Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
ICCEA '10 Proceedings of the 2010 Second International Conference on Computer Engineering and Applications - Volume 02
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Blood pressure is a significant vital sign; blood pressure monitoring has a great significance to determine the health status of parents. This paper proposes a continuous, non-invasive blood pressure monitoring system concept, which requires a minimum of calibrations. Using the changes in Pulse Transit Time (PTT) to derive changes in blood pressure is a new method to detect the continuous non-invasive blood pressure monitoring. The system proposed is also based on this method, but different from other systems, which use electrocardiograph (ECG) signal and photoplethysmography (PPG) signal to obtain the PTT. In this paper a new method is introduced with two PPG sensors located at different positions to obtain the PTT. The sensor is placed on the fingertip and the other one on the ear lobe. The optical signals obtained from the sensors will be sent to a micro-processor. According to the time difference of the pulse arrived between the sensors, microprocessor can calculate the PTT time and then the current blood pressure value with the predefined algorithm is calculated. Blood pressure values can be sent to a variety of terminals present in the wireless network, such as mobile phone, PC and medical monitoring systems. The two micro PPG sensors and a microprocessor form a wearable wireless sensor network on the body of the patient, this network will not disturb the patient's daily life and can achieve 24-hour continuous blood pressure monitoring. For the algorithm to estimate the changes in blood pressure from PTT time, researchers in previous studies tend to focus on finding the linear relationship between PTT and the changes in blood pressure. This paper considers the PTT as a unique parameter and also takes into account other vital signs of the patient such as height, weight, age, gender, body temperature, heart rate, etc., as these factors have an effect on blood pressure changes too. More experiments have to be performed and large amount of data has to be collected to develop the algorithm. The mathematical software Matlab and the available statistical methods will be adopted to analyze these data to obtain a precise algorithm for calculation of the correlations between blood pressure and multiple parameters. In future, more sensors can be added in this network such as ECG, Electroencephalography (EEG), etc., which can extend into a complete human vital signs monitoring system.