Signal Processing for Blood Pressure Measurement
Date Issued
2015
Date
2015
Author(s)
Huang, Yen-Ming
Abstract
Blood pressure (BP) is one of the most important signs of human cardiovascular health. The precision measurement of the blood pressure is necessary in diagnosis and treatment of hypertension and the risks related blood pressure. While the traditional auscultatory method using mercury sphygmomanometer is still viewed as the most accurate non-invasive blood pressure measurement method, it is complicated and only suitable for medical personnel. Currently, self-blood pressure monitoring devices are popular in the market and widely used in homecare. Most of those devices are based on the oscillometric method, as it requires less professional training and is less sensitive to external noise. However, most of these work well on young healthy subjects, but show less precision in some cases such as older people. Most of the devices in the market can only provide single time BP value, it’s unable to see the continuous change in BP. However, by continuous way, we can get more physiological information than traditional non-continuous measurement. As a result, a novel blood pressure sensor and signal processing algorithm for removing noise have been developed in this study. It can accurately determine blood pressure non-invasively for all age group. The effective signal processing is based on Ensemble Empirical Mode Decomposition (EEMD) method to remove the noise from the sensor. Due to the non-stationary characteristics of BP, EEMD is practical to achieve accurate decomposition. The signal can be decomposed into several Intrinsic Mode Functions (IMFs) by EEMD. The results suggest that that the proposed EEMD can indeed effective separate the pure BP from the sensor output.
Subjects
Non-invasive blood pressure measurement,Signal Processing, HHT
SDGs
Type
thesis