CN111856452B - OMP-based static human heartbeat and respiration signal separation and reconstruction method - Google Patents
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Abstract
The invention provides a static human heartbeat and respiratory signal separation and reconstruction method based on OMP. Firstly, analyzing and calculating according to actual human body target detection data to obtain distance information of the human body target and construct a distance-time graph. And then, carrying out direct current offset correction on the I/Q two paths of signals, and calculating phase information through an arc tangent function. And then, carrying out phase expansion on the detected human body target to be detected based on the extended DACM algorithm of the derivative operation, solving the problem of phase ambiguity, and carrying out phase difference to enhance the heartbeat signal. And separating the heartbeat signal and the respiration signal by using two second-order cascaded fourth-order IIR band-pass filters, and finally completing the separation and reconstruction of the respiration signal and the heartbeat signal by using a compressive sensing theory and an OMP algorithm. The invention innovatively provides a static human heartbeat and respiratory signal separation and reconstruction method based on orthogonal matching pursuit, effectively reduces the influence of harmonic waves and noise on the estimation of the heart rate and the respiratory frequency, and greatly improves the accuracy of the final estimation of the heart rate and the respiratory frequency.
Description
Technical Field
The invention is mainly used for detecting vital signals, and particularly relates to a static human heartbeat and respiratory signal separation and reconstruction method based on OMP.
Background
The traditional life signal detection methods include a pressure sensor method, an electrocardiogram method and the like. However, these methods achieve the purpose of monitoring the heartbeat and respiration signals by direct physical contact, so that the measured person feels uncomfortable or even impossible to detect. Meanwhile, the detection result of the contact method can be interfered by the body surface micromotion due to various physiological activities of the human body. The non-contact life signal detection technology can realize the detection of breathing and heartbeat signals under the condition of not contacting the body, and can provide more relaxed and comfortable experience for the human body. The non-contact monitoring sensor proposed by EarlySense screens the patient for new coronary pneumonia (COVID-19) by monitoring the heart rate, respiration and other data of the patient. Therefore, with the attention of modern society to health and medical care, the non-contact vital sign detection technology is becoming one of the most attractive technologies.
Based on this, a Frequency Modulated Continuous Wave (FMCW) non-contact life monitoring radar is a research hotspot in the technical field of non-contact life feature monitoring. The FMCW radar can not only obtain the displacement motion of the target through Doppler detection, but also obtain the absolute distance of the target through a distance measurement algorithm. The FMCW radar sensor emits electromagnetic waves with specific waveforms to a human body, extracts and processes the phase of an echo signal, and then detects the motion characteristics of respiration and heartbeat of the human body. The radar life signal detection technology directly monitors respiration heartbeat signals, and has wide application requirements in the fields of emergency rescue, sleep monitoring, fall detection and the like.
Since the vital signal is a composite signal of respiration and heartbeat, although the heartbeat and respiration signals are periodic, when harmonic frequencies and noise frequencies exist, so that the main respiration signal is not an ideal periodic sine wave, the heartbeat and respiration frequencies are easily buried in the harmonics or noise. Particularly, the heartbeat frequency is more obvious even after the difference, when the main respiratory signal is not periodic, a harmonic peak value larger than the heartbeat frequency peak value is easy to appear, so that the frequency domain 1D-FFT method and the time domain autocorrelation method have wrong estimation, and the heartbeat signal and the respiratory signal must be separated firstly. There have been studies to design two band-pass filters to separate respiration and heartbeat signals according to the difference between respiration and heartbeat frequency ranges. However, because the displacement of the chest cavity caused by the respiratory motion is far larger than the heartbeat, and the amplitude of the respiratory signal in the vital signal is far larger than the amplitude of the heartbeat signal, the amplitude of the higher harmonic wave of the respiratory signal may exceed the amplitude of the heartbeat signal, and great interference is caused to the extraction of the heartbeat signal. When the frequency spectrums of the signals and the noise are overlapped, the accuracy rate of the respiratory and heartbeat signal estimation is seriously reduced and even cannot be recovered. It is worth noting that there are researchers who propose empirical mode decomposition to achieve separation of respiratory and heartbeat signals, but that it is very computationally expensive.
Therefore, the invention utilizes an Orthogonal Matching Pursuit (OMP) method to separate and reconstruct the heartbeat and respiratory signals, thereby improving the accuracy of heart rate and respiratory rate detection.
Disclosure of Invention
Based on the defects and shortcomings of the existing respiration and heartbeat signal separation method, the invention provides a FMCW radar static human body heartbeat and respiration signal separation and reconstruction method based on orthogonal matching tracking. According to the method, firstly, distance-Time-Map (RTM) is constructed according to distance information obtained by radar echo signals of the human body target so as to determine the position of the human body target to be detected. And then carrying out I/Q two-path direct current offset correction, expanding a nonlinear demodulation phase by using an expanded differential and cross multiplication (DACM) algorithm, and simultaneously enhancing the heartbeat signal by using a differential phase so as to further extract accurate phase change information caused by respiratory and thoracic displacement motion. And finally, separating and reconstructing the heartbeat and the respiratory signals by adopting an orthogonal matching tracking method, and effectively reducing the influence of harmonic waves and noise on the heart rate and respiratory rate estimation.
A static human heartbeat and respiration signal separation and reconstruction method based on OMP specifically comprises the following steps:
1) collecting human body target information by using an FMCW radar to obtain radar intermediate frequency signals, and performing fast Fourier transform on single-frame intermediate frequency signals to obtain a distance vector matrix R m Then, againPerforming multi-frame accumulation through time, and constructing a distance-time matrix R in a column form by using n frame distance vectors T ＝[R 1 ,R 2 ,…R m ] m×n Thereby obtaining a Range-Time-Map (RTM), and further determining the target to be detected through the maximum average power of different distance intervals;
2) carrying out orthogonal down-conversion on a human body target signal B (t) to be detected, carrying out direct current correction by using a differential amplifier to obtain a complex signal I (t) + j.Q (t) consisting of two paths of signals I (t) and Q (t), and obtaining a phase value of a respiratory heartbeat signal by using nonlinear arc tangent demodulation
The arctan trigonometric function calculation is then converted into a derivative operation using the DACM algorithm
Wherein Q (t) and I (t) are differential forms of Q (t) and I (t), respectively, and finally, in discrete form, are reduced by time accumulation
3) An Orthogonal Matching Pursuit (OMP) algorithm is adopted to separate and reconstruct the respiration and heartbeat signals, and the specific steps are as follows:
3a) the heartbeat frequency interval is [0.8Hz-2Hz ], the respiratory frequency interval is [0.1Hz-0.5Hz ], two second-order cascaded fourth-order IIR band-pass filters are designed to separate heartbeat signals from respiratory signals, the sampling rate of the four-order IIR band-pass filters is 20Hz, and the heartbeat signals and the respiratory signals are separated by respectively passing differential signals through the two designed band-pass filters;
3b) sparse representation of heartbeat and respiration signals x ═ ψ (α + w), where ψ ═ ψ 1 ，ψ 2 ，ψ 3 ，…，ψ N The original signal is projected on an M multiplied by N measurement matrix phi to obtain an unadapted projection value y phi x phi psi (alpha + w) A of x CS α + Z, wherein A CS Phi psi is the sensing matrix, Z phi psi wFor the projection value of noise, in order to obtain a noise-free heartbeat signal and a respiration signal, respectively keeping k in y as 1 important component;
3c) solving the norm optimal value of L1, argmin | | | alpha | | Y 1 s.t.||A cs α-y|| 2 Epsilon is less than or equal to epsilon, wherein epsilon is a noise boundary, a weight coefficient alpha is obtained, and an original signal x is reconstructed from x psi alpha;
3d) when the peak value of the frequency spectrum of the reconstructed signal is equal to the peak value of the frequency spectrum of the original signal, outputting the reconstructed signal
3e) Finding out all peak values of reconstructed signal frequency spectrum and reserving the peak values in 0.8Hz-2Hz]Removing respiratory harmonic wave peak by using difference method, and counting frequency of corresponding frequency of peak value of reconstructed signal
Defining a frequency weight coefficient
k represents the occurrence frequency of the corresponding frequency of the reconstruction, and the heartbeat frequency is calculated as
Wherein f is i Is the peak frequency of each set of reconstructed signals, and the respiration rate is obtained by fast fourier transform.
The invention has the following advantages: compared with the traditional technical method, the invention realizes non-contact detection of the vital sign signals of the human body by the FMCW radar, so that the application range is wider and the use condition is looser. The binding and the discomfort brought to the patient by the traditional contact detection equipment are avoided. Based on the prior art, the method for separating and reconstructing the heartbeat and the respiratory signal of the static human body based on the orthogonal matching tracking method is provided. The method effectively reduces the influence of harmonic waves and noise on the heart rate and respiratory rate estimation, and greatly improves the detection accuracy of the respiratory rate and the heart rate.
Drawings
FIG. 1 is a flow chart of human heartbeat and respiration signal detection
FIG. 2 RTM build flow diagram
FIG. 3 amplitude-frequency response diagram of a respiratory filter
FIG. 4 is a graph of the amplitude-frequency response of a heart beat filter
FIG. 5 heartbeat and respiration waveforms and their spectrograms
FIG. 6 OMP reconstructs heartbeat and respiration signals and their spectrograms
Detailed Description
The technical scheme adopted by the invention is as follows: a static human heartbeat and respiration signal separation and reconstruction method based on OMP mainly comprises the following steps:
1) the FMCW radar signal modulation mode has two modes, namely a sawtooth wave mode and a triangular wave mode, and the sawtooth wave modulation mode is adopted in the invention. The emission signal of the radar system meets the object and is reflected in the propagation process and passes through the time delay t d The back radar receives the echo signal, and the transmission signal of the FMCW radar can be expressed as:
wherein the content of the first and second substances,
is the slope of the chirp signal, representing the rate of change of the frequency, f c Is the chirp start frequency, B is the bandwidth, A TX Is the amplitude, T, of the transmitted signal c Is the pulse width of the chirp signal and,
is phase noise.
Let R (t) be the displacement of thoracic cavity movement and the distance d from the radar sensor to the body 0 The distance x (t) from the thorax to the radar, r (t) + d 0 Time delay
The received signal can be obtained:
the echo signal and the sending signal are orthogonally mixed through two paths of I/Q signals and then are processed through a low-pass filter to obtain an intermediate frequency signal S IF (t)：
2) Target distance calculation according to step 1) FMCW radar is:
wherein F s Is the sampling rate, c is the speed of light, and S is the slope of the sawtooth sweep. The single frame beat frequency signal obtained after A/D is a two-dimensional matrix formed by fast sampling and slow sampling, the vertical axis corresponds to a slow time axis constructed by the sweep frequency number, and the horizontal axis is the number M of fast time sampling points samples . In order to inhibit sidelobe leakage, Hamming window filtering is added, and meanwhile FFT is carried out on the fast time sampling points to obtain distance-FFT vectors so as to obtain corresponding radar field distance distribution. And calculating the mean distance spectrum according to columns by using N frequency sweeps in the two-dimensional matrix in a multi-frequency sweep mode. A distance-time diagram is constructed based on a distance spectrum obtained by multi-sweep coherent accumulation, and a specific construction flow is shown in FIG. 2.
3) Extracting phase information from the determined distance interval of 2). And after the detected target is subjected to interference elimination, distance-FFT is carried out on the frame data, and a phase value is extracted from the identified distance interval to carry out vital sign estimation. In equation (3) in step 1), for single target detection, its signal expression form:
B(t)＝cos(2πf b nT m +ψ l ) (6)
4) the two I/Q demodulated signals are expressed as:
B I (t)＝A I cos(2πf b nT m +ψ l )+DC I (7)
B Q (t)＝A Q sin(2πf b nT m +ψ l )+DC Q (8)
in the formula A I /A Q Is the amplitude, DC, of the two paths of I/Q I /DC Q Respectively, the direct current bias of two paths thereof. The DC offset is mainly affected by the defects of the circuit elements, so that the interference needs to be corrected for the acquisition of the DC component
By adopting the circle center dynamic direct current offset tracking, the method can realize the dynamic direct current offset tracking by using an efficient gradient descent algorithm and then carry out direct current offset correction. D.C. offset correction is carried out on the two paths of I/Q, and A is I ＝A Q ＝A R Obtaining:
|B I (t)-DC I | 2 +|B Q (t)-DC Q | 2 ＝A R 2 (9)
using a gradient descent algorithm, the following optimization function is minimized:
when the above equation takes a minimum value, the optimum result will be obtained.
5) An extended differential cross multiplication algorithm is used. Order to
The algorithm can automatically perform phase compensation and solve the problem of phase ambiguity. The DACM algorithm turns the arctan function into a derivative operation, then:
wherein Q (t) and I (t) are differential versions of Q (t) and I (t), respectively. The equation is expressed in discrete form and the integral is accumulated as:
6) the method adopts an OMP algorithm to separate and reconstruct the respiration and heartbeat signals, and comprises the following specific steps:
6a) the heartbeat frequency interval is [0.8Hz-2Hz ], and the respiratory frequency interval is [0.1Hz-0.5Hz ]. Designing two second-order cascaded fourth-order IIR band-pass filters to separate heartbeat signals from respiration signals; the sampling rate is 20 Hz. The filtered amplitude frequency response of the respiration and heartbeat signals is shown in fig. 3 and 4. The difference signals are respectively passed through two designed band-pass filters to separate the heartbeat and respiration signals, as shown in fig. 5.
6b) The heartbeat signal and the respiration signal have sparsity, and the sparse representation of the heartbeat signal and the respiration signal:
x＝ψ(α+w) (13)
where psi ═ psi 1 ，ψ 2 ，ψ 3 ，…，ψ N Frequency domain orthogonal transformation base, alpha is weight coefficient of Nx 1, and w is noise. K important characteristic components in the non-adaptive linear projection value y are reserved, and the original signal is projected to a measuring matrix phi of M multiplied by N [ [ phi ] ] 1 ,φ 2 ,φ 3 ,…,φ N ]Upper, then the adaptive projection value of the signal x
y＝Φx＝ΦΨ(α+w)＝A CS α+Z (14)
Wherein A is CS Phi psi is the perceptual matrix and Z phi psi w is the projected value of the noise.
6c) The measurement matrix M < N, where reconstructing x in y is an ill-defined problem, so equation (14) is an underdetermined equation. Phi and psi H Extremely dissimilar, so the L1 norm is used to find the optimum value:
arg min||α|| 1 s.t.||A cs α-y|| 2 ≤ε (15)
wherein
Is the L1 norm of α, and ε is the noise boundary. After α is obtained, the signal x is reconstructed from equation (13). And respectively keeping 1 important component k in y, and obtaining a noise-free heartbeat or respiration signal.
6d) In order to ensure that the reconstructed signal is a denoised heartbeat or respiration signal, a limiting condition is set, and the reconstructed signal is output when the spectral peak value of the reconstructed signal is equal to the spectral peak value of the original signal, and the result is shown in fig. 6.
6e) The respiration rate is obtained by a fast fourier transform. For a heartbeat spectrum, its maximum peak may not necessarily correspond to the heart rate. For this purpose, all peaks of the reconstructed signal spectrum are found and retained in 0.8Hz-2HZ]Removing respiratory harmonic wave peak by using difference method, counting frequency of reconstructed signal peak value corresponding to frequency, defining a frequency weight coefficient
Where k represents the number of occurrences of the corresponding frequency of reconstruction,
representing the reconstruction times, the heartbeat frequency is:
Claims (1)
1. a static human heartbeat and respiration signal separation and reconstruction method based on OMP specifically comprises the following steps:
1) collecting human body target information by using FMCW (frequency modulated continuous wave) radar to obtain radar intermediate frequency signals, and performing fast Fourier change on single-frame intermediate frequency signals to obtain a distance vector matrix R m Then, multi-frame accumulation is carried out in time, and a distance-time matrix R is constructed by n frame distance vectors in a column form T ＝[R 1 ,R 2 ,…R m ] m×n Thereby obtaining the pitchA Range-Time-Map (RTM) for determining a target to be detected by maximum average power of different distance intervals;
2) carrying out orthogonal down-conversion on a human body target signal B (t) to be detected, carrying out direct current correction by using a differential amplifier to obtain a complex signal I (t) + j.Q (t) consisting of two paths of signals I (t) and Q (t), and obtaining a phase value of a respiratory heartbeat signal by using nonlinear arc tangent demodulation
The arctan trigonometric function calculation is then converted into a derivative operation using the DACM algorithm
Wherein Q (t) and I (t) are differential forms of Q (t) and I (t), respectively, and finally, in discrete form, are reduced by time accumulation
3) An Orthogonal Matching Pursuit (OMP) algorithm is adopted to separate and reconstruct the respiration and heartbeat signals, and the specific steps are as follows:
3a) the heartbeat frequency interval is [0.8Hz-2Hz ], the respiratory frequency interval is [0.1Hz-0.5Hz ], two second-order cascaded fourth-order IIR band-pass filters are designed to separate heartbeat signals from respiratory signals, the sampling rate of the four-order IIR band-pass filters is 20Hz, and the heartbeat signals and the respiratory signals are separated by respectively passing differential signals through the two designed band-pass filters;
3b) sparse representation of heartbeat and respiration signals x ═ ψ (α + w), where ψ ═ ψ 1 ，ψ 2 ，ψ 3 ，…，ψ N The original signal is projected on an M multiplied by N measurement matrix phi to obtain an unadapted projection value y phi x phi psi (alpha + w) A of x CS α + Z, wherein A CS The method comprises the following steps that phi psi is a perception matrix, Z phi psi w is a projection value of noise, and k in y is respectively reserved as 1 important component in order to obtain a noise-free heartbeat signal and a noise-free respiration signal;
3c) solving the L1 modelNumerical optimum value, argmin | | | alpha | | non-woven phosphor 1 s.t.||A cs α-y|| 2 Epsilon is less than or equal to epsilon, wherein epsilon is a noise boundary, a weight coefficient alpha is obtained, and an original signal x is reconstructed from x psi alpha;
3d) when the peak value of the frequency spectrum of the reconstructed signal is equal to the peak value of the frequency spectrum of the original signal, outputting the reconstructed signal
3e) Finding out all peak values of reconstructed signal frequency spectrum and reserving the peak values in 0.8Hz-2Hz]Removing respiratory harmonic wave peak by using difference method, and counting frequency of corresponding frequency of peak value of reconstructed signal
Defining a frequency weight coefficient
k represents the occurrence frequency of the corresponding frequency of the reconstruction, and the heartbeat frequency is calculated as
Wherein f is i Is the peak frequency of each set of reconstructed signals, and the respiration rate is obtained by fast fourier transform.
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| CN114403820A (en) * | 2022-01-13 | 2022-04-29 | 武汉烽火凯卓科技有限公司 | Vital sign detection method and system for moving target |
| CN114601432A (en) * | 2022-01-28 | 2022-06-10 | 中国第一汽车股份有限公司 | Vehicle-mounted human body information acquisition method and device and vehicle |
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