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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55484
Title: | 雲端脈波分析系統與臨床資料測試 Cloud-based Pulse Analysis System with Clinical Data Examination |
Authors: | Wei Chen 陳維 |
Advisor: | 賴飛羆(Feipei Lai) |
Keyword: | 中醫,生醫資訊,資料處理, Traditional Chinese Medicine,Bioinformatics,Data Processing, |
Publication Year : | 2014 |
Degree: | 碩士 |
Abstract: | 中醫發展了五千年,近年來再度受到學者的關注。如何將近代的科學研究方法與精神,應用在傳統的中國醫學所描繪的人體系統與氣的循環上,是醫學研究者與資訊學研究者需要共同攻克的議題。中醫與現代醫學這兩種截然不同醫學描述方法,在脈波的研究時擦出了閃耀的火花。脈波也是波,能夠被醫療資訊研究與分析,分析的結果是血液動力學的模型能解釋的結論,也是中醫系統能夠闡述的脈象。
本研究開發了一雲端脈波的分析平台。該平台能用以累積脈波研究資料,並將資料分組分析。有鑑於先前的脈波分析使用了大量的時頻分析方法的共振頻分析方法,而該方法在脈波維持連續重複波形的特性下才具有數學意義,本研究於訊號前處理端亦開發了一於時域訊號進行週期波分析的演算法。該演算法能進行快速的週期波篩選以利於後續的脈波分析。 本研究之派波分析平台可提供所有進行脈波研究者所使用。本平台目前已收集六組研究小組之資料,包含安寧病房病患之脈波研究,敗血症病患之存活率研究,洗腎病患脈波研究,脈波於不同年齡層之研究,心血管疾病與脈波之研究,肺部疾病脈波之研究等等。經由統計與分析觀察脈波隨身體狀態之變化,本研究已有相當數量之結果與產出。 The proposed pulse analysis system provides a platform for researchers to analyze, share, and store their pulse data. A novice and easy time-domain algorithm is applied to the pulse signals for periodic function examination. Monitoring and modeling our body as a system with pulse using digital pulse signals is one of the linkages that we can find between Traditional Chinese Medicine (TCM) and modern Information Communication Technology (ICT). With the labeling of diseases or some modern Bio-markers, we are able to link TCM, ICT, and the latest medical science together. The proposed method is capable of calculating the instability of the pulse wave of subjects. After finding the starting point of each period in a periodic wave, we use set theory as the constraint to detect stable periodic wave. With normal heart rate checking and the variability of each period checking, our algorithm can detect whether the input signal is normal, stable and periodic. A coefficient that represents the instability is calculated by the average standard deviation of each period in the waveform. The proposed system helps the automation for pulse examination to select a proper segment for harmonic analysis. The system is capable of using Fast Fourier Transform (FFT), harmonics, Ensemble Empirical Mode Decomposition (EEMD) as feature extraction, and using two-sample t-test as statistics and classification. An example of hospice patient death prediction shows that near-death patients within 7 days can be significantly separated from the others by the Standard Deviations (SD) of harmonics with the proposed pulse analysis system. With the periodic function examination method, non-survival sepsis patients in Continuous Mandatory Ventilation (CMV) mode or Continuous Positive Airway Pressure (CPAP) mode can be significantly separated. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55484 |
Fulltext Rights: | 有償授權 |
Appears in Collections: | 生醫電子與資訊學研究所 |
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ntu-103-1.pdf Restricted Access | 1.29 MB | Adobe PDF |
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