensed. Next, the signal sensed can first be analyzed by Independent Component Analysis method for separating the biosignals and noises, then, making use of Wavelet transform analysis method deals with the biosignals in time domain and frequency domain, respectively. Finally, comparing Wavelet transform analysis method with traditional Fourier transform method expects that the variation behavior of heart rate variance can attain to measurement more accurately than the traditional measurement method in the literature.

By aforementioned results, the Independent Component Analysis method is very easy to separate the low frequency noise from signal sensed and make the signal processing more accurate. The Wavelet transform analysis method can replace Fourier transform method to approach the analysis of heart rate variance, observe the variation of mini frequency more perfectly and find out the related of time domain and frequency domain at the same time. Finally, using the analysis of summing up to the phenomena observed hopes to reach clinic application in the future and offer to doctor as the auxiliary tool for diagnosis.

目錄
摘 要 I
Abstract II
誌謝 III
目錄 IV
表目錄 IV
圖目錄 V
符號索引 VII
第1章 前言 1
1.1 研究動機與背景 1
1.2文獻回顧 2
1.3本文架構 3
第2章 理論分析 5
2.1 紅外線感測器 5
2.2 心率的定義 5
2.3 光體積變化信號量測原理 6
2.4 電路作動原理 8
2.5 獨立成分分析法 8
2.5.1 中央極限定理 10
2.5.2 共同資訊理論 12
2.5.3 最佳演算法 14
2.6 小波理論分析 20
第3章 實驗材料與方法 26
3.1 實驗材料 26
3.2 實驗方法與步驟 26
3.2.1 量測波形之驗證 26
3.2.2 獨立成份分析法分離訊號的能力 27
3.2.3 小波分析的運用 29
第4章 結果與討論 34
第5章 結論 47
5.1 結論 47
5.2 未來方向 47
參考文獻 48
作者簡介 50

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