Blood Pressure Variation Trend Analysis Based on Model Study

EasyChair Preprint no. 716

Abstract

Blood pressure variability is an important risk factor of stroke and cardiovascular diseases, but people often ignored it because of the inconveniences of continuous blood pressure measurement. For the last decade people use ambulatory blood pressure measurement to estimate the trend of the blood pressure curve, but this method need to set a cuff around the upper arm and fully occluding the arm’s blood circulation during the recording period, makes people feel uncomfortable and affects the quality of sleep.

Now an innovative method can estimate blood pressure using electrocardiogram(ECG) and photoplethysmopraphy (PPG). The time delay between R peak and PPG feature point is reversely related to blood pressure. This is a potential method to improve comfort during measurement and the sensor can be designed as a wearable device.

This study collects ten patient’s data from the MIMIC II database, including ECG, PPG and arterial blood pressure (ABP) to verify the blood pressure estimate result. Five different blood pressure regression models with pulse arrival time (PAT) as the main parameters, heart rate, pulse wave interval and pulse width as auxiliary parameters were proposed. Blood pressure regression analysis through different blood pressure models.

Analysis of correlation and consistency in different blood pressure regression models in units of 60 seconds. The highest correlation between ABP and PPG is the peak of PAT. The blood pressure regression model PATALL-BP  is the best result. ABP with ECG correlation is 0.89 better than ABP with PPG correlation of 0.87. In consistency, PPG has the highest RRratio of 0.67, while ABP has the highest RRratio of 0.81. Comparing the regression models PATHR -BP with PATHR,PPI-BP , whether ABP or PPG can also see a slight improvement in consistency.

Keyphrases: blood pressure variability, Cuffless Blood Pressure Measurement., pulse arrival time

@Booklet{EasyChair:716,
  author = {Chen Pei-Ying and Ding Hao-Ren and Chen Mei-Fen and Lin Wen-Chen and Lin Kang-Ping},
  title = {Blood Pressure Variation Trend Analysis Based on Model Study},
  howpublished = {EasyChair Preprint no. 716},

  year = {EasyChair, 2019}}