Proceedings of the 5th International Conference on Information Technology and Application in Biomedicine, in conjunction with The 2nd International Symposium & Summer School on Biomedical and Health Engineering Shenzhen, China, May 30-31, 2008
A LabVIEW Based Measure System for Pulse Wave Transit Time J. M. Zhang, P. F. Wei and Y. Li Abstract—Pulse wave transit time (PWTT) is used as a non-invasive and cuffless method for blood pressure estimation. In this paper, we design a system that can measure PWTT by monitoring ECG and pulse wave continuously. The system includes analog signal sampling in PCB, signal display and data processing in computer. We measure pulse wave by the photo-plethysmograph (PPG) device in finger which includes an infrared LED transmitting light, photodiode in OPT101 as detector, amplifier and filters. We measure ECG by the sensor on limb. We design amplifier, 0.01Hz high pass filter, 75Hz low pass filter, and 50Hz notch filter. After filtering and amplification, ECG and PPG are sampled by MCU and then the data are transmitted to computer. LabVIEW is used to receive, display and process these data, and finally figure out the PWTT. Based on PWTT value, we coarsely calculate the SBP. Keywords—pulse wave transit time, ECG, PPG, LabVIEW
I. INTRODUCTION
B
LOOD pressure (BP)
is the force applied against the walls of the arteries as the heart pumps blood through the body. It is one of the most important physiological parameters. It gives lots of useful information about cardiovascular condition and the therapeutic effect. Therefore, it is very important to measure the blood pressure accurately on clinic or medical research. There are two methods, namely the invasive method and the non-invasive method to obtain the BP. The invasive method can obtain BP continuously and accurately, but it is hard to set up and in the danger of infection. The common non-invasive methods are Korotkoff and oscillometric methods [1]. These methods are easy to obtain BP, but need to use a cuff. They measure the pressure only at a single point of time and are not accurate enough. In 1922, Bazzett had found that pulse wave velocity (PWV) is related not only to BP, but also to the cubage and flexibility of the arteries [2]. Thus, V could be a useful and convenient parameter for continuous monitoring of calculating blood pressure. However, it is hard to measure PWV in clinic. So people replace PWV by pulse wave transit time (PWTT) and thus describe the relation to BP. In 1957, Lansdown put forward that the relation between PWTT and BP in physiology is linear, and the relation is likely steady for individual during certain time [3]. Generally speaking, if BP increases, PWTT decreases and vice versa.
PWTT is the time interval between two pulses detected on the same artery. Usually, PWTT is defined as the time interval between two characteristic points – the R peak of the electrocardiogram (ECG) and the peak of the pulse at finger, ear lobe, or toe [4]. So PWTT is measured by monitoring ECG and Pulse wave in this study. The two signals are recorded during the measurement which increases the accuracy by extra information of the blood pressure. This paper focuses on the initial work to develop a monitoring system and measure PWTT by LabVIEW. Based on the results, systolic blood pressure (SBP) is coarsely estimated. LabVIEW (Laboratory Virtual Instrumentation Engineering Workbench) is a platform and development environment for a visual programming language from National Instruments [5]. LabVIEW helps create flexible and scalable design, control, and test applications. With the help of LabVIEW, we can interface with real-word signals; analyze data for meaningful information; and share results through intuitive displays and reports. The remainder of this paper is organized as follows. Section II gives the general block diagram of the measure system and the specifications of amplifier and filters. Section III describes the process of ECG and PPG in detail and the detection of PWTT. Section IV approximates the linear function based on PWTT and SBP, and compares the calculated SBP with measured SBP. Section V summarizes this paper.
The authors are with Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology; Key Laboratory for Biomedical informatics and Health Engineering, Chinese Academy of Sciences, (phone: +86-0755-26803567; e-mail:
[email protected];
[email protected];
[email protected] ).
978-1-4244-2255-5/08/$25 ©2008 IEEE
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Amplifier High pass filter
Low pass filter
High pass filter Amplifier Low pass filter
Right-leg driver Notch filter
LabVIEW
Serial port
Notch filter
Micro-controller
Fig.1 ECG and PPG measurement
II. ECG AND PPG MEASUREMENT Photo-plethysmograph (PPG) is a non-invasive method to detect cardio-vascular pulse wave that propagates through the body by a light source and a detector. PPG signal indicates the volume changes in the blood vessels. PPG sensor is put on finger-tip to acquire the reliable and stable PPG signal from people [6]. ECG signal is measured by standard limb lead I. The block diagram of ECG and PPG measurement system is shown in Fig.1. PPG sensor on finger-tip includes infrared LED (940nm) and photodiode. Infrared LED is used to transmit light and photodiode in OPT101 works as the light detector [7], as illustrated in Fig. 2. Infrared LED Cuff Finger Photodiode in OPT101 Original PPG Amplifier Fig.2 PPG Sensor on finger-tip
The signal collected by the sensor is very weak, and it includes AC signal which contains the information about pulse wave, DC signal, and some noises. To amplify the required AC signal, we design the 0.1Hz high pass filter to remove the DC signal. The frequency range of pulse wave is from 0.1Hz to 60Hz, and the most is less than 20Hz. Thus, another 40Hz low pass filter is adopted to remove the high-frequency interference [8]. Power noise is the main noise in this circuit, and can be restrained by 50Hz notch filter [9]. ECG is a representation of the heart’s electrical activity and recorded by the electrodes on both the right arm and left arm. In ECG measurement, 0.01Hz high pass, 75 Hz low pass, and 50Hz notch filters are necessary. These filters not only eliminate the disturbance caused by the 50Hz working frequency, but also suppress the interference caused by the outside sources. We also design a right-leg-drive circuit in order to suppress common-mode interference. Thus, it improves the common-mode rejection ratio in preamplifier.
signal are sampled by analog-to-digital converter which located in a micro-controller. We used MSP430F149 for micro-controller to process the data [10]. It is a 16-bit CPU from Texas Instruments, and is designed for low power measure device. Some usual peripherals, such as ADC and SPI, are included in the chip. The signal sample rate is 250Hz and the resolution of A/D is 12bit. Micro-controller connects the computer by serial port. The PCB of the system is shown in Fig. 3 [11]. III. PWTT DETECTION LabVIEW programs are called virtual instruments (VIs). Each VI has three components: a block diagram, a front panel and a connector pane. We program in the block diagram. There are lots of blockettes provided for our programming from LabVIEW, such as filters.vi, windows.vi and so on. When we receive data from serial port, we need to know how to use the resource of VISA in LabVIEW. Baud rate and the signal format of VISA must be consistent with those of MCU. After LabVIEW received the data, we need to process and transform wavelet to find the characteristic points of ECG and PPG signals. As mentioned before, PWTT is defined as the time interval between the R peak of ECG and the minimum point of PPG, as illustrated in Fig. 4.
ECG
PPG
Fig.4 The definition of PWTT
The physiologic signal processing is implemented in two steps, namely the baseline wander and the significant point detection. In the first step, the method based on shifting window is adopted to remove the baseline wander of ECG and PPG. This method is easy to be implemented, especially to our real-time system. Assume the original signal is sig1 with the length of L. The procedure of the algorithm is briefly described as follows: 1) Select the appropriate width (W) of the shifting window. It must be smaller than L, and it’s an odd integer in general. Here we assume that W equals 101. 2) Extend the original signal (sig1) to sig2, and thus avoid the edge effect. Sig2 is obtained from the Eqn. (1). sig 1( i ) ⎧ ⎪ W −1 ⎪ sig 2( i ) = ⎨ sig 1( i − ) 2 ⎪ ⎪⎩ sig 1( L − 1)
Fig.3 The PCB of system
After filtering and amplification, ECG signal and PPG
W −1 2 W −1 W +1
