臺灣博碩士論文加值系統

本論文利用穿透式之光體積描記法(Photoplethysmography, PPG)量測人體脈搏波形(cardiac pulse waveforms)，提出一套生理訊號演算法，分析呼吸系統(respiratory system)與心肺系統(cardiopulmonary system)之生理節律(physiological rhythms)及同步生理交互作用。
以小波轉換(Continuous Wavelet Transformation, CWT)為基礎，以其能分析即時(real time)頻譜分布(spectrum)的特性，即時監控心率(Heart Rate, HR)與心率變異度(Heart Rate Variability, HRV)，證明心率高(~2Hz)與心率低(~1Hz)時分別對應到吸氣(inspiration)與吐氣(expiration)時間。利用主峰心率(peak frequency of HR)描繪心率變異波形與提取呼吸頻率(~0.1Hz)；並由其權重(weighting)極大值訊號推論出呼吸波形。
以反小波轉換(Inverse Continuous Wavelet Transformation, ICWT)能夠重建無時間延遲訊號的特性，將心跳訊號(heart beat signal)分成吸氣與吐氣時之心跳訊號，並繪出其演化圖。再由重建之呼吸波形判斷呼吸深度(depth of respiration)。最後根據心跳倍頻之反小波轉換圖，解釋呼吸頻率(~0.1Hz)造成心率以0.1Hz的拍頻疊加出心跳波形。

In this thesis, we use transmitted PPG to measure human cardiac pulse waveforms. And we develop the algorithm for physiological signal to analyze the physiological rhythms and synchronized physiological interaction of cardiopulmonary system and respiratory system.
Based on Continuous Wavelet Transformation(CWT) and the CWT characteristic of analyzing real time spectrum, instantly monitor heart rate(HR) and heart rate variability(HRV) to prove that the moment of high HR(~2Hz) and low HR(~1Hz) corresponds to inspiration and expiration, respectively. We apply peak frequency of HR to depict waveform of HRV and obtain the frequency of respiration. And pick the signal of maximum weighting to derive the waveform of respiration.
According to the characteristic of Inverse Continuous Wavelet Transformation(ICWT), we can rebuild the signal without time delay and separate the HR signal into two parts, HR with inspiration and HR with expiration. Moreover, depict the evolution of HR waveform. Based on rebuilt waveform of respiration, determine the depth of respiration. Finally, by applying ICWT to second harmonic of HR, explain that the frequency of respiration(~0.1Hz) causes HR to form the waveforms with 0.1Hz beat frequency.

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 viii
第一章 緒論 1
1-1　前言 1
1-2　光體積描記訊號 3
1-3　心率變異度 5
1-4　呼吸與心率間之相關性 8
第二章 實驗架構與PPG訊號量測 9
2-1　實驗架構 9
2-2　雷射穩定度及光偵測器取樣頻率 10
2-3　原始PPG訊號之訊雜比 11
2-4　提升PPG訊雜比之演算法 12
第三章 運用連續小波轉換解析呼吸與心率變異度 13
3-1　連續小波轉換(CWT)運用於PPG訊號 13
3-2　小波轉換x-y極值演算法 18
3-3　小波轉換x-z極值演算法 20
3-4　小波轉換x-z極值之相位偏移 23
第四章 運用反小波轉換解析耦合效應 25
4-1　反小波轉換(ICWT)運用於PPG之小波轉換圖 25
4-2　利用反小波轉換分析呼吸深度 30
4-3　受控於呼吸耦合效應之心跳波形演化 32
4-4　利用倍頻解析呼吸與心率間之耦合效應 34
第五章 結論與未來展望 37
參考文獻 41

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