以非侵入式生理訊號連續預測血壓系統與其無線同步機制

Abstract

心血管疾病（CVD）已經成為全球死亡的主要原因（據世界衛生組織2016年6月的報導）。其中，一個簡易而有效來預防心血管疾病的方法即是測量和監測血壓（BP）的數值，而血壓常被作為心血管系統的生理指標。先前的研究提出使用脈搏傳播時間（PTT）信息來計算血壓。在本篇論文中，我們提出一種基於線性回歸模型的新方法，其包含靜態和動態脈搏傳播時間（DPTT）作為參數，以提供更準確的預測血壓數值。此外，所提出的模型中，將預測的收縮壓（SBP）作為估計舒張壓時的參數，實驗結果顯示能提高舒張壓預測能力，顯示預測的收縮壓為估計舒張壓預測提供了有用的信息。依據本論文的實驗結果顯示，相比先前研究中單以靜態脈搏傳播時間的預測結果，本模型預測出之血壓數值與其真實數據（ground-truth）的相關係數（correlation of coefficient）顯著提高，且均方誤差（mean squared error）顯著下降，顯示本論文中提出的模型可以更準確地預測血壓，這些結果證實了將動態脈搏傳播時間納入準確血壓預測模型參數的有效性。此外，我們提出一種用於本血壓預測模型的無線系統與其心電圖（ECG）和光電容積圖（PPG）傳感器間的同步機制。本論文也將展示初步實驗結果和未來實踐的建議。

Cardiovascular diseases (CVDs) have become the leading cause of death globally (as reported by the WHO, June 2016). An effective method of preventing CVDs is to measure and monitor blood pressure (BP), which serves as a physiological indicator for cardiovascular systems. A previous research has proposed the use of pulse transit time (PTT) information to compute the BP measure. We propose herein a novel method based on a linear regression model that incorporates static and dynamic PTT features to predict BP measures more accurately. Moreover, the proposed model considers the estimated systolic blood pressure (SBP) when estimating the diastolic blood pressure (DBP), our experimental results demonstrate that the proposed method attains a better DBP prediction capability. The experimental results show that the proposed method can predict the BP more accurately than conventional methods, with notably higher correlation scores and lower mean square errors. Besides, a wireless system for our blood pressure prediction algorithm and its synchronization mechanism between ECG (electrocardiography) and photoplethysmogram (PPG) sensors is also proposed. In addition, the preliminary experimental results and advices for future implementation will be shown in the thesis.