A New Wavelet Based Algorithm for Estimating Respiratory Motion Rate Using UWB Radar Mehran Baboli1, Seyed Ali Ghorashi1, Namdar Saniei1, and Alireza Ahmadian2 Faculty of Electrical and Computer Engineering, Shahid Beheshti University, Tehran, Iran 2 Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran, Iran e-mail:
[email protected],
[email protected],
[email protected],
[email protected] 1
Abstract-UWB signals have become attractive for their particular advantage of having narrow pulse width which makes them suitable for remote sensing of vital signals. In this paper a novel approach to estimate periodic motion rates, using Ultra Wide Band (UWB) signals is proposed. The proposed algorithm which is based on wavelet transform is used as a noncontact tool for measurement of respiration motion rate. Compared with traditional contact measurement devices, experimental results utilizing a 3.2 GHz bandwidth transceiver, demonstrate 99% similar results. The standard deviation of the proposed algorithm for 30 independent experiments has obtained 19% for respiration motion. Keywords-; UWB; Respiration Rate; Motion Detection; wavelet
I. INTRODUCTION Respiration rate is a basic vital sign of a patient and monitoring it can give important information for health care purposes. Conventional respiration measurement methods utilize contact devices and may have problems such as causing stress for patient, involving a human operator and the possibility of inaccuracy, due to human errors. Wireless sensing of patient’s vital signs makes it possible to continuously and comfortably monitor the patient’s health situation. One of the proposed wireless monitoring techniques is using UWB signals. Considering FCC definition for UWB [1], we can use the UWB waves in different contexts, such as data transmission, military and radar applications [2]. In Radar applications, detection of objects in enclosed environments, imaging and motion modeling are some new areas of research. There are different works to find respiration rate in literature [3], [4] and [5]. In [3] an analytical framework for the development of signal processing algorithms to estimate respiration and heart rates is presented. In [4] they used averaging and correlation processing for motion rate estimation. In [5] the structure of UWB transceiver for measurement of respiration rate is presented. In this work, a new algorithm for estimating respiration rate using wavelet filter bank is presented. The proposed algorithm is working with different UWB transmit pulse shapes and different mother wavelet. II. METHOD Fig. 1 shows the measurement setup. UWB pulses are transmitted to chest cavity with an angle of degrees. d1 is the distance between Tx and Rx antennas, d2 is the distance
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between the Tx antenna and the target, and d3 is the distance between the Rx antenna and the target. For the experimental results, a transceiver of UWB waves with 3.2 GHz bandwidth is used. The time interval between transmitting pulses is called I.
Fig 1 shows a sample of receiving signal. Transmitted signals are saved at receiver in a matrix, R, for a T second time interval. The proposed algorithm in this paper to find the respiration rate, is based on the computation and then comparison of the filter bank sub-bands’ energy. Particularly, wavelet filter banks are used because of their capability to find the energy in the desired frequency interval. The wavelet transform which is used in this paper for respiration rate calculations, is [7]: (1) where is the mother wavelet, n is the translation, m is the dilation, a0 is the dilation step parameter, and b0 is the location parameter. In this work the Daubechies 8 wavelet as shown in Fig.3 is selected as the mother wavelet. The energy of the wavelet is [6]: (2)
The proposed algorithm uses perfect wavelet transform. In the j-th level, frequency intervals [0 2j] are divided in to 2j equal intervals and using (2), we can say, the energy of each level is equal to the sum of the energy of components in each interval. The steps of proposed algorithm are summarized as follows: Target
d2 Tx
d3
d1
Rx
proposed algorithm, several respiratory rates are estimated in two measurement setups with parameters which are shown in Table1. Fig. 4 shows the results of the algorithm in frequency and time domain. In this experiment, target is located 1m away from the antennas. The direct measurement of respiration rate is 0.45Hz, which means 27 breaths per min. As shown in Fig. 4, the estimated rate using proposed algorithm is 0.4525Hz, or 27.15 breaths/min.
Fig 1. Measurement setup 200 150
Amplitude
100 50 0 -50 -100 -150 -200
0
2
4 6 Time(ns)
8
10
Amplitude
Fig 2. Received waveform
Time (ns)
Fig 5 shows the results of another experiment in which the object is located 2m away from antennas. For this experiment, contact measurement device shows a rate of 0.37 Hz, while the estimated rate by the proposed method is 0.38 Hz.
Fig 3. Debouche 8 wavelet Table1. Parameters of measurement setups.
1. The length of the frequency intervals are selected based on the frequency range of respiration, and then the number of transform levels is specified. 2. Static samples are deleted from data. This is done by giving an average of each column of matrix R and subtracting it from all columns. The result of this step is recorded in a matrix, Q. 3. All of the columns of matrix Q are the inputs of wavelet packet transform. Then, the energy of all of the frequency intervals in the last level, are calculated and their maximum is saved in an array, S. 4. The column, in which S has its maximum value, is the location of motion with strong amplitude compared to other places. Assuming that the respiration movement is the only motion in the experiment environment, we can specify this place as the goal column. We call this column c. By analyzing the frequency spectrum column c in matrix Q, the respiration rate can be calculated. In busy environment which contains some motion which has greater amplitude than respiratory movement, despite of finding maximum energy in all column, the energy of all column in frequency interval of respiration is compared with each other and the column that the energy is maximized is selected. III. EXPERIMENTAL RESULTS In this section, the results of applying the proposed algorithm to experimental data are presented. Using the
Experiment #1
d1= 1m, d2=d3=1m
I= 100 ms = 60º
Experiment #2
d1= 1m, d2=d3=2.5m
I= 100 ms = 60º
The accuracy of the algorithm can be calculated as: per min. Where X is the real value of the respiration rate, and ܺതis the expected value of the measurements for 30 repetitions. Also, the standard deviation (SD) of the measured values is 0.19 per minute which shows the precision of the algorithm.
Fig 6 Result of algorithm for 30 independent experiments IV. CONCLUSIONS
Fig 4. The result of algorithm (a) Time domain (b) Frequency domain
In this paper, the respiration rate is estimated using a new algorithm based on wavelet filter bank and maximum energy detection. The proposed algorithm can detect the motion in specific frequency range and has very good accuracy and precision. ACKNOWLEDGMENT The authors would like to thank the Research Center for Science and Technology in Medicine, Tehran University of Medical Science, for their permission to use their transceiver.
[1]
[2] [3]
[4]
[5] Fig 5. The estimated respiration rate (a) Time domain (b) Frequency domain
In order to show the stability of the results, we repeat the proposed algorithm for several times, keeping all the experimental setup parameters and patient situation fixed. Estimated frequency for 30 repetitions is illustrated in Fig 6.
[6]
[7]
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