CN115844355A - UWB (ultra wide band) biological radar echo signal heart rate extraction method - Google Patents
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Abstract
The invention provides a method for extracting a heart rate of an echo signal of a UWB (ultra wide band) biological radar. The method comprises the steps of firstly extracting and reconstructing a radar data frame aiming at a detection target point to obtain a time domain data sequence of the displacement of the thoracic cavity of a monitored object. The reconstructed time domain data sequence is then subjected to an autocorrelation operation, which can retain periodic signals in the echo signal, such as respiration and heart rate signals, and respective periods do not change, while effectively suppressing noise and clutter interference in the measurement. And finally, performing discrete wavelet change time-frequency analysis, and clearly and accurately acquiring heart rate components and frequency in a time-frequency graph. The invention provides a new signal processing method for the non-contact measurement of the heart rate of the human body, which can enhance the identification of heart rate signals and improve the accuracy of heart rate frequency measurement.
Description
Technical Field
The invention belongs to the technical field of non-contact measurement of human vital signs, and particularly relates to a method for extracting the heart rate of an echo signal of a UWB (ultra wide band) biological radar.
Background
The health of a person is reflected by a variety of vital sign parameters, including respiration, heart rate, blood pressure, body temperature, and the like. When the physical parameters are within a normal range, the human body is in a healthy state. Among these parameters, heart rate is a very important one of physiological signals of human body, and it can not only judge the state of human body, but also prevent some sudden diseases. Therefore, the method has great medical value and research significance for accurately measuring the human heart rate.
In the traditional method, a contact type detection method is generally adopted to measure the heart rate of a human body, and relevant devices used in the contact type measurement method are an electrocardiogram instrument, a contact type bandage, a wrist strap type pulse sphygmomanometer and the like. However, this contact method can cause considerable confusion and even secondary injury to specific persons, such as burn patients, infants, landfills, etc., during the measurement process. The non-contact vital sign detection method can make up or improve the disadvantages and shortcomings of the contact measurement. The non-contact detection method is characterized in that a detection device detects vital sign parameters of a detected object on the premise of not contacting the detected object, so that the problems of detection of serious burn patients and infants can be solved, and the method can be used in the fields of earthquake relief and the like. Along with the development requirements of medical science and civil science and technology over the years, the non-contact detection technology is more and more emphasized, and the research and development of related technologies and instruments become a hot problem.
In the existing non-contact detection technology, ultra Wide Band (UWB) is a new technology, which can detect and extract the micro-motion signal on the surface of the object to be detected. The heart and lung movement of the tested object makes the thoracic cavity move up and down, and the biological radar detects the movement to obtain the heart and lung movement information. Therefore, the ultra-wideband biological radar technology has very strong potential and application prospect in the aspects of medical treatment, health monitoring, disaster rescue and the like.
However, the ultra-wideband biological radar echo signal is a clutter signal mixed by heart rate, respiration and various interference noises, and how to separate heart rate components from the echo signal and extract corresponding parameters is a key technology. The traditional method comprises a band-pass filtering method, a short-time Fourier transform method, an adaptive filtering method and the like, and the band-pass filtering method cannot well separate signals because the respiratory frequency band and the heartbeat frequency band are relatively close and a frequency domain filter has the characteristics of transition band, pass-band ripple and the like. The short-time fourier transform performs windowing on the signal, and performs section-by-section fourier transform by shifting a window function on a time axis. However, it cannot change the window width and can not perform real time frequency analysis and multi-resolution analysis of signals. The adaptive filtering method needs to obtain a pure breathing signal in the echo as a reference signal in advance to adjust the adaptive filter coefficient, which is very difficult to implement.
Disclosure of Invention
The invention aims to solve the problems in the prior art, carry out non-contact extraction and measurement of human heart rate signals, and provides a method for extracting the heart rate of echo signals of a UWB (ultra wide band) biological radar.
The invention is realized by the following technical scheme, and provides a UWB (ultra wide band) biological radar echo signal heart rate extraction method, which specifically comprises the following steps:
the step 2 specifically comprises the following steps: performing autocorrelation operation on the reconstruction data sequence obtained in the step 1; the autocorrelation function sequence is R ww (m) having the formula of formula (1):
wherein, w (n) is a discrete sequence, M is an autocorrelation operation data point interval, for a sequence with a finite length M, the data point interval range is-M < M < M, and the length of the autocorrelation sequence is 2M-1;
Further, in step 1, the data frame of the echo signal records the reflection echo of each position point in the detection range, and the reflection echo is continuously output at a certain frame frequency; in each frame of data, extracting data points corresponding to the target position of the detected human body, and reconstructing the extracted points of different data frames according to a time sequence to form a new time domain sequence w (n), wherein the length of the new time domain sequence w (n) is M and represents the change of the displacement of the thoracic cavity of the human body along with the time.
Further, the autocorrelation function calculation process specifically includes:
first, assigning w (n) to two sequences w 1 (n) and w 2 (n)，0≤n≤M-1；
w 1 (n)＝w(n)
w 2 (n)＝w(n)
Second step, w 2 (n) left-shifted by M-1, i.e. w 2 (n + M-1), then w 1 (n) and w 2 (n + M-1) only has one coincident point 0, the coincident points are multiplied correspondingly and assigned to R ww (-M+1)；
Thirdly, mixing w 2 (n + M-1) right-shifted by k bits, i.e. w 2 (n + M-1-k) k =1 \ 8230m-1, so that w 2 (n + M-1-k) gradually moves into w 1 In the region of (n), for each k, w is 1 (n) and w 2 The coincident points of (n + M-1-k) k =1 \8230thatM-1 are multiplied and summed correspondingly and assigned to R ww (-M+1+k)；
The fourth step is to get w 2 (n + M-1-k) continued right shift, k = M \8230; 2M-2, so that w 2 (n + M-1-k) gradually moving out w 1 (n) until k =2M-2, when w 1 (n) and w 2 (n + M-1-k) has only one coincident point M-1, and for each k, w is added 1 (n) and w 2 The coincident points of (n + M-1-k) k = M \8230and2M-2 are multiplied and summed correspondingly and assigned to R ww (-M+1+k)；
Through the four steps, the autocorrelation function R of the discrete sequence w (n) is obtained ww (m)。
Further, a Morlet wavelet is selected as a mother wavelet, and the expression of the Morlet wavelet is as follows:
wherein ω is 0 Is the center frequency.
Furthermore, the mother wavelet is discretized according to the data inter-frame period delta t of the biological radar echo, and meanwhile, the scale parameter and the translation parameter are also discretized to form a discrete wavelet function:
get a 0 ＝2,b 0 =1, then the discrete wavelet is:
ψ j,k (mΔt)＝2 -j/2 ψ(2 -j mΔt-k)。
further, the autocorrelation function sequence R ww (m) performing discrete wavelet transform to obtain wavelet coefficients as:
further, the wavelet coefficient mesoscale sequence is converted into an actual frequency sequence f, the wavelet coefficient is converted into a time-frequency graph in combination with the time sequence t, and the heart rate can be determined according to the prominent frequency components in the time-frequency graph.
Furthermore, the UWB radar continuously generates pulse electromagnetic waves which are amplified by the power amplifier and transmitted by the transmitting antenna, the electromagnetic waves are reflected by the body of the monitored person, the original signals are modulated due to the fluctuating motion of the chest cavity of the monitored person, the Doppler effect is formed, and echo signals are generated.
The invention provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the UWB biological radar echo signal heart rate extraction method when executing the computer program.
The invention provides a computer readable storage medium for storing computer instructions which, when executed by a processor, implement the steps of the UWB bio-radar echo signal heart rate extraction method.
The invention has the following beneficial effects:
the invention provides a processing method based on autocorrelation operation combined with wavelet time-frequency analysis, which aims at the problem of extracting the heart rate of an echo signal of a UWB (ultra wide band) biological radar in the field of non-contact measurement of the heart rate of a human body. Firstly, extracting and reconstructing a radar data frame aiming at a detection target point to obtain a time domain data sequence of the displacement of the thoracic cavity of a monitored object. The reconstructed time domain data sequence is then subjected to an autocorrelation operation, which can retain periodic signals in the echo signal, such as respiration and heart rate signals, and respective periods do not change, while effectively suppressing noise and clutter interference in the measurement. And finally, performing discrete wavelet change time-frequency analysis, and clearly and accurately acquiring heart rate components and frequency in a time-frequency graph. The invention provides a new signal processing method for the non-contact measurement of the heart rate of the human body, which can enhance the identification of heart rate signals and improve the accuracy of the heart rate frequency measurement.
Drawings
FIG. 1 is a diagram of a system used in a method for extracting a heart rate from an echo signal of a UWB biological radar;
FIG. 2 is a diagram of processing procedures of extraction and reconstruction of inter-frame data of echo signals;
FIG. 3 is w 2 (n) left shift by M-1 bit results;
FIG. 4 is w 2 (n + M-1) right shift by k bits results;
FIG. 5 is w 2 (n + M-1-k) continued right shift, k = M \8230; 2M-2 schematic results;
FIG. 6 is a diagram showing the result of the respiratory and heartbeat extraction of the object; wherein (a) is a test object 1, and (b) is a test object 2.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The basic principle of UWB radar is that it constantly generates pulsed electromagnetic waves, amplified by a power amplifier, and transmitted through a transmitting antenna. The electromagnetic wave meets the reflection of the monitored human body, the original signal is modulated due to the fluctuating motion of the thoracic cavity of the human body, the Doppler effect is formed, and an echo signal is generated. The receiving antenna receives the echo signal and obtains information about the distance from the radar to the monitored person and the displacement of the chest cavity of the human body after the echo signal is preprocessed by the low-noise amplification, the analog-to-digital converter and the control unit. The fluctuation motion of the human thorax is related to respiration and heartbeat, wherein the respiration plays a main role, and the fluctuation amplitude of the thorax is 0-3 cm during respiration. The heartbeat also causes small amplitude fluctuation movement of the surface of the thoracic cavity, the amplitude is 1.5-3.5 mm. Both signals are periodically changed and are added together.
The echo signal of UWB radar is output in the form of data frame, and the reflected echo of every position point in the detection range is recorded, and it can continuously output data frame with a certain frame frequency. The heart rate extraction for echo signals is mainly divided into the following three steps: extracting and reconstructing data between frames; performing autocorrelation operation on the reconstructed signal; and finally, performing wavelet transform analysis. The system structure diagram is shown in fig. 1, a computer controls a UWB radar to collect signals, and the whole signal processing algorithm is completed on the computer.
With reference to fig. 1 to 6, the present invention provides a method for extracting a heart rate of an echo signal of a UWB biological radar, where the method specifically includes:
in step 1, the data frame of the echo signal records the reflection echo of each position point in the detection range, and the reflection echo is continuously output at a certain frame frequency; in each frame of data, extracting data points corresponding to the target position of the detected human body, and reconstructing the extracted points of different data frames according to a time sequence to form a new time domain sequence w (n), wherein the length of the new time domain sequence w (n) is M and represents the change of the displacement of the thoracic cavity of the human body along with the time. The sampling frequency of the new data sequence is the interframe frequency of the UWB radar, and the new data sequence contains displacement information such as human respiration and heartbeat. The whole process is shown in fig. 2.
said step (c) is2 specifically comprises the following steps: performing autocorrelation operation on the reconstruction data sequence obtained in the step 1; the autocorrelation operation can effectively reserve periodic signals in the sequence and inhibit noise interference, so that the periodic signals of respiration and heart rate in the sequence are reserved, and the period is kept unchanged. The autocorrelation function sequence is R ww (m) having the formula of formula (1):
wherein w (n) is a discrete sequence, M is an autocorrelation operation data point interval, for a sequence with a finite length M, the data point interval range is-M < M < M, and the autocorrelation sequence length is 2M-1;
the autocorrelation function calculation process specifically comprises the following steps:
first, assigning w (n) to two sequences w 1 (n) and w 2 (n)，0≤n≤M-1；
w 1 (n)＝w(n)
w 2 (n)＝w(n)
Second step, w 2 (n) left-shifted by M-1, i.e. w 2 (n + M-1), as shown in FIG. 3, then w 1 (n) and w 2 (n + M-1) only has one coincident point 0, the coincident points are multiplied correspondingly and assigned to R ww (-M+1)；
Thirdly, mixing w 2 (n + M-1) right-shifted by k bits, i.e. w 2 (n + M-1-k) k =1 \ 8230M-1, so that w 2 (n + M-1-k) gradually moves into w 1 (n) in the region shown in FIG. 4. For each k, w 1 (n) and w 2 The coincident points of (n + M-1-k) k =1 \8230, M-1 are multiplied and summed correspondingly, and assigned to R ww (-M+ 1+k)；
The fourth step is to get w 2 (n + M-1-k) continued right shift, k = M \8230; 2M-2, so that w 2 (n + M-1-k) gradually moving out w 1 (n) until k =2M-2, when w 1 (n) and w 2 (n + M-1-k) has only one coincident point M-1, as shown in FIG. 5. For each k, w 1 (n) and w 2 The coincident points of (n + M-1-k) k = M \8230and2M-2 are multiplied and summed correspondingly and assigned to R ww (-M+ 1+k)；
Through the four steps, the autocorrelation function R of the discrete sequence w (n) is obtained ww (m)。
Selecting Morlet wavelet as mother wavelet, whose expression is:
wherein omega 0 Is the center frequency.
Discretizing the mother wavelet according to the data interframe period delta t of the biological radar echo, and discretizing the scale parameter and the translation parameter to form a discrete wavelet function:
get a 0 ＝2,b 0 =1, then the discrete wavelet is:
ψ j,k (mΔt)＝2 -j/2 ψ(2 -j mΔt-k)。
sequence of autocorrelation functions R ww (m) performing discrete wavelet transform to obtain wavelet coefficients as:
and (3) converting the wavelet coefficient mesoscale sequence into an actual frequency sequence f, combining the time sequence t, finally converting the wavelet coefficient into a time-frequency graph, and determining the heart rate according to the prominent frequency components in the time-frequency graph.
Fig. 6 is a schematic diagram of the result of the breathing and heartbeat extraction of the measured object. As shown in fig. 6 (a), the breathing and heartbeat frequencies of the subject 1 are 0.2324Hz and 1.096Hz, respectively, and are calculated as follows: breaths were 13.944/min and heartbeats were 65.76/min. Referring to fig. 6 (b), it can be seen that the breathing and heartbeat frequencies of the subject 2 are 0.2324Hz and 1.295Hz, respectively, and are calculated as: breaths were 13.944 beats/minute and heartbeats were about 77.7 beats/minute.
The invention provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the UWB biological radar echo signal heart rate extraction method when executing the computer program.
The invention provides a computer readable storage medium for storing computer instructions which, when executed by a processor, implement the steps of the UWB bio-radar echo signal heart rate extraction method.
The memory in the embodiments of the present application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may be a Read Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), synchronous Dynamic Random Access Memory (SDRAM), double data rate SDRAM, enhanced SDRAM, SLDRAM, synchronous Link DRAM (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memories of the methods described herein are intended to comprise, without being limited to, these and any other suitable types of memories.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a Digital Video Disk (DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)), among others.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in a processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
It should be noted that the processor in the embodiments of the present application may be an integrated circuit chip having signal processing capability. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The processor described above may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The method for extracting the heart rate of the echo signal of the UWB biological radar is described in detail, a specific example is applied in the method for explaining the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A heart rate extraction method for echo signals of a UWB (ultra Wide band) biological radar is characterized by specifically comprising the following steps:
step 1, extracting and reconstructing data between frames of echo signals;
step 2, performing autocorrelation operation processing on the reconstructed signal;
the step 2 specifically comprises the following steps: performing autocorrelation operation on the reconstruction data sequence obtained in the step 1; the autocorrelation function sequence is R ww (m) having the formula of formula (1):
wherein w (n) is a discrete sequence, M is an autocorrelation operation data point interval, for a sequence with a finite length M, the data point interval range is-M < M < M, and the autocorrelation sequence length is 2M-1;
step 3, the autocorrelation function sequence R ww And (m) performing wavelet transformation to obtain a time-frequency diagram so as to determine the heart rate frequency contained in the radar echo signal.
2. The method of claim 1, wherein in step 1, the data frame of the echo signal records the reflection echo of each position point in the detection range, and the reflection echo is continuously output at a certain frame frequency; in each frame of data, extracting data points corresponding to the target position of the detected human body, and reconstructing the extracted points of different data frames according to a time sequence to form a new time domain sequence w (n), wherein the length of the new time domain sequence w (n) is M and represents the change of the displacement of the thoracic cavity of the human body along with the time.
3. The method according to claim 1, wherein the autocorrelation function calculation process is specifically:
first, assigning w (n) to two sequences w 1 (n) and w 2 (n)，0≤n≤M-1；
w 1 (n)＝w(n)
w 2 (n)＝w(n)
Second step, w 2 (n) left-shifted by M-1, i.e. w 2 (n + M-1), then w 1 (n) and w 2 (n + M-1) only has one coincident point 0, the coincident points are multiplied correspondingly and assigned to R ww (-M+1)；
Thirdly, mixing w 2 (n + M-1) right-shifted by k bits, i.e. w 2 (n+M-1-k)k =1 \ 8230M-1, so that w 2 (n + M-1-k) gradually moves into w 1 In the region of (n), w is added for each k 1 (n) and w 2 The coincident points of (n + M-1-k) k =1 \8230thatM-1 are multiplied and summed correspondingly and assigned to R ww (-M+1+k)；
The fourth step is to mix w 2 (n + M-1-k) continued right shift, k = M \8230; 2M-2, so that w 2 (n + M-1-k) gradually moving out w 1 (n) until k =2M-2, when w 1 (n) and w 2 (n + M-1-k) has only one coincident point M-1, and for each k, w is added 1 (n) and w 2 The coincident points of (n + M-1-k) k = M \8230and2M-2 are multiplied and summed correspondingly and assigned to R ww (-M+1+k)；
Through the four steps, the autocorrelation function R of the discrete sequence w (n) is obtained ww (m)。
4. A method as claimed in claim 3, characterized by selecting a Morlet wavelet as the mother wavelet, whose expression is:
wherein omega 0 Is the center frequency.
5. The method of claim 4, wherein the mother wavelet is discretized according to the data inter-frame period Δ t of the biological radar echo, and the scale parameter and the translation parameter are also discretized to form a discrete wavelet function:
get a 0 ＝2,b 0 =1, then the discrete wavelet is:
ψ j,k (mΔt)＝2 -j/2 ψ(2 -j mΔt-k)。
6. the method of claim 5, whereinCharacterised by the sequence of autocorrelation functions R ww (m) performing discrete wavelet transform to obtain wavelet coefficients as:
7. the method of claim 6, wherein the wavelet coefficient mesoscale sequence is converted into an actual frequency sequence f, the time sequence t is combined, and finally the wavelet coefficients are converted into a time-frequency graph, and the heart rate can be determined according to the prominent frequency components in the time-frequency graph.
8. The method of claim 1, wherein the UWB radar continuously generates pulsed electromagnetic waves, which are amplified by a power amplifier, transmitted through a transmitting antenna, and which encounter reflections from the monitored human body, modulate the original signal due to the fluctuating motion of the human chest, create doppler effect, and generate echo signals.
9. An electronic device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method according to any one of claims 1-8 when executing the computer program.
10. A computer-readable storage medium storing computer instructions, which when executed by a processor implement the steps of the method of any one of claims 1 to 8.
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