一種連續監測動脈壓之方法

Abstract

與心血管有關的疾病，如腦血管疾病、心臟病與高血壓是常見的死亡原因。相較於其他病症，心血管疾病較無明顯的病徵，心血管阻塞不易察覺，容易受到忽略而錯過治療時間，若能及早發現異狀可以降低心血管疾病的發生機率。 血壓是評估心血管疾病一項重要的指標。然而，現今被視為黃金標準的水銀血壓計，其量測血壓方式需由他人協助聽診，也容易因為壓脈帶的鬆緊度造成量測誤差，更無法長時間連續監測血壓。 為了開發連續監測血壓之方法，本研究利用壓力感測器與心電圖所測量之訊號，透過撰寫之峰值擷取方法計算心電圖訊號與橈動脈脈波訊號的波峰時間差距來獲得脈波傳導時間，再推算出脈搏波速度，最後將脈搏波速度與水銀血壓計量測之血壓數據進行線性迴歸模型的分析，建立推估血壓的演算法。 未來若能配合合適的機構，開發成穿戴式設備，並透過臨床試驗，收集更多數據分析後，將可以更有效的將此方法運用於血壓量測，達到長期追蹤血壓的功能。

Cardiovascular-related diseases such as cerebrovascular disease, heart disease, and hypertension are common causes of death. Compared with other diseases, cardiovascular disease has less obvious symptoms, cardiovascular obstruction is imperceptible, and it is easy to be ignored and miss the treatment time. As soon as possible find abnormalities that can reduce the incidence of cardiovascular disease. Blood pressure is an important evaluation indicator of cardiovascular disease. However, the mercury sphygmomanometer, which is now regarded as the gold standard, requires the assistance of auscultation by others. It is also easy to cause measurement errors due to the tightness of the cuff, and it is impossible to continuously monitor blood pressure for a long time. In order to develop a method for continuous monitoring of blood pressure, this study uses signals measured by force sensor and electrocardiograms and calculates the peak time difference between ECG signal and radial arterial pulse wave signal by the peak detection method written to obtain the pulse wave transit time. Then calculate the pulse wave velocity. The blood pressure data of mercury sphygmomanometer and the pulse wave velocity we obtained were analyzed by a linear regression model to establish an algorithm for estimating blood pressure. In the future, if we can use the right mechanism to develop a wearable device, and collect more data analysis through clinical trials, we will be able to apply this method to blood pressure measurement more effectively and achieve long-term blood pressure tracking.