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.

1] Francois Guibert “Augmented Internet of Things”, COMPUTEX TAIPEI , Jun 9 , 2014
[2] Saverio Romeo , “Wearable Technology | Towards a multidisciplinary approach - Beecham Research “ , Jan 26, 2015
[3] COManlises , JCDelaCruz , CFausto , LMAMuralla , DMTPayas and MJTPosada ,” Monitoring of Blood Pressure Using Photoplethysmographic (PPG) Sensor with Aromatherapy Diffusion” , School of Electrical Electronics and Computer Engineering, Mapua Institute of Technology, Manila, Philippines , 6th IEEE , 2016
[4] Toshiyo Tamura *, Yuka Maeda , Masaki Sekine and Masaki Yoshida , “Wearable Photoplethysmographic Sensors—Past and Present” ,Electronics, 2014
[5] K.H.Shelley , A.A.Alian , A.J.Shelley ,R. “Role of the photoplethysmogrphic waveform in the care of high-risk surgical patients” Anesthesiology , June , 2013.
[6] Alex Page , Moeen Hassanalieragh , Tolga Soyata , Mehmet K. Aktas , Burak Kantarci , Silvana Andreescu , “Conceptualizing a Real-time Remote Cardiac Health Monitoring System” , ResearchGate , August 2015
[7] Hyun-Min Lee , Dong-Jun Kim , Heui-Kyung Yang , Kyeong-Seop Kim , Jeong-Whan Lee , Eun-Jong Cha , Kyung-Ah Kim,“Human Sensibility Evaluation using Photoplethysmogram(PPG)”, CISIS , IEEE , 2009
[8] J. P. Phillips ; V. Cibert-Goton ; R. M. Langford ; P. J. Shortland, “Perfusion assessment in rat spinal cord tissue using photoplethysmography and laser Doppler flux measurements,” J. Biomed. Opt., 18(3) , 2013.