臺灣博碩士論文加值系統

背景：慢性疼痛雖然沒有立即致命的危機，卻嚴重影響病患的生活。而慢性疼痛中以肌筋膜疼痛症(Myofascial pain syndromes, MPS)最為常見。疼痛對MPS病患而言是極欲解決之主訴，也是病人就診之主要原因。但由於個體間充滿差異，對於主觀的疼痛感覺之定量是非常困難的。因此，如何有效評估疼痛是一項極待解決的重大議題。目的：本研究透過非侵入式的方法量測治療前後的心電圖訊號(Electrocardiography, ECG)、光體積變化描記圖訊號 (Photoplethysmography, PPG)以及膚電反應訊號(Galvanic skin response, GSR)，進而分析參數比較治療前後的差異，再搭配臨床上常用的面部表情量表法 (Ｗong -Banker)、口語性等級刻度尺(Verbal rating scale, VRS)、視覺類比刻度尺(Visual analogue scale, VAS)以及壓痛閥值(Pain pressure threshold, PPT)來做為評估的依據，期望能促使肌筋膜疼痛症患者得到更好疼痛管理品質。方法：本研究收取37位MPS病患，藉由多訊號擷取系統，本研究同步量測肌筋膜疼痛患者於治療前後ECG、PPG以及GSR三種生理訊號，經LabVIEW軟體分析處理計算出三種訊號之生理參數值，再使用SPSS統計軟體(Statistic Package for Social Science, SPSS)進行統計分析。主要針對三種生理訊號於肌筋膜疼痛治療前後之療效進行探討評估。結果：研究結果經統計分析得知，疼痛量表以及PPT結果皆為顯著性的下降，代表著疼痛的緩解。而PPG以及ECG幾乎所有自主神經參數於治療前後，藉由自主神經活性觀察，有著與疼痛減緩趨勢相同之顯著性改變，而GSR之交感神經參數則具有下降的趨勢。結論：本研究建構了一個多生理參數的量測系統，可以在非侵入的情況下測量GSR、PPG以及ECG三項生理訊號，比較肌筋膜疼痛症於治療前後的差異。本研究之結果配合先前文獻可知，ECG以及PPG之參數評估慢性MPS痛症之舒緩是可行的。

Background: Chronic pain, although not immediately fatal crisis, but seriously affect the lives of patients. And chronic pain in myofascial pain syndrome (MPS) is most common. Pain in terms of MPS patients is depravity solution complaint, is the main reason of patient visits. However, due to differences between individuals filled, quantitating subjective sensation of pain it is very difficult. Therefore, how to effectively assess pain is an extremely important issue to be resolved. Purpose: This study through the electrocardiography (ECG), photoplethysmography (PPG) and galvanic skin response (GSR) signal before and after the non-invasive method of measuring treatment, further analysis parameters Compare the differences before and after treatment, and then with facial expressions commonly used in clinical scale method Wong -Banker, oral rating scale (VRS), visual analogue scale (VAS) and pain pressure threshold (PPT) to do based on the assessment, hoping to induce myofascial pain syndrome patients get better quality of pain management. Methods: The study collected 37 MPS patients, with multiple signal acquisition system, this study measured the myofascial pain patients before and after treatment ECG, PPG and GSR three physiological signals processed by LabVIEW software analysis .The physiological parameter value, and then use the SPSS statistical software (Statistic Package for Social Science, SPSS) for statistical analysis. It will focus on three physiological signals in myofascial pain before and after treatment efficacy were discussed. Results: Results Statistical analysis showed that the pain scale and PPT of the results are all significant of decline, representing the pain relief. And as well as almost all of the PPG and ECG autonomic nervous parameters before and after treatment, by autonomic nervous system activity observed with the same pain relief and significant changes in the trend, but sympathetic parameters of the GSR decreased. Conclusion: This study constructed a multiple physiological parameters measurement system that can measure the difference of GSR, PPG and ECG three physiological signals, and compare myofascial pain syndrome before and after treatment in the case of non-invasive. The results of this study with previous literature shows that the parameters of the ECG and PPG assessment relieve chronic pain of MPS are valid.

目錄
摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VII
第一章　緒論 1
1.1　前言背景 1
1.2 文獻回顧 3
1.2.1肌筋膜疼痛症 3
1.2.2生理訊號與疼痛 4
1.3研究動機與目的 5
1.4論文架構 6
第二章 基礎理論 7
2.1 肌筋膜疼痛症之理論 7
2.2 心電圖訊號原理 8
2.2.1心率變異度分析(HRV) 10
2.3 光體積變化描記圖原理 13
2.4膚電反應分析原理 14
第三章 系統架構與研究方法 15
3.1 硬體系統架構 15
3.1.1 心電圖類比訊號模組 16
3.1.2 光體積變化描記圖類比訊號模組 16
3.1.3膚電反應訊號模組 17
3.1.4 資料擷取卡USB-6009 17
3.2參數分析 19
3.2.1 ECG之R波偵測-動態閥值偵測法 19
3.2.2 PPG參數分析 21
3.2.3 GSR參數分析 24
3.3 實驗方法 24
3.3.1 實驗步驟 26
3.3.2 收案標準 27
3.3.3 電性安全檢測 27
3.3.4 統計分析 28
第四章 研究結果與討論 29
4.1研究結果 29
4.1.1疼痛量表與PPT閥值之分析結果 29
4.1.2生理參數之治療前後分析結果 30
4.1.3電性安全檢測結果 32
4.2研究討論 33
第五章 結論與未來展望 35
5.1結論 35
5.2未來展望 35
第六章 參考文獻 37
附錄一 42
A生理參數與VAS之相關性分析 42
B疼痛因子之分析結果 44
B.1性別因子(男/女) 44
B.2年齡因子(20~40歲/40~70歲) 47


圖目錄
圖1心臟切片示意圖 8
圖 2 心電圖波形 9
圖 3 R-R Interval 示意圖 10
圖 4功率密度圖 12
圖 5循環週期之PPG波形示意圖 13
圖 6 BL為標準PPG基線，PPGA為振幅，P為週期 14
圖 7系統架構圖 15
圖 8穿透式Nellcor DS-100A探頭 16
圖 9 NeXus NX-GSR1D 探頭 17
圖 10資料擷取卡USB-6009 18
圖 11 LabVIEW程式流程 19
圖 12動態閥值示意圖 20
圖 13 PPG之P Peak偵測示意圖 21
圖 14 PPG之P Valley偵測示意圖 21
圖 15 ECG Peak之手動補點人機介面 22
圖 16 PPG Peak之手動補點人機介面 22
圖 17 PPG Valley之手動補點人機介面 23
圖 18 GSR波動之自動取點人機介面(所有波皆擷取) 24
圖 19 GSR波動之自動取點人機介面(僅符合條件之波) 24
圖 20收案流程 25
圖 21心率變異度之治療前後分析成果 30
圖 22光體積變化描記圖參數之治療前後分析成果 31
圖 23膚電反應參數之治療前後分析成果 32
圖 24 ECG、PPG、GSR漏電流檢測結果 33
表目錄
表 1時域分析之指標 11
表 2頻譜分析之指標 12
表 3資料擷取卡USB-6009規格 18
表 4受測者基本生理參數 29
表 5 疼痛量表之分析成果 29
表 6 壓力疼痛閥值之分析成果 29

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