Remote Monitoring of Heart and Respiration Rate Using a Wireless Microwave Sensor

Transcription

1 Remote Monitoring of Heart and Respiration Rate Using a Wireless Microwave Sensor 1 Ali SAAD*, Amr Radwan*, Sawsan SADEK**, Dany, OBEID***, ZAHARIA, Ghaïs EL ZEIN***, Gheorghe * 1 Associate professor at King Saud University, College of applied medical sciences, Department of biomedical technology. alisaad@ksu.edu.sa ** Lebanese university, IUT Saida, Department of telecommunication, Saida, Lebanon *** IETR-INSA of Rennes, FRANCE. Abstract : Our research project consists of developing a wireless microwave sensor for remote sensing of heart and respiration activity, and then process the received signal in order to separate the respiratory from the heart response. The purpose of this paper is to develop a signal processing algorithm in order to separate the tow signal response from heart and respiration and then calculate the heart and respiration rates in order to transmit them via wireless network to the physician or to the hospital. A real set of data were acquired using a 2.4 GHz radar Doppler sensor. Two data set were acquired one is with respiration and another without respiration at different level of signal power starting from 3dB down to -27dB. The heart signal is very small comparing to the respiratory one in real time signal the separation seems to be very difficult using classical low pass and high pass filter analysis especially for the heart beat in a signal with respiration. Using wavelet toolboxes for signal analysis and processing, the (Daubechies) wavelets were applied to both the heartbeat and the respiration signals. This helps in determining the level of the wavelet packet at which the data can be extracted out of the system without losing information such as the heartbeat rate. After trial and error, it is shown that at level 7 of wavelet decomposition, the heartbeat rate can be extracted from the heartbeat signal. The determined type of the wavelet (Daubechies) is applied to the respiration signal. At the desired level (7), the output signal shows a frequency between 1.16 Hz and 1.3 Hz. This corresponds to beats/min. The calculation of the heartbeat rate and respiration rate are based on counting the number of peaks found in the output signals. Remote monitoring of respiration and heart activity can be implemented using wireless communications technology and Doppler radar techniques. This technology can potentially enable lowcost, non-invasive long term monitoring of chronic and recovering patients. A wireless sensor promises to improve the quality of life for the chronically ill. Also, vital signs of patients could be registered at home and data could be sent to a physician I. Introduction The need of remote monitoring of heart and respiratory rates is a necessity in many cases including: infants, burn victims, elderly and chronic patients, emergency cases (ventricular fibrillation), even for security purpose it helps detecting suspects having high heart rate. Microwave Doppler radar has been used to sense physiological movement since the early Advancing in technology of micro fabrication made it possible to integrate a Doppler radar in a single chip to be compact, lightweight and cheap in the market. In 2004,

2 direct-conversion Doppler radars, operating at 1.6 and 2.4 GHz, have been integrated in 0.25 mm CMOS and BiCMOS technologies. The 2.4 GHz radar, placed at 50 cm range, uses a quadrature (I/Q) receiver. The use of a quadrature receiver improved the lowest accuracy form 40% to 80%. There has been additional recent work in using existing wireless communications infrastructure. A modified Wireless Local Area Network PCMCIA card and a module combining the transmitted and reflected signals were used to detect heart and respiration activity in [1]. A low-power doublesideband transmission in the Ka-Band was used in 2006 [2-3]. Radar motion sensing systems usually transmit a continuous wave (CW) signal, which is reflected off the target and then demodulated in the receiver. A target with time-varying position reflects the signal and modulates its phase proportional to the target s time varying position. Therefore, CW radar with the chest as the target will receive a signal similar to the transmitted signal, with its phase modulated by the time-varying chest position. As shown in relation (1), the phase (t) of the reflected signal will be directly proportional to the chest position x(t) that contains information about movement caused by heartbeat and respiration. t x(t) Demodulating the phase will then give a signal directly proportional to the chest position, which contains information about movement due to both heartbeat and respiration. In equation (1) is the wavelength of the transmitted waves. The average range of the peak-to-peak chest motion due to respiration is between 4 mm and 12 mm, whereas the chest displacement due to heartbeat alone is about 0.5 mm [7]. Heart and respiration rates can be extracted from the received signal of figure.0. Figure 0. transmission and reception radar Doppler system a. The transmitter creates waveform and amplifies it to the required transmission power. b. A directional antenna both concentrates the beam in the direction of the target and enables determination of the direction of the target; electronically tunable antenna arrays are often used for this purpose.

3 c. The duplexer isolates the receiver from the transmitter while permitting them to share a common antenna. d. The receiver converts the signal from the transmission frequency to either an intermediate frequency or baseband, separates the signal from both noise and interferers, and amplifies the signal enough for digitization and/or display. e. Signal processing is used to reject clutter and out-of-band noise, while passing the desired signal, and to derive information from the signal. The measurement of this small displacement using non-contact Doppler sensors for the remote monitoring of such signals was developed by our team in laboratory at the INSA of Rennes France [8]. II. Methods II.1 Data Acquisition: The objective of the 2.4 GHz system is to perform a study using direct measurements with only a Vector Network Analyzer (VNA) and two antennas. The HP 7853D Network Analyzer provides a 2.4 GHz and 0 dbm continuous wave driven to a 10 db gain Model 3115 Double-Ridged Waveguide Horn Antenna directed to a human chest positioned at 1 m. The reflected signal, received by another similar antenna, is driven to the VNA where the phase is calculated measurement points are taken in a window of 10 seconds. Hence, a 160 Hz sampling frequency is obtained which is sufficient to produce accurate measurements. It is noteworthy to mention that the range of the heart rate is between 60 and 120 beats per minute; in other words, heartbeat frequency ranges from 1 to 2 Hz. Values are collected and transmitted via a GPIB cable to a PC where the Matlab software is used for processing the recorded data. Figure 1 shows the result for the heart-beat detection where the phase shift varies from 2 to 4. The maximum-to-maximum delay shown in this figure represents the R-R interval, i.e. each maximum corresponds to a beat. For this experiment, the average heart rate is about 72 beats per minute.

4 Figure 1 Heart activity detected at 2.4 GHz and 1 m from the patient II.2 Separation of heart beat and breathing II.2.1 Introduction: In the Doppler radar cardiopulmonary motion monitoring system, the heartbeat and respiration signals are superimposed on each other (figure 2). Because the chest moves a much greater distance due to breathing than it does due to the heart beating and a greater area of the chest moves for respiration, the amplitude of the respiration signal is typically about 100 times greater than that of the signal due to the heartbeat. Therefore, the respiration rate can be detected without filtering, but the heart signal must be isolated from the respiration signal to detect heart rate. Figure 2. Heart beat and respiration signal at 2.4 GHz. with heart rates varying from 43 to 94 beats per minute (1 to 1.67 Hz) and respiration rates varying from 5 to 21 breaths per minute (0.08 to 0.35 Hz).This requires a

5 highpass filter with a transition between 0.70 Hz and 0.35 Hz to isolate the heart signal. II.2.2 Wavelets Separation: The choice of wavelet filter bases depends on the signal. Signals coming from different sources have different characteristics. For audio, speech, image and video signals the best choices of wavelet bases are known. The best choice for the signal processed here is not clear. The problem is to represent typical signals with a small number of convenient computable functions. An investigation to choose the best wavelet bases for our signal was performed here. During this study, simulated and real signals were used. The majority of the wavelets basis existing in Matlab-5 software [6-10] was tested. The criterion used to determine the best wavelet base was the one which optimizes the signal to noise ratio in a broad spectrum of spatial frequencies. The Daubechies and bi-orthogonal wavelets basis [11,16] in Matlab-5, with filters width of 10 elements and using 10 levels of decomposition which is the maximum level set in Matlab, yielded the best average signal to noise ratio in the range of the spatial frequency relevant to data analysis and also provide the best separation for both signals in synthetic and real data. The principle of the wavelet decomposition is to transform the signal into several components: one low-resolution component called "approximation" [10], and the other components called "details" (Fig.3). The approximation component is obtained after applying a low-pass Daubechies wavelet filter followed by a sub-sampling by a factor of 2. The details are obtained with the application of a high-pass Daubechies wavelet filter followed by a sub-sampling by a factor of 2. The noise is mainly present in the detail components. A higher level of decomposition is obtained by repeating the same filtering operations on the approximation. This separation between low frequency data (approximation) and high frequency (details) were used here to separate the respiratory signal (low frequency) from the heart beat one (high frequency). Figure 3. Wavelet decomposition of a signal at level 3, the top represents the original signal the blue (left) components represent the approximations and the greens (right) represent the details.

6 III. Results: III.1 Signal without respiration Two type of data were processed here the first is without respiration (Holding breath for 50 seconds) the second is with respiration. Figure 4. signal of the heart without respiration for 10 second.

7 Figure 5: Wavelet decomposition in the left side the a i represent the approximation and in the right side the d i represent the details and/or noise, where i represents the level of decomposition. We can see from the figure that a 7 is the approximation were we have a sinusoids with a constant signal added to it. We can also see from d 8 ( Figure 6), that the DC component attached to the signal in a 7 is eliminated and a sinusoid signal is obtained from where we can count the number of picks in 10 seconds then we calculated the number of picks in one minute, which is equal to 78 correspond to the heart rate of the human body. Figure 6: detail component at level 8 where the signal is very similar to sinusoid. III.1 Separation of heart signal from respiration one. The signal of figure 2 represents one type of real radar respiration and heart response, in this signal respiratory movement of the chest is the dominant one the heart response is very small and can't be seen. A set of different signal power were tested starting

8 from 3dB down to -27dB. All of them provide the separation between respiration and heart at the level 8 of wavelet decomposition using Daubechies of size 10, where d 8 represents the heart signal and a 7 represent the respiration signal. Figure 7: Wavelet decomposition of a real signal in the left side the a i represent the approximation and in the right side the d i represent the details, where i represents the level of decomposition. Figure 8: detail component at level 8 where the signal is very similar to sinusoid, which represent the heart signal.

9 Figure 9: represent a zoom of the a 7 and a 8 of the same area from left to right respectively. It is clear from figure 9, in the left image a 7 that, the signal contains some variation, which is due to the heart response. In a 8 we can see the signal is very smooth and the heart component where separated from it, and transferred to d 8, as the wavelet decomposition theory suggest. The heart rates calculated from d 8 are obtained between 60 and 78 beat per minutes, the corresponding respiration rates are between 12 and 15 per minute. IV. Conclusion: The proposed work treats a separation of 2 noisy signals the first one is the heart response, which is 100 times smaller than the second one, which is the respiration one. Viewing the real signal containing both respiration and heart, we can't see the heart signal on top of the respiration one, it looks like noise. However, separation using wavelet analysis technique shows that the separation is still possible and the heart rate and respiration rate calculated from the processed signals fit in the normal range of frequencies corresponding to each organ. Further work need to be done in order to make the calculation automatic.

10 V. References 1. O. Boric-Lubecke, G. Awater, and V. M. Lubecke, Wireless LAN PC Card Sensing of Vital Signs, IEEE Topical Conference on Wireless Communication Technology, 2003, pp C. Li, Y. Xiao, and J. Lin, Experiment and Spectral Analysis of a Low-Power K a- Band Heartbeat Detector Measuring From Four Sides of a Human Body, IEEE Transactions on Microwave Theory and Techniques, Vol. 54, No. 12, December 2006, pp Y. Xiao, J. Lin, O. Boric-Lubecke, and V. M. Lubecke, A K-a Band Low Power Doppler Radar System for Remote Detection of Cardiopulmonary Motion, Proceedings of the 2005 IEEE, Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, September 1-4, 2005, O. Boric-Lubecke, A. D. Droitcour, V. M. Lubecke, J. Lin, and G. T. A. Kovacs, Wireless IC Doppler for Sensing of Heart and Respiration Activity, IEEE TELSIKS 2003, Serbia and Montenegro, Nis, October 1-3, 2003, pp Dany OBEID1, Sawsan SADEK2, Gheorghe ZAHARIA1, Ghaïs EL ZEIN1 "Feasibility Study for Non-Contact Heartbeat Detection at 2.4 GHz and 60 GHz, URSI. 6. Daubechies I: Ten lectures on wavelets. SIAM Strang G, Nguyen T: Wavelets and Filter Banks. Wellesley Cambridge Press; Meyer Y: Wavelets and operators. Cambridge UK, Cambridge University press; Mallat S: Multi-frequency Channel Decompositions of images and wavelet Models. IEEE on ASSP 1989, 37: Beylkin G, Coifman R, Rokhlin V: Fast Wavelet transforms and numerical algorithms. Comm Pure Appl Math 1991, 44: Cohen A, Daubechies I, Feauveau JC: Biorthogonal Bases of Compactly supported Wavelets. Comm Pure App Math 1992, 45: Phoong SM, Kim CW, vaidyanathan PP, Ansari R: A new class of two-channel biorthogonal filter banks and wavelets bases. IEEE Trans SP 1997, 43: Turcajova J, Kautsky R: Discrete Biorthogonal Wavelet transform as block circulant matrices. Lin Algeb Appl 1995, 223: