Continuous, Non-invasive and Cuff-free Blood Pressure Monitoring ...

2012 Andean Region International Conference

Continuous, Non-invasive and Cuff-free Blood Pressure Monitoring System Fredy Rivera Ingenier´ıa de Sistemas Universidad de Antioquia Medell´ın, Colombia Email: [email protected]

Juan Franco, José Aedo Ingenier´ıa Electrónica Universidad de Antioquia Medell´ın, Colombia Email: [email protected], [email protected]

Abstract—This paper presents a continuous and non-invasive blood pressure monitoring system, which can measure the blood pressure without the use of a inflatable cuff. A wearable IEEE 802.15.4-based Wireless Body Sensor Network (WBSN) composed by a photoplethysmographic (PPG) sensor node located on the forehead, and an electrocardiographic sensor node located on the chest, is used to measure the propagation time of the pressure wave from the chest to the forehead. This propagation time is related with the user blood pressure through a mathematical model, which is solved by the WBSN without sending raw signals from the sensors to a more complex device, such as a laptop or cell phone. On-node processing and blood pressure cuff-free estimation allow reducing energy consumption, while the location of the sensor nodes provides the user with a complete freedom of movement. After evaluating the system, experimental results show that the proposed system has the potential for constantly and non-invasively measuring the blood pressure of a person during daily activities.

cardiac cycle: the systolic pressure (the highest), and the diastolic pressure (the lowest). In [3], the authors reported a classification of blood pressure measurement methods. The invasive and noninvasive cuff-based methods are not suitable for continuous monitoring of blood pressure. The first one is based on the insertion of a catheter into an artery, which must be performed by a doctor and requires proper medical and environmental conditions to avoid infection. Meanwhile, the second ones require total or partial occlusion of artery due to the cuff pressure. Therefore, they are not suitable for long monitoring periods. Moreover, these cuff-based methods are also inefficient in terms of energy, due to the use of a pump to inflate the cuff. Methods based on the photoplethysmographic (PPG) approach have the potential to provide continuous and noninvasive blood pressure monitoring [4]. However, some systems reported in the literature, which use these methods, have problems such as motion artifacts, difficulties to be worn while doing another activity (intrusive) [5], and the need for a sophisticated device for estimating the blood pressure, such as a smartphone or a computer [6]. Wireless body sensor networks (WBSNs) have been proposed to overcome the aforementioned difficulties [7]. A WBSN is a network of nodes or devices with sensing, processing, and wireless communication capabilities, which can be used to estimate the blood pressure using noninvasive cuff-less methods, allowing the construction of systems that are less intrusive, non-invasive, lightweight, low power, motion tolerant, and easy to place in a user. In this paper, we present a wearable IEEE 802.15.4-based WBSN for continuously and non-invasively estimating the blood pressure. First, the blood pressure estimation concept is described. Then, the system prototype is explained. Later, the system is evaluated by comparing the experimental results with those obtained using a standardized cuff-based method. Finally, conclusions and future work are discussed.

Keywords-Wearable computers and body area networks; Wireless sensor networks;

I. I NTRODUCTION The need for continuous and non-invasive monitoring of physiological parameters in both military and civilian applications has led to tremendous growth in systems research with this aim. Some factors that have motivated the research on the subject are the growing percentage of aging population, and chronic diseases caused by lifestyle changes, leading to the need for constantly measuring the health status of individuals along their daily routine to prevent lifethreatening disorders [1]. In particular, blood pressure is one of the most important vital signs. The increase in blood pressure or hypertension is one of the factors that significantly raises the rate of morbidity and mortality in developed countries. One out of three U.S. adults suffers from hypertension, and about 8% of U.S. adults have undiagnosed hypertension, representing an annual cost to the U.S. government of 50.6 billion dollars in terms of direct costs (hospital care), and indirect costs by decreasing productivity [2]. Blood pressure is the pressure, measured in millimeters of mercury (mmHg), exerted on the walls of blood vessels by the action of the circulating blood. To measure blood pressure, it is necessary to obtain two pressures of the 978-0-7695-4882-1/12 $26.00 © 2012 IEEE DOI 10.1109/Andescon.2012.17

II. B LOOD P RESSURE E STIMATION C ONCEPT Equations (1) and (2) show the estimation models used to determine the systolic and diastolic blood pressures, respectively, where α, β, α and β are parameters to be 25 31

calibrated for each user. These models are based on heuristic models reported in [8]. In these equations, the pulse arrival time (PAT) is defined as the time delay between the R-peak of the QRS wave from the ECG signal, and the middle of the pulse inrush of the PPG signal. These features are extracted by means of a WBSN comprised of a PPG sensor node located on the forehead, and an ECG sensor node located on the chest. Psystolic =

α +β P AT

(1)

Pdiastolic =

α + β P AT

(2)

Figs. 1 and 2 show the physical appearance of the sensor nodes, while in table I their physical dimensions are summarized.

The PPG sensor node transmits to the ECG sensor node the time stamp at which the middle of the pulse inrush of the PPG signal occurs. Meanwhile, the ECG sensor node process the ECG signal to find the time stamp of the R-peak. When both time stamps are available, the ECG sensor node calculates the PAT. Because the PAT is calculated by the ECG sensor node, the transmitted PPG time stamp must be referred to the ECG sensor node clock. Therefore, the clocks of sensor nodes must be synchronized, as it is explained further on.

Figure 1.

PPG sensor node (both sides)

Figure 2.

ECG sensor node (both sides)

Table I P HYSICAL DIMENSIONS OF THE NODES Node ECG PPG

length (cm) 7.1 6.7

width (cm) 4.5 3.8

height (cm) 1.2 1.5

III. S YSTEM P ROTOTYPE A. System Architecture B. Signal Processing of PPG and ECG Signals

The sensor nodes are generic and they can be used not only for estimating the blood pressure but also other vital signs such as pulse oximetry, heart rate, respiration rate, and user movement. The PPG signal is acquired by means of a reflective forehead sensor, reference 8000R from Nonin Medical. This signal is processed by an integrated module, reference OEMIII, also from Nonin Medical. The OEM-III generates a digital pulse indicating the middle of the pulse inrush of the PPG signal. This digital pulse is captured by an ATmega128RFA1 system-on-chip (SoC) from Atmel, which transmits the time stamp of this pulse to an ATmega128RFA1 in the ECG sensor node. The ATmega128RFA1 SoC is comprised by an AVR microcontroller and an IEEE 802.14.5 RF transceiver (AT86RF231). The ECG signal is acquired with a single lead configuration (RA and LA electrodes), plus a right leg drive electrode (RL). The ECG signal is processed by a MSP430F5438A microcontroller from Texas Instruments to find the R-peak feature. Once this feature is found, the MSP430F5438A produces a digital pulse. The ATmega128RFA1 in the ECG sensor node calculates the PAT value with the time stamp of the previous pulse, and the time stamp received from the PPG sensor node. Finally, the systolic and diastolic blood pressures are estimated with the heuristic models shown in (1) and (2).

As mentioned before the PPG signal is processed by the module OEM-III. The ECG signal is pre-processed using a the discrete time continuous wavelet transform (DT-CWT), as defined in [9]. The sampling frequency equals to 250 Hz and the wavelet central frequency peak is equal to 17 Hz. Once the ECG signal is pre-processed with the DT-CWT, a variable threshold method is used to detect the R-peak. The variable threshold method implemented is based on the work proposed in [10]. The R-peak detection algorithm was implemented as a finite state machine. The microcontroller system clock was 8 MHz, and the operation voltage 3.3 V. Since the sampling time of the ECG signal is 250 Hz, there is a 4 ms time window to apply the DT-CWT, adjust the variable threshold, and find the R-peak. The worst execution time of the algorithm was 741 μs, measured with a Logic Analyzer LA1034 Logic Port from Intronix. The amount of flash memory used was 1136 bytes. The amount of RAM memory used during run time was 104 bytes. C. Sensor Nodes Communication Protocol The radio technology selected to communicate the sensor nodes was the IEEE 802.15.4 in beacon mode. To implemented it, the MACv2.6.1 stack library from Atmel was used. The software stack is executed by the on-chip AVR

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A. Experimental Setup

microcontroller of the ATmega128RFA1 SoC. In this work, all communications are conducted in the contention access period. A star network topology was implemented. The ECG sensor node acts as the network coordinator, and the PPG sensor node acts as a reduce function device, as defined in the IEEE 802.15.4 standard.

Fig. 4 shows the experimental setup used to calibrate and evaluate the blood pressure estimation system. The calibration and evaluation experiments were conducted on one user. Two data sets were gathered. The first one was for calibration, and the second one was for evaluation. In both experiments, the measures delivered by the estimation device were compared with an automatic measuring instrument (Omron HEM-7200) based on the oscillometric method. The mean absolute error, mean error, and standard deviation of the error are reported.

D. Sensor Nodes Synchronization To measure the PAT, the clocks of the PPG and ECG sensor nodes must be synchronized. Accurate blood pressure estimation requires up to 100 μs precision [6]. Therefore, in this work the synchronization strategy was implemented at the MAC layer level using hardware features offered by the ATmega128RFA1. To evaluate the synchronization strategy, an experiment was designed. On both sensor nodes a simultaneous external interrupt was generated at an input port of each of the ATmega128RFA1. Each node reported the time instant at which the event occurred. A total of 2052 events were generated at a rate of one event every 5 seconds. The beacon interval was 2 seconds. Fig. 3 shows an histogram of the error. The error is measured as the difference between the times captured by the ECG sensor node and the PPG sensor node, respectively. The error ranges from -16 μs to 64 μs. Therefore, with this error, it is posible to reach the precision required to estimate the blood pressure according to [6].

S1 System for estimating the blood pressure (device under test)

Cuff S2

Figure 4.

Blood pressure monitor (reference measuring instrument)

Experimental setup

B. Evaluation Results Table II shows the mean absolute error, mean error, and standard deviation of the error between the estimation system and the reference instrument.

ESTIMATION ERROR HISTOGRAM

Table II C OMPARISON BETWEEN THE ESTIMATION SYSTEM AND THE

NUMBER OF 1000 EVENTS 900

874

REFERENCE INSTRUMENT

823 800 700 600

Pressure

Mean absolute error (mmHg)

Mean error (mmHg)

Systolic Diastolic

4.3578 4.2033

0.7905 -3.5938

500 400 300

Standard deviation of the error 5.5056 3.2920

215 200 117 100 21 -16

Based on the American Association for the Advancement of Medical Instrumentation (AAMI) requirements (5±8 mmHg), the proposed system for estimating the blood pressure has the potential for measuring constantly and non-invasively the blood pressure of a person during daily activities.

2

0 0

16

32

48

64

Error

μs

Figure 3. Histogram of the time estimation error that shows the distribution of the 2052 events

C. System Power Consumption The ECG and PPG sensor nodes were powered by a 3.8 V regulated power supply in order to determine their current consumption. The power supply’s positive wire is passed through a current to voltage converter whose output is recorded with an oscilloscope. For the PPG sensor node, the maximum current consumption in continuos operation, i.e. each cardiac cycle, is

IV. S YSTEM E VALUATION This section addresses the evaluation of the proposed system for estimating the blood pressure, comparing the measurements of systolic and diastolic blood pressures that it reports with those obtained with a reference measuring instrument.

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R EFERENCES

approximately 41.3 mA at 3.8 V. Therefore, the PPG sensor node can operate without interruption an ideal amount of time of 1000 41.3 = 24 hours. Meanwhile, the ECG sensor node could reach, in continuous operation, 1000 18.6 = 53.8 hours.

[1] T. Yilmaz, R. Foster, and Y. Hao, “Detecting vital signs with wearable wireless sensors,” Sensors, vol. 10, no. 12, pp. 10 837–10 862, 2010. [Online]. Available: http://www.mdpi.com/1424-8220/10/12/10837

V. C ONCLUSIONS

[2] V. Roger, A. Go, D. Lloyd-Jones, E. Benjamin, J. Berry, W. Borden, D. Bravata, S. Dai, E. Ford, C. Fox, H. Fullerton, C. Gillespie, S. Hailpern, J. Heit, V. Howard, B. Kissela, S. Kittner, D. Lackland, J. Lichtman, L. Lisabeth, D. Makuc, G. Marcus, A. Marelli, D. Matchar, C. Moy, D. Mozaffarian, M. Mussolino, G. Nichol, N. Paynter, E. Soliman, P. Sorlie, N. Sotoodehnia, T. Turan, S. Virani, N. Wong, D. Woo, and M. Turner, “on behalf of the american heart association statistics committee and stroke statistics subcommittee. heart disease and stroke statistics-2012 update: a report from the american heart association,” Circulation, pp. e88–e98, 2012.

This paper presented the implementation of a system for continuously and non-invasively estimating the blood pressure. The estimation method uses the pulse arrival time (PAT) derived from photoplethysmographic (PPG) and electrocardiographic (ECG) sensors. The PAT is measured by means of a wireless body sensor network (WBSN) comprising of PPG and ECG sensor nodes. Blood pressure is estimated within the ECG sensor node, and therefore, a cell phone or personal computer could be only needed to visualize the blood pressure values in applications that demand it. This feature enables reducing both power consumption, and system cost, and also providing with free movement to the user. Thanks to the proposed location of the nodes, the user can perform daily activities, eliminating the discomfort produced by systems that require the installation of sensors in the fingers.

[3] G. Fortino and V. Giampá, “Ppg-based methods for non invasive and continuous blood pressure measurement: an overview and development issues in body sensor networks,” in Medical Measurements and Applications Proceedings (MeMeA), 2010 IEEE International Workshop on, 302010-may1 2010, pp. 10– 13. [4] C. Poon and Y. Zhang, “Cuff-less and noninvasive measurements of arterial blood pressure by pulse transit time,” in Engineering in Medicine and Biology Society, 2005. IEEEEMBS 2005. 27th Annual International Conference of the, jan. 2005, pp. 5877 –5880.

A. Future Work

[5] P. Shaltis, A. Reisner, and H. Asada, “Cuffless blood pressure monitoring using hydrostatic pressure changes,” Biomedical Engineering, IEEE Transactions on, vol. 55, no. 6, pp. 1775– 1777, june 2008.

Thanks to the time synchronization strategy developed, it is possible to synchronize more than one sensor node with the network coordinator. In future works, more sensor nodes could be included to achieve more accurate results. The development of the blood pressure estimation system was guided by the reduction in energy consumption. However, further power consumption optimization is posible. Particularly, by using and coordinating low power modes of the different radios and microcontrollers, and by adjusting their duty cycle. The PPG sensor node uses an integrated module from Nonin Medical. This module is currently being replaced by a different alternative to reduce manufacturing costs, and improve measurement algorithms in the presence of motion. After evaluating the system, results showed that the proposed system has the potential for measuring constantly and non-invasively the blood pressure of a person during daily activities. However, it is necessary to provide more efficient methods of calibration, and to validate the system with a greater number of users.

[6] J. Espina, T. Falck, J. Muehlsteff, Y. Jin, M. Adan, and X. Aubert, “Wearable body sensor network towards continuous cuff-less blood pressure monitoring,” in Medical Devices and Biosensors, 2008. ISSS-MDBS 2008. 5th International Summer School and Symposium on, June 2008, pp. 28–32. [7] G.-Z. Yang, Body Sensor Networks. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006. [8] J. Muehlsteff, X. Aubert, and M. Schuett, “Cuffless estimation of systolic blood pressure for short effort bicycle tests: The prominent role of the pre-ejection period,” in Engineering in Medicine and Biology Society, 2006. EMBS ’06. 28th Annual International Conference of the IEEE, 30 2006-sept. 3 2006, pp. 5088 –5092. [9] M. Rooijakkers, C. Rabotti, M. Bennebroek, J. van Meerbergen, and M. Mischi, “Low-complexity r-peak detection in ecg signals: A preliminary step towards ambulatory fetal monitoring,” in Engineering in Medicine and Biology Society,EMBC, 2011 Annual International Conference of the IEEE, 30 2011sept. 3 2011, pp. 1761–1764.

ACKNOWLEDGMENT

[10] H.-P. Kew and D.-U. Jeong, “Variable threshold method for ecg r-peak detection.” J. Medical Systems, vol. 35, no. 5, pp. 1085–1094, 2011.

This research work was support by ARTICA, COLCIENCIAS, ICT Ministry of Colombia, and University of Antioquia.

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