Respiration heart rate detection method and device based on FMCW radar

Abstract

The invention relates to a breathing heart rate detection method and device based on an FMCW radar, and belongs to the technical field of signal processing.The breathing heart rate detection method based on the FMCW radar comprises the steps that FMCW radar detection signals are preprocessed, and thoracic cavity phase signals are obtained; performing time sequence data decomposition on the thoracic cavity phase signals, and determining a target signal from the thoracic cavity phase signals after time sequence data decomposition based on the reference range of the respiratory frequency and the heartbeat frequency of the target population; performing multi-scale decomposition on the target signal, and separating a target respiration signal and a target heartbeat signal from the target signal; and based on a spatial spectrum estimation algorithm, determining the breathing frequency and the heartbeat frequency of the to-be-detected person from the target breathing signal and the target heartbeat signal. According to the method, the respiratory heart rate detection is realized, the influence of noise on the detection result is reduced, and the accuracy of the respiratory heart rate detection is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular to a method and device for detecting breathing and heart rate based on FMCW radar. Background Art

[0002] Respiratory rate and heart rate are important vital signs of human beings, and can be used as an important basis for human health status and disease diagnosis. At present, the detection of respiratory rate and heart rate is mainly contact detection method, such as electrocardiograph, photoplethysmography, etc. Such methods rely on contact sensors to directly contact the human body for measurement. However, it is not easy to perform contact detection for patients with skin diseases, burns, etc. Millimeter wave radar is a non-contact sensor that can extract human vital signs information using micro-motion signals on the human body surface. It has broad development prospects and practical value in clinical medicine, health monitoring, disaster relief, military security and other scenarios.

[0003] Frequency Modulated Continuous Wave (FMCW) radar detection has the advantages of high-precision distance resolution, long-distance detection accuracy, and low hardware cost, so it has become the preferred method for respiratory and heart rate detection research. However, the existing FMCW radar-based respiratory and heart rate monitoring solutions are greatly affected by environmental noise, and the estimation of respiratory rate and heart rate is not accurate enough. Summary of the invention

[0004] In view of this, it is necessary to provide a method and device for detecting respiratory and heart rate based on FMCW radar to solve the problem that the existing respiratory and heart rate monitoring scheme based on FMCW radar is greatly affected by environmental noise and the estimation of respiratory rate and heart rate is not accurate enough.

[0005] In order to solve the above problems, the present invention provides a method for detecting respiratory heart rate based on FMCW radar, comprising: Preprocess the FMCW radar detection signal to obtain a chest phase signal; Performing time series data decomposition on the chest phase signal, and determining a target signal from the chest phase signal after the time series data decomposition based on a reference range of the respiratory rate and the heart rate of the target population; Performing multi-scale decomposition on the target signal to separate a target breathing signal and a target heartbeat signal from the target signal; Based on a spatial spectrum estimation algorithm, the breathing frequency and the heart rate of the person to be measured are determined from the target breathing signal and the target heart rate signal.

[0006] In a possible implementation, preprocessing the FMCW radar detection signal to obtain the chest phase signal includes: Performing a range-dimensional fast Fourier transform on the FMCW radar detection signal to obtain a real signal and an imaginary signal corresponding to the FMCW radar detection signal; Based on the inverse tangent function, performing phase calculation on the real signal and the imaginary signal to obtain a first phase signal; Performing an unwrapping process on the first phase signal to obtain a second phase signal; The second phase signal is differentially processed to obtain the chest cavity phase signal.

[0007] In a possible implementation, performing unwrapping processing on the first phase signal to obtain a second phase signal includes: The first phase signal is unwrapped based on the following formula to obtain the second phase signal:

[0008] in, represents the second phase signal, represents the first phase signal, represents the absolute value operation, Represents the time domain.

[0009] In a possible implementation, performing time series data decomposition on the chest phase signal, and determining a target signal from the chest phase signal after time series data decomposition based on a reference range of a target population's respiratory rate and heart rate, includes: Decomposing the chest cavity phase signal by time series data based on a single-round integrated empirical mode decomposition algorithm to obtain a plurality of intrinsic mode function components corresponding to the chest cavity phase signal; The target signal is determined from the multiple intrinsic mode function components based on a reference range of the respiratory frequency and the heart rate of the target population.

[0010] In a possible implementation, the time series data decomposition of the chest phase signal based on the single-round integrated empirical mode decomposition algorithm is performed to obtain a plurality of intrinsic mode function components corresponding to the chest phase signal, including: Performing multiple empirical mode decompositions on the chest cavity phase signal, and adding white noise to the chest cavity phase signal when performing the first empirical mode decomposition; Based on the mean of the intrinsic mode function components obtained by performing multiple empirical mode decompositions on the chest phase signal, multiple intrinsic mode function components corresponding to the chest phase signal are obtained.

[0011] In a possible implementation, performing multi-scale decomposition on the target signal to separate a target breathing signal and a target heartbeat signal from the target signal includes: The target signal is decomposed at multiple scales based on a discrete wavelet transform algorithm to separate the target breathing signal and the target heartbeat signal from the target signal.

[0012] In a possible implementation, the determining the breathing rate and the heart rate of the person to be measured from the target breathing signal and the target heart rate signal based on the spatial spectrum estimation algorithm includes: Based on a frequency division multi-signal classification algorithm, the target breathing signal and the target heartbeat signal are converted from the time domain to the angle frequency domain; The breathing frequency and heart rate of the person to be measured are determined based on the frequency values ​​corresponding to the peak angle points of the target breathing signal and the target heart rate signal converted into the angle frequency domain.

[0013] The present invention also provides a breathing and heart rate detection device based on FMCW radar, comprising: A preprocessing module, used for preprocessing the FMCW radar detection signal to obtain a chest phase signal; A time series decomposition module, used for performing time series data decomposition on the chest phase signal, and determining a target signal from the chest phase signal after time series data decomposition based on a reference range of the respiratory frequency and heart rate of the target population; A separation module, used for performing multi-scale decomposition on the target signal, and separating a target breathing signal and a target heartbeat signal from the target signal; The determination module is used to determine the breathing frequency and the heart rate of the person to be measured from the target breathing signal and the target heart rate signal based on a spatial spectrum estimation algorithm.

[0014] The present invention also provides an electronic device, including a memory and a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the respiratory heart rate detection method based on FMCW radar as described above is implemented.

[0015] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the FMCW radar-based respiration and heart rate detection method as described above is implemented.

[0016] The beneficial effects of the present invention are as follows: the FMCW radar-based respiration and heart rate detection method and device provided by the present invention first pre-process the FMCW radar detection signal to achieve preliminary denoising and obtain a chest phase signal, then further remove the interference of clutter signals in the chest phase signal by time series data decomposition to obtain a target signal, and then separate the respiration signal and the heartbeat signal from the target signal by multi-scale decomposition, and finally determine the respiration frequency and the heartbeat frequency of the person to be tested from the respiration signal and the heartbeat signal by a spatial spectrum estimation algorithm, thereby realizing the respiration and heart rate detection of the person to be tested. While realizing the respiration and heart rate detection, the present invention reduces the influence of noise on the detection result, thereby improving the accuracy of the respiration and heart rate detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a flow chart of an embodiment of a method for detecting respiratory heart rate based on FMCW radar provided by the present invention; Figure 2 A schematic diagram of a flow chart of an embodiment of a FMCW radar detection process provided by the present invention; Figure 3 A schematic diagram of a process flow of an embodiment of a respiratory and heartbeat signal processing process provided by the present invention; Figure 4 A schematic diagram of a flow chart of an embodiment of a respiratory and heartbeat signal separation process provided by the present invention; Figure 5 A schematic diagram of a process flow of an embodiment of a process for separating respiratory and heartbeat signals based on a DWT algorithm provided by the present invention; Figure 6 A schematic diagram of a process flow of an embodiment of a process for estimating respiratory heart rate based on the FDMUSIC algorithm provided by the present invention; Figure 7 A schematic structural diagram of an embodiment of a respiratory heart rate detection device based on FMCW radar provided by the present invention; Figure 8 A schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0019] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0020] In the description of the present invention, reference to an "embodiment" means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive of other embodiments. It is explicitly and implicitly understood by those skilled in the art that the described embodiments may be combined with other embodiments.

[0021] Respiratory rate and heart rate are important vital signs of human beings, and can be used as an important basis for human health status and disease diagnosis. At present, the detection of respiratory rate and heart rate is mainly contact detection method, such as electrocardiograph, photoplethysmography, etc. Such methods rely on contact sensors to directly contact the human body for measurement. However, it is not easy to perform contact detection for patients with skin diseases, burns, etc. Millimeter wave radar is a non-contact sensor that can extract human vital signs information using micro-motion signals on the human body surface. It has broad development prospects and practical value in clinical medicine, health monitoring, disaster relief, military security and other scenarios.

[0022] FMCW radar detection has become the preferred method for respiratory and heart rate detection research due to its advantages such as high-precision distance resolution, long-distance detection accuracy, and low hardware cost. However, the existing FMCW radar-based respiratory and heart rate monitoring solutions are greatly affected by environmental noise, and the estimation of respiratory rate and heart rate is not accurate enough.

[0023] In order to solve the above problems, the present invention provides a breathing and heart rate detection method based on FMCW radar to improve the accuracy of breathing rate and heart rate estimation.

[0024] The specific embodiments are described in detail below: A specific embodiment of the present invention discloses a method for detecting respiratory heart rate based on FMCW radar, combined with Figure 1 Come and see, Figure 1 The flowchart of an embodiment of a method for detecting respiratory heart rate based on FMCW radar provided by the present invention includes steps S101 to S104, wherein: In step S101, the FMCW radar detection signal is preprocessed to obtain a chest cavity phase signal; In step S102, the chest phase signal is decomposed into time series data, and a target signal is determined from the chest phase signal after the time series data decomposition based on a reference range of the respiratory frequency and heart rate of the target population; In step S103, the target signal is subjected to multi-scale decomposition to separate a target breathing signal and a target heartbeat signal from the target signal; In step S104, based on a spatial spectrum estimation algorithm, the breathing frequency and the heart rate of the person to be measured are determined from the target breathing signal and the target heart rate signal.

[0025] During implementation, the detection signal of the FMCW radar on the person to be tested (i.e., the FMCW radar detection signal) can be first obtained, and then the FMCW radar detection signal can be preprocessed to achieve preliminary denoising and obtain the chest phase signal. The displacement change caused by the chest movement of the person to be tested corresponds to the phase change of the FMCW radar detection signal, so the FMCW radar detection signal can reflect the movement of human breathing and heartbeat.

[0026] In order to further remove the interference of clutter signals in the chest phase signal, the chest phase signal can also be decomposed into time series data, and then the target signal can be determined from the chest phase signal after the time series data decomposition based on the reference range of the respiratory rate and heart rate of the target population (such as healthy adults). The target signal can include a target respiratory signal and a target heart rate signal.

[0027] In order to separate the target breathing signal and the target heartbeat signal from the target signal, the target signal may be decomposed in a multi-scale manner, and then the target breathing signal and the target heartbeat signal may be separated from the target signal after the multi-scale decomposition.

[0028] Finally, the respiratory rate and heart rate of the person to be tested can be determined from the target respiratory signal and the target heart rate signal through the spatial spectrum estimation algorithm, thereby realizing the respiratory and heart rate detection of the person to be tested.

[0029] The FMCW radar-based respiratory and heart rate detection method provided by the present invention can be applied to sleep monitoring scenarios, or other auxiliary diagnosis and treatment scenarios that require non-contact methods to obtain physiological parameters, and the present invention does not make specific limitations on this.

[0030] Compared with the prior art, the FMCW radar-based respiration and heart rate detection method provided in this embodiment first preprocesses the FMCW radar detection signal to achieve preliminary denoising and obtain a chest phase signal, then further removes the interference of clutter signals in the chest phase signal through time series data decomposition to obtain a target signal, and then separates the respiration signal and the heartbeat signal from the target signal through multi-scale decomposition, and finally determines the respiration frequency and heartbeat frequency of the person to be tested from the respiration signal and the heartbeat signal through a spatial spectrum estimation algorithm, thereby realizing respiration and heart rate detection of the person to be tested. While realizing respiration and heart rate detection, the present invention reduces the influence of noise on the detection result, thereby improving the accuracy of respiration and heart rate detection.

[0031] Exemplarily, the preprocessing of the FMCW radar detection signal to obtain the chest phase signal includes: Performing a range-dimensional fast Fourier transform on the FMCW radar detection signal to obtain a real signal and an imaginary signal corresponding to the FMCW radar detection signal; Based on the inverse tangent function, performing phase calculation on the real signal and the imaginary signal to obtain a first phase signal; Performing an unwrapping process on the first phase signal to obtain a second phase signal; The second phase signal is differentially processed to obtain the chest cavity phase signal.

[0032] Specifically, when preprocessing the FMCW radar detection signal, firstly, a distance-dimensional Fast Fourier Transform (FFT) may be performed on the FMCW radar detection signal to obtain a real signal and an imaginary signal corresponding to the FMCW radar detection signal.

[0033] Then, the inverse tangent function can be used to solve the phase of the real signal and the imaginary signal to obtain the first phase signal. is monotonically increasing, and If the phase value is When the phase signal is close to the positive value, a sudden change from a positive value to a negative value occurs. In order to eliminate the influence of this sudden change on subsequent signal processing, it is also necessary to unwrap the first phase signal to obtain the second phase signal.

[0034] In addition, compared with the environmental clutter signal, the signal-to-noise ratio of the chest phase signal is very small and is almost submerged in the environmental clutter signal. Therefore, the second phase signal can also be differentially processed to enhance the signal-to-noise ratio of the chest phase signal to obtain the chest phase signal.

[0035] For example, the second phase signal can be differentiated by the following formula:

[0036] in, represents the chest phase signal, represents the second phase signal, Represents the time domain.

[0037] Exemplarily, performing unwrapping processing on the first phase signal to obtain a second phase signal includes: The first phase signal is unwrapped based on the following formula to obtain the second phase signal:

[0038] in, represents the second phase signal, represents the first phase signal, represents the absolute value operation, Represents the time domain.

[0039] Specifically, when the first phase signal is unwrapped, the first phase signal can be unwrapped by the above formula to obtain the second phase signal, thereby eliminating the tangent function when the phase value is When it is near, there is a sudden change from positive to negative value.

[0040] Exemplarily, performing time series data decomposition on the chest phase signal, and determining a target signal from the chest phase signal after time series data decomposition based on a reference range of a target population's respiratory rate and heart rate, includes: Decomposing the chest cavity phase signal by time series data based on a single-round integrated empirical mode decomposition algorithm to obtain a plurality of intrinsic mode function components corresponding to the chest cavity phase signal; The target signal is determined from the multiple intrinsic mode function components based on a reference range of the respiratory frequency and the heart rate of the target population.

[0041] Specifically, when determining the target signal from the chest phase signal after time series data decomposition according to the reference range of the target population's respiratory rate and heart rate, the chest phase signal can first be decomposed by the single ensemble empirical mode decomposition algorithm (SEEMD) to obtain multiple intrinsic mode function (IMF) components corresponding to the chest phase signal. Then, according to the reference range of the target population's respiratory rate and heart rate, the target signal is determined from the multiple intrinsic mode function components.

[0042] For example, the normal breathing rate of an adult is 12-20 times / minute, and the normal heart rate is 60-100 times / minute. When selecting frequency domain signals, due to the need to consider the influence of harmonics, the frequency band selection range of the signal needs to be appropriately expanded. That is, the reference range of the breathing and heart rates of adults can be determined as 0.1-0.5Hz and 0.8-2Hz, respectively. Based on this frequency range, the target signal can be determined from multiple intrinsic mode function components.

[0043] Exemplarily, the time series data decomposition of the chest phase signal based on the single-round integrated empirical mode decomposition algorithm to obtain multiple intrinsic mode function components corresponding to the chest phase signal includes: Performing multiple empirical mode decompositions on the chest cavity phase signal, and adding white noise to the chest cavity phase signal when performing the first empirical mode decomposition; Based on the mean of the intrinsic mode function components obtained by performing multiple empirical mode decompositions on the chest phase signal, multiple intrinsic mode function components corresponding to the chest phase signal are obtained.

[0044] Specifically, the SEEMD algorithm reduces the number of times white noise is added based on the Ensemble Empirical Mode Decomposition (EEMD) algorithm, and only adds white noise assistance in the first round of phase signal decomposition, thereby reducing the influence of artificial noise interference on the target signal separation during the phase signal decomposition process.

[0045] When the chest phase signal is decomposed into time series data by the SEEMD algorithm to obtain multiple IMF components corresponding to the chest phase signal, the chest phase signal can firstly be subjected to multiple empirical mode decompositions, and white noise can be added to the chest phase signal during the first empirical mode decomposition.

[0046] For example, the first empirical mode decomposition of the chest phase signal can be performed by the following formula:

[0047] in, represents the chest phase signal after adding white noise, represents the number of IMF components, Indicates IMF components, Represents the noise component.

[0048] The subsequent empirical mode decomposition can be expressed by the following formula:

[0049] in, Indicates Empirical Mode Decomposition.

[0050] After completing multiple empirical mode decompositions, multiple IMF components corresponding to the chest phase signal can be obtained by taking the mean of the IMF components obtained by performing multiple empirical mode decompositions on the chest phase signal. This process can be expressed by the following formula:

[0051] in, Indicates the chest phase signal corresponding to the IMF components, represents the total number of empirical mode decompositions, Indicates The empirical mode decomposition IMF components.

[0052] Exemplarily, performing multi-scale decomposition on the target signal to separate a target breathing signal and a target heartbeat signal from the target signal includes: The target signal is decomposed at multiple scales based on a discrete wavelet transform algorithm to separate the target breathing signal and the target heartbeat signal from the target signal.

[0053] Specifically, when performing multi-scale decomposition on the target signal and separating the target breathing signal and the target heartbeat signal from the target signal, the discrete wavelet transform (DWT) algorithm can be used to perform multi-scale decomposition on the target signal, thereby separating the target breathing signal and the target heartbeat signal from the target signal. At the same time, in order to reduce the computational complexity, the scaling factor and translation factor of the DWT algorithm can be set to ( is an integer greater than 0).

[0054] Exemplarily, the determining the breathing rate and the heart rate of the person to be measured from the target breathing signal and the target heart rate signal based on the spatial spectrum estimation algorithm includes: Based on a frequency division multi-signal classification algorithm, the target breathing signal and the target heartbeat signal are converted from the time domain to the angle frequency domain; The breathing frequency and heart rate of the person to be measured are determined based on the frequency values ​​corresponding to the peak angle points of the target breathing signal and the target heart rate signal converted into the angle frequency domain.

[0055] Specifically, when determining the respiratory rate and heart rate of the person to be tested from the target respiratory signal and the target heart rate signal according to the spatial spectrum estimation algorithm, the target respiratory signal and the target heart rate signal can be first converted from the time domain to the angle frequency domain (i.e., the spatial domain) by the frequency division multiple signal classification (FDMUSIC) algorithm. Then, the respiratory rate and heart rate of the person to be tested are determined by the frequency values ​​corresponding to the peak angle points of the target respiratory signal and the target heart rate signal converted to the angle frequency domain.

[0056] The following is a specific application scenario to better illustrate the technical solution of the present invention: Combination Figure 2 Come and see, Figure 2 The present invention provides a flow chart of an embodiment of the FMCW radar detection process. The FMCW radar system is composed of a transmitting antenna, a receiving antenna, a clock, an analog-to-digital converter, a digital circuit, a single-chip microcomputer and a digital signal processor (DSP). The FMCW radar periodically transmits a linear frequency modulation signal whose frequency increases linearly with time through the transmitting antenna. The signal received by the receiving antenna is first amplified by a frequency selective amplifier, and then the receiving signal is mixed with the transmitting signal by a mixer to form an intermediate frequency signal. Finally, the intermediate frequency signal is sampled by the analog-to-digital converter and sent to the DSP for algorithm processing.

[0057] The transmitted FMCW signal can be expressed as:

[0058] in, is the starting frequency of the linear frequency modulation signal, is the bandwidth, is the amplitude of the transmitted signal, is the phase noise, is the width of the linear frequency modulation signal pulse, is the slope of the linear frequency modulation signal.

[0059] The received signal and the transmitted signal are mixed by two orthogonal input and output channels, and then amplified by frequency selection to obtain an intermediate frequency signal:

[0060] Perform FFT on the intermediate frequency signal to obtain the frequency distribution related to the target distance. The spectrum peak corresponds to the distance between the subject and the radar sensor, and the phase change Distance from human body to radar There is a corresponding relationship between them:

[0061] in, The displacement caused by the target chest movement corresponds to the phase change of the FMCW signal, which can reflect the movement of human breathing and heartbeat.

[0062] Combination Figure 3 Come and see, Figure 3 The present invention provides a schematic flow chart of an embodiment of the respiratory heartbeat signal processing process, wherein the signal processing process mainly consists of three parts: preprocessing, signal separation, and respiratory rate and heart rate estimation. The intermediate frequency signal is first preprocessed to obtain a chest phase signal, and then the chest phase signal is converted into signals on multiple time scales by the SEEMD algorithm to obtain an optimal scale signal. Subsequently, the DWT algorithm is used to separate the respiratory signal and the heartbeat signal from the optimal scale signal. Finally, the FDMUSIC algorithm is used to estimate the heart rate and respiratory rate.

[0063] After the FMCW radar converts the detected chest displacement information of the human body into an intermediate frequency signal, it outputs the corresponding real and imaginary parts of the intermediate frequency signal after the distance dimension FFT, and solves the phase signal through the inverse tangent function:

[0064] in, is the real data, is the imaginary data, is the chest phase signal.

[0065] Since the tangent function is is monotonically increasing, and If the phase value is When it is near, there is a sudden change from positive to negative value, so the phase obtained by the inverse tangent method needs to be unwrapped:

[0066] in, is the chest phase signal after unwrapping.

[0067] Compared with the environmental clutter signal, the signal-to-noise ratio of the chest phase signal is very small and is almost submerged in the environmental clutter signal. Therefore, it is necessary to perform differential processing on the unwrapped chest phase signal to enhance the signal-to-noise ratio of the chest phase signal:

[0068] Combination Figure 4 Come and see, Figure 4This is a flow chart of an embodiment of the respiratory and heartbeat signal separation process provided by the present invention. In addition to the respiratory signal and the heartbeat signal, the chest phase signal after the signal-to-noise ratio is enhanced actually still contains interference from clutter signals. Therefore, the SEEMD algorithm can be used to separate the target signal containing the respiratory signal and the heartbeat signal from the clutter signal.

[0069] The SEEMD algorithm is based on the EEMD algorithm, which reduces the number of times white noise is added, and only adds white noise assistance during the first round of phase signal decomposition, thereby reducing the impact of artificial noise interference on target signal separation during the phase signal decomposition process. The specific process of the SEEMD algorithm is as follows: First, extract the phase signal Add a white noise , get a new signal for:

[0070] Signal Multiple decompositions are performed to obtain various scale components, namely IMF components:

[0071] in, is the quantity of the component, is the IMF component subscript index, is the noise component.

[0072] SEEMD introduces white noise only in the first round of phase signal decomposition, and does not introduce white noise in subsequent decompositions. The expression of subsequent components is:

[0073] Replace all decomposed components with the average component, and the final IMF component expression is:

[0074] After obtaining the final IMF component, select the better signal segment as the optimal scale signal, that is, the target signal, based on experience, and discard the IMF component containing noise. When selecting the time domain signal, the selection experience is based on the normal range of adult vital sign parameters generally recognized by doctors (normal breathing rate: 12-20 times / minute, normal heart rate: 60-100 times / minute). When selecting the frequency domain signal, considering the influence of harmonics, the frequency band selection range of the signal needs to be appropriately expanded, that is, the breathing and heart rates of adults are 0.1-0.5Hz and 0.8-2Hz respectively.

[0075] After obtaining the target signal, the DWT algorithm is used to separate the breathing signal and the heartbeat signal. Figure 5 Come and see, Figure 5 The present invention provides a flowchart of an embodiment of the process of separating respiratory and heartbeat signals based on the DWT algorithm. The DWT algorithm discretizes the scale parameters and translation parameters of the continuous wavelet transform. In order to reduce the amount of calculation, the scaling factor and the translation factor are selected ( is an integer greater than 0), then the discrete wavelet transform can be defined as:

[0076] in, is the target signal, is the wavelet packet function.

[0077] After obtaining the respiratory signal and heartbeat signal through DWT separation, the FDMUSIC algorithm is used to estimate the respiratory rate and heart rate. As a spatial spectrum estimation algorithm, the FDMUSIC algorithm further subdivides the sub-signal space into respiratory signal subspace and heartbeat signal subspace based on the MUSIC algorithm according to the different frequencies of respiratory and heartbeat signals, and then estimates the corresponding frequency based on the angle value of the maximum energy convergence in different subspaces. This frequency is the respiratory frequency and heartbeat frequency estimated by the FDMUSIC algorithm. Figure 6 Come and see, Figure 6 A flowchart of an embodiment of a process for estimating respiratory heart rate based on the FDMUSIC algorithm provided by the present invention.

[0078] The FDMUSIC algorithm takes the breathing signal or heartbeat signal as input signal , is an additive noise vector, whose components are Gaussian white noise:

[0079] Assume that the noise vector With the signal vector The statistics are uncorrelated, and the covariance matrix of the observed data vector is :

[0080] Will The feature decomposition is:

[0081]

[0082]

[0083] in, Included A large eigenvalue, is the subspace vector, for The conjugate of is the noise vector of the subspace, for The conjugation of.

[0084] Since the normal range of the frequency of the respiratory heartbeat signal is 0.1-0.5Hz and 0.8-2Hz, the heartbeat signal will contain harmonic components of the respiratory signal with a frequency above 0.5Hz. Similarly, the respiratory signal will contain harmonic components of the heartbeat signal below 0.8Hz. In order to solve the influence of the harmonic components on the frequency estimation of each signal, the large eigenvalue of the first quarter of the respiratory signal separated by DWT is used as the respiratory signal subspace, as shown in the following formula:

[0085] in, is the respiratory signal subspace, for The matrix consisting of the first quarter of the eigenvalues.

[0086] The first three quarters of the large eigenvalues ​​in the heartbeat signal are taken as the heartbeat signal subspace, as shown in the following formula:

[0087] in, is the heartbeat signal subspace, for The matrix consisting of the first three quarters of the eigenvalues.

[0088] Therefore, the power spectrum function of the breathing signal is as follows:

[0089] The power spectrum function of the heartbeat signal is shown as follows:

[0090] in, is the direction vector The conjugation of.

[0091] The cost function of the heartbeat signal is obtained ,turn up The peak value of the heart rate corresponding to the peak point is obtained Similarly, find the cost function of the breathing signal The peak value of the peak point corresponds to the respiratory frequency .

[0092] The final heart rate (HR) and breathing rate (BR) can be obtained by the following formula:

[0093]

[0094] In order to verify the effect of the solution provided by the present invention, the present invention also selected 6 groups of experiments, each of which used EEMD-DWT, Adaptive Variational Mode Decomposition (APVMD)-DWT, SEEMD-DWT (the solution of the present invention) three separation algorithms combined with FFT and FDMUSIC two estimation algorithms for detection, so each group of experiments used 6 different algorithm combinations for detection to obtain 6 respiratory rate detection results and 6 heart rate detection results. The respiratory rate detection results of the 6 groups of experiments are shown in Table 1, and the heart rate detection results are shown in Table 2.

[0095] Table 1: Comparison of respiratory rate test results

[0096] Table 2: Comparison of heart rate test results

[0097] It can be seen from Table 1 and Table 2 that, when the separation algorithm is the same, the respiratory rate and heart rate detection results obtained by using the FDMUSIC estimation algorithm are closer to the reference value than those obtained by using the FFT algorithm. When the estimation algorithm is the same, the respiratory rate and heart rate detection results obtained by using the algorithm provided by the present invention are closer to the reference value than those obtained by using the EEMD-DWT and APVMD-DWT algorithms. Comprehensive comparison shows that the SEEMD-DWT-FDMUSIC method provided by the present invention has more accurate detection results than the traditional method.

[0098] In order to more intuitively reflect the detection results of respiratory rate (BR) and heart rate (HR), three statistical indicators, namely mean, variance and accuracy, are used to evaluate the detection effects of the method of the present invention and the traditional method.

[0099] Table 3: Test results analysis and comparison table

[0100] As shown in Table 3, the mean values ​​of respiratory rate and heart rate detection of the three methods all meet the vital sign standards of normal adults. The variances of respiratory rate and heart rate detection of the method of the present invention are 0.90 and 1.50, respectively, and the accuracy rates are 94.62% and 97.81%, respectively. The accuracy rates of respiratory rate and heart rate detection of the EEMD-DWT-FDMUSIC method are 90% and 96.84%, respectively, and the accuracy rates of respiratory rate and heart rate detection of the APVMD-DWT-FDMUSIC method are 92.21% and 97.32%, respectively. Compared with the two traditional methods, the method of the present invention has better stability and higher accuracy in the detection of respiratory rate and heart rate.

[0101] The present invention first uses the SEEMD algorithm to perform multi-scale decomposition of the chest phase signal and obtain the optimal scale signal to reduce the influence of environmental noise. Then the optimal scale is separated by the DWT algorithm to obtain more accurate respiratory signals and heartbeat signals. Finally, the FDMUSIC algorithm is used to transform the respiratory signal and heartbeat signal into the airspace to eliminate the influence of respiratory harmonics, and achieve accurate estimation of respiratory rate and heart rate. It is suitable for sleep monitoring, auxiliary diagnosis and treatment where it is inconvenient to obtain physiological parameters by contact, and other scenarios.

[0102] The embodiment of the present invention also provides a breathing and heart rate detection device based on FMCW radar, Figure 7 Come and see, Figure 7 This is a schematic diagram of the structure of an embodiment of a respiratory heart rate detection device based on FMCW radar provided by the present invention. The respiratory heart rate detection device based on FMCW radar 700 includes: A preprocessing module 701 is used to preprocess the FMCW radar detection signal to obtain a chest phase signal; A time series decomposition module 702 is used to perform time series data decomposition on the chest phase signal, and determine a target signal from the chest phase signal after the time series data decomposition based on a reference range of the respiratory frequency and heart rate of the target population; A separation module 703 is used to perform multi-scale decomposition on the target signal to separate the target breathing signal and the target heartbeat signal from the target signal; The determination module 704 is used to determine the breathing frequency and the heart rate of the person to be measured from the target breathing signal and the target heart rate signal based on a spatial spectrum estimation algorithm.

[0103] The specific implementation methods of each module of the respiratory and heart rate detection device based on FMCW radar can refer to the description of the respiratory and heart rate detection method based on FMCW radar, and have similar beneficial effects, which will not be repeated here.

[0104] It should be noted that the breathing and heart rate detection device based on FMCW radar can be set in a smart watch or in other smart monitoring equipment, and the present invention does not make specific limitations on this.

[0105] The embodiment of the present invention further provides an electronic device, Figure 8 Come and see, Figure 8 This is a structural diagram of an embodiment of an electronic device provided by the present invention. The electronic device 800 includes a processor 801, a memory 802, and a computer program stored in the memory 802 and executable on the processor 801. When the processor 801 executes the program, the respiratory heart rate detection method based on FMCW radar as described above is implemented.

[0106] As a preferred embodiment, the electronic device 800 further includes a display 803 for displaying that the processor 801 executes the FMCW radar-based respiration and heart rate detection method as described above.

[0107] Exemplarily, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory 802, and are executed by the processor 801 to complete the present invention. One or more modules / units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device 800. For example, the computer program may be divided into the preprocessing module 701, the timing decomposition module 702, the separation module 703, and the determination module 704 in the above-mentioned embodiment. The specific functions of each module are as described above, and are not described one by one here.

[0108] The electronic device 800 may be a desktop computer, a notebook, a PDA or a smart phone with an adjustable camera module.

[0109] Among them, the processor 801 may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 801 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present invention can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0110] The memory 802 may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electric erasable programmable read-only memory (EEPROM), etc. The memory 802 is used to store programs, and the processor 801 executes the program after receiving the execution instruction. The process definition method disclosed in any of the embodiments of the present invention may be applied to the processor 801 or implemented by the processor 801.

[0111] The display 803 may be an LCD display screen or an LED display screen, for example, a display screen on a mobile phone.

[0112] Understandably, Figure 8 The structure shown is only a schematic diagram of a structure of the electronic device 800. The electronic device 800 may also include Figure 8 More or fewer components as shown. Figure 8 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0113] The electronic device provided according to the above-mentioned embodiment of the present invention can be implemented with reference to the specific description of the respiratory heart rate detection method based on FMCW radar as described above according to the present invention, and has similar beneficial effects as the respiratory heart rate detection method based on FMCW radar as described above, which will not be repeated here.

[0114] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned method for detecting respiration and heart rate based on FMCW radar is implemented.

[0115] Generally speaking, the computer instructions for implementing the method of the present invention can be carried by any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media can include any computer-readable media except for the signal itself that is temporarily propagating.

[0116] Computer-readable storage media may be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium that contains or stores a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0117] Computer program codes for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. may be used. The program code may be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0118] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0119] The invention discloses a method and device for detecting a respiration and heart rate based on an FMCW radar. Firstly, the FMCW radar detection signal is preprocessed to achieve preliminary denoising and obtain a chest phase signal. Then, the interference of a clutter signal in the chest phase signal is further removed by time series data decomposition to obtain a target signal. Then, a respiration signal and a heartbeat signal are separated from the target signal by multi-scale decomposition. Finally, a spatial spectrum estimation algorithm is used to determine the respiration frequency and heartbeat frequency of a person to be tested from the respiration signal and the heartbeat signal, thereby realizing the respiration and heart rate detection of the person to be tested. While realizing the respiration and heart rate detection, the invention reduces the influence of noise on the detection result, thereby improving the accuracy of the respiration and heart rate detection.

[0120] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for detecting respiratory heart rate based on FMCW radar, characterized in that: include: Preprocess the FMCW radar detection signal to obtain a chest phase signal; Performing time series data decomposition on the chest phase signal, and determining a target signal from the chest phase signal after the time series data decomposition based on a reference range of the respiratory rate and the heart rate of the target population; Performing multi-scale decomposition on the target signal to separate a target breathing signal and a target heartbeat signal from the target signal; Based on a spatial spectrum estimation algorithm, the breathing frequency and the heart rate of the person to be measured are determined from the target breathing signal and the target heart rate signal.

2. The method for detecting respiratory heart rate based on FMCW radar according to claim 1, characterized in that: The preprocessing of the FMCW radar detection signal to obtain the chest cavity phase signal includes: Performing a range-dimensional fast Fourier transform on the FMCW radar detection signal to obtain a real signal and an imaginary signal corresponding to the FMCW radar detection signal; Based on the inverse tangent function, performing phase calculation on the real signal and the imaginary signal to obtain a first phase signal; Performing an unwrapping process on the first phase signal to obtain a second phase signal; The second phase signal is differentially processed to obtain the chest cavity phase signal.

3. The method for detecting respiratory heart rate based on FMCW radar according to claim 2, characterized in that: The step of performing unwrapping processing on the first phase signal to obtain a second phase signal includes: The first phase signal is unwrapped based on the following formula to obtain the second phase signal: in, represents the second phase signal, represents the first phase signal, represents the absolute value operation, Represents the time domain.

4. The method for detecting respiratory heart rate based on FMCW radar according to claim 1, characterized in that: The step of performing time series data decomposition on the chest phase signal and determining a target signal from the chest phase signal after time series data decomposition based on a reference range of a respiratory frequency and a heart rate of a target population includes: Decomposing the chest cavity phase signal by time series data based on a single-round integrated empirical mode decomposition algorithm to obtain a plurality of intrinsic mode function components corresponding to the chest cavity phase signal; The target signal is determined from the multiple intrinsic mode function components based on a reference range of the respiratory frequency and the heart rate of the target population.

5. The method for detecting respiratory heart rate based on FMCW radar according to claim 4, characterized in that: The method of performing time series data decomposition on the chest phase signal based on a single-round integrated empirical mode decomposition algorithm to obtain a plurality of intrinsic mode function components corresponding to the chest phase signal includes: Performing multiple empirical mode decompositions on the chest cavity phase signal, and adding white noise to the chest cavity phase signal when performing the first empirical mode decomposition; Based on the mean of the intrinsic mode function components obtained by performing multiple empirical mode decompositions on the chest phase signal, multiple intrinsic mode function components corresponding to the chest phase signal are obtained.

6. The method for detecting respiratory heart rate based on FMCW radar according to claim 1, characterized in that: The step of performing multi-scale decomposition on the target signal to separate a target breathing signal and a target heartbeat signal from the target signal includes: The target signal is decomposed at multiple scales based on a discrete wavelet transform algorithm to separate the target breathing signal and the target heartbeat signal from the target signal.

7. The method for detecting respiratory heart rate based on FMCW radar according to claim 1, characterized in that: The method of determining the breathing rate and the heart rate of the person to be measured from the target breathing signal and the target heart rate signal based on the spatial spectrum estimation algorithm includes: Based on a frequency division multi-signal classification algorithm, the target breathing signal and the target heartbeat signal are converted from the time domain to the angle frequency domain; The breathing frequency and heart rate of the person to be measured are determined based on the frequency values ​​corresponding to the peak angle points of the target breathing signal and the target heart rate signal converted into the angle frequency domain.

8. A respiratory heart rate detection device based on FMCW radar, characterized in that: include: A preprocessing module, used for preprocessing the FMCW radar detection signal to obtain a chest phase signal; A time series decomposition module, used for performing time series data decomposition on the chest phase signal, and determining a target signal from the chest phase signal after time series data decomposition based on a reference range of the respiratory frequency and heart rate of the target population; A separation module, used for performing multi-scale decomposition on the target signal, and separating a target breathing signal and a target heartbeat signal from the target signal; The determination module is used to determine the breathing frequency and the heart rate of the person to be measured from the target breathing signal and the target heart rate signal based on a spatial spectrum estimation algorithm.

9. An electronic device, characterized in that: The invention comprises a memory and a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for detecting respiratory heart rate based on FMCW radar according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method for detecting respiration and heart rate based on FMCW radar as claimed in any one of claims 1 to 7 is implemented.

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