K-band continuous wave fdDBF-based DCG detection method

Abstract

The application discloses a DCG detection method based on K-band continuous wave FDDBF, and the method comprises the following steps: transmitting electromagnetic waves to a detection object by a K-band continuous wave millimeter wave radar, receiving echo signals, reconstructing intermediate frequency signals obtained by mixing into virtual frequency domain arrays, extracting waveforms corresponding to different frequencies linearly by FDDBF, and obtaining pure DCG signals, differential DCG signals and chest movement signals through summation filtering.

Description

Technical Field

[0001] This invention relates to a technology in the field of medical testing, specifically a Doppler echocardiography (DCG) detection method based on K-band continuous wave frequency domain digital beamforming (FDDBF). Background Technology

[0002] Current methods primarily detect cardiac activity through electrocardiography (ECG), but its contact-based nature can cause discomfort during prolonged monitoring, making it unsuitable for routine use. Doppler echocardiography (DCG), a non-contact method that reflects the mechanical motion of the heart, holds great promise for medical applications. However, traditional techniques suffer from respiratory interference during DCG extraction; respiratory signals interfere with cardiac signals. Existing methods often employ digital filtering techniques to eliminate this interference, but nonlinear distortion remains a significant concern. Summary of the Invention

[0003] This invention addresses the breathing interference and signal distortion problems existing in current DCG detection technologies by proposing a DCG detection method based on K-band continuous wave FDBF. This method achieves linear and distortion-free extraction of DCG and differential DCG signals through K-band continuous wave millimeter waves, significantly improving the quality and accuracy of DCG signals.

[0004] This invention is achieved through the following technical solution:

[0005] This invention relates to a DCG detection method based on K-band continuous wave FDBF. After transmitting electromagnetic waves to the target object and receiving the echo signal using a K-band continuous wave millimeter-wave radar, the intermediate frequency signal obtained by mixing is reconstructed to obtain a virtual frequency domain array. Then, the waveforms corresponding to different frequencies are extracted linearly using FDBF. After summation and filtering, pure DCG signals, differential DCG signals, and chest cavity motion signals are obtained.

[0006] This invention relates to a system for implementing the above method, comprising: a displacement demodulation unit, a frequency domain array reconstruction unit, a frequency domain array processing unit, and a DCG extraction unit, wherein: the displacement demodulation unit performs phase demodulation processing on the CW intermediate frequency signal to obtain the motion of the thoracic cavity; the frequency domain array reconstruction unit performs group reconstruction based on the phase characteristics of the intermediate frequency signal to obtain an equivalent frequency domain array; the frequency domain array processing unit performs frequency domain beamforming and frequency domain digital beamforming processing based on the phase relationship between the frequency domain array elements to obtain the spectrum of the thoracic cavity motion and extract the time domain waveforms corresponding to the respiratory fundamental wave and harmonics; and the DCG extraction unit removes the obtained respiratory waveforms of different frequencies from the total motion to obtain a pure Doppler echocardiogram (DCG).

[0007] Technical effect

[0008] This invention utilizes the motion phase characteristics obtained by demodulating CW intermediate frequency signals to group and reconstruct displacement data, acquiring an equivalent frequency domain array. Frequency domain beamforming technology is then used to obtain the frequency distribution of the motion. Subsequently, frequency domain digital beamforming technology is employed to linearly acquire the time-domain waveforms corresponding to the respiratory fundamental and harmonics. Removing the respiratory component from the total motion yields the pure heartbeat motion. Compared to existing technologies, this invention can linearly and efficiently acquire the heartbeat displacement of a human target non-contactly using millimeter-wave radar. It avoids waveform distortion and subjective selection of cutoff frequencies caused by filter technology in existing technologies, as well as the computational complexity and subjective selection of decomposition parameters associated with VMD, CEEMDAN, and other similar techniques. Attached Figure Description

[0009] Figure 1 This is a flowchart of the present invention;

[0010] Figure 2 Flowchart of continuous wave frequency domain digital beamforming;

[0011] Figure 3 This is a simulation diagram of the present invention;

[0012] In the figure: (a) is the simulated scene, (b) is the original motion spectrum obtained by radar and the spectrum after processing by the present invention, (c) is the original motion time domain waveform obtained by radar, the breathing waveform obtained by the present invention and the extracted DCG time domain waveform, (d) is the DCG signal obtained after simulation processing and the simulated reference motion, and (e) is a comparison of DCG extraction results of different existing technologies.

[0013] Figure 4 This is an experimental diagram of the present invention;

[0014] In the figure: (a) shows the experimental scenario and system settings. (b) shows the original motion time-domain waveform acquired by radar, the breathing waveform acquired by this invention, and the extracted DCG time-domain waveform. (c) shows a comparison between the differential DCG signal obtained after processing by this invention and the reference ECG signal. Detailed Implementation

[0015] like Figure 1 As shown in the figure, this embodiment relates to a DCG detection method based on K-band continuous wave FDBF. After transmitting electromagnetic waves to the detection object and receiving the echo signal through a K-band continuous wave millimeter-wave radar, the intermediate frequency signal obtained by mixing is reconstructed to obtain a virtual frequency domain array. Then, the waveforms corresponding to different frequencies are extracted linearly through FDBF. After summation and filtering, pure DCG signals, differential DCG signals, and chest cavity motion signals are obtained.

[0016] The K-band continuous wave millimeter-wave radar includes: a monolithic microwave integrated unit (MMIC) and a microcontroller unit (MCU), a transmitting antenna (TX), and a receiving antenna (RX) connected thereto.

[0017] The microcontroller unit (MCU) includes a digital-to-analog converter (DAC), an analog-to-digital converter (ADC), and a DC adjustment unit.

[0018] like Figure 2 As shown, the intermediate frequency signal Where: A is the amplitude, λ is the wavelength, x(t) is the acquired thoracic cavity motion signal, d0 is the distance between the radar and the target, and Δθ(t) is the residual phase. The thoracic cavity motion signal is obtained by demodulating the intermediate frequency signal. K is the total number of harmonics, while A k f k and These represent the amplitude, frequency, and initial phase of each harmonic.

[0019] The aforementioned data reconstruction refers to: during the detection duration T, at a sampling rate f s After collecting a total of N+M-1 sampling points, they are reorganized into a virtual frequency domain array with M channels, each channel containing N snapshots.

[0020] Among them: A(f)=[a(f1),a(f2),…,a(f K )] is the array manifold vector, a(f k )=[1,exp(j2πf k t s ),…,exp(j2πf k (M-1)t s )] T As the guiding vector, X(t) = [x1(t), x2(t), ..., x K (t)] T For the corresponding signal vector, To reconstruct the motion signals at different frequencies after grouping, N(t) is additive white Gaussian noise.

[0021] The spectral distribution S obtained by frequency domain spatial beam scanning of Sarray CBF (f i )=l(f i S[m,n]S H [m,n]l H (f i ),in: i is the frequency index, S and S ArrayThey are the same matrix, and the superscript H indicates the conjugate transpose of this matrix.

[0022] Identifying the basic respiratory rate f by analyzing the frequency domain of the signal. k And its harmonics. It is worth noting that respiratory harmonics with frequencies higher than the heart rate are generally weaker and have less impact on DCG, so they can be ignored. Furthermore, a steering vector l(pf) corresponding to the respiratory frequency is constructed using the FDBF technique. k These steering vectors are then directed to the corresponding frequency regions to extract the time-domain waveform associated with each frequency.

[0023] The frequency domain array processing unit acquires time-domain signals of different respiratory frequency components, and performs frequency domain digital beamforming (FDDBF), i.e., from the frequency domain array S Array Extract the time-domain waveform related to each frequency, specifically: Wherein: the time-domain displacement, i.e., the time-domain waveform, corresponds to different respiratory frequencies. p is the index of the basic respiratory rate and its harmonics, p = 1, 2, ..., P.

[0024] The pure DCG signal mentioned above refers to: Where: x(t) is the chest cavity motion signal; D-DCG(t) = diff(DCG(t)), where: D represents the difference of DCG, DCG is the heartbeat motion, i.e., Doppler echocardiography, x(t) is the acquired chest cavity motion signal, and D-DCG is the differential DCG.

[0025] Through specific practical experiments, such as... Figure 3 (a) shows a high-precision slide stage to simulate human breathing and heartbeat. The radar unit receives the signals and processes them using the present invention to obtain high linearity and high precision DCG and differential DCG signals.

[0026] The slide stage was configured to reproduce respiratory and cardiac motion with high precision (1 μm). Respiration was modeled using multiple combinations of sine waves, including the fundamental frequency to the third harmonic. Since an accurate model of cardiac motion was not yet available, a DCG signal pre-recorded by a subject during breath-holding was used. This signal represented a cardiac cycle and was periodically extended to simulate the motion caused by the heartbeat.

[0027] like Figure 3 (b) shows the identified respiratory frequencies as indicated by the dashed lines. FDBF is applied to extract the waveforms corresponding to these frequency components. By summing these components, the overall respiratory signal is reconstructed with high linearity, as shown below. Figure 3 (c) shows the dashed line. The overall motion signal is processed to remove the respiratory component, resulting in a pure DCG, as shown below. Figure 3(c) and Figure 3 As shown by the short dashed line in (d). Figure 3 The solid line in (b) shows the spectrum of the signal, indicating that the respiratory component was effectively removed, leaving only the heart rate. Some of the data is shown in Table 1.

[0028] Table 1

[0029]

[0030] like Figure 3 As shown in (e), this invention is compared with the prior art in a horizontal manner, wherein the correlation coefficient of the invention is significantly better than the average level of the prior art.

[0031] like Figure 4 The image shows a human field experiment, conducted on a person sitting in front of the radar. Radar measurements and synchronized ECG readings were performed simultaneously, with the ECG serving as a reference signal; as shown... Figure 4 (b) The solid line represents the demodulated motion signal acquired from the radar; the present invention extracts, as shown in the figure... Figure 4 (b) shows the respiratory components indicated by the dashed line and obtains the following: Figure 4 (b) The clean DCG signal shown by the short dashed line. Furthermore, the D-DCG signal was extracted and compared with the ECG data, as shown below. Figure 4 As shown in (c), some of its data are shown in Table 2.

[0032] Table 2

[0033]

[0034] The comparison results show significant consistency in the RR gap, with a root mean square error (RMSE) of 6.82 ms. These results validate that the technique can achieve high-precision, linear, and distortion-free pure DCG and differential DCG reconstruction, highlighting its significant potential in radar-based medical applications.

[0035] Compared with existing technologies, this invention utilizes the phase characteristics of intermediate frequency (IF) signals to reconstruct a virtual frequency domain array. Traditional beamforming techniques are then used to estimate the spectrum of the motion-based frequency domain array to obtain the frequencies corresponding to the motion components. Frequency domain digital beamforming technology is used to guide the frequency space of the respiratory fundamental and harmonics, and time-domain waveforms corresponding to different frequency spaces are extracted, ultimately achieving the extraction of DCG and differential DCG signals. Compared with traditional technologies, this technology features high efficiency, robustness, distortion-free operation, and linearity, making it particularly significant in non-contact vital sign monitoring applications.

[0036] The above-described specific implementations can be partially adjusted by those skilled in the art in different ways without departing from the principles and purpose of the present invention. The scope of protection of the present invention is defined by the claims and is not limited to the above-described specific implementations. All implementation schemes within the scope of the claims are bound by the present invention.

Claims

1. A DCG detection method based on K-band continuous wave frequency domain digital beamforming, characterized in that, After transmitting electromagnetic waves to the target object and receiving the echo signal through K-band continuous wave millimeter-wave radar, the intermediate frequency signal obtained by mixing is reconstructed to obtain a virtual frequency domain array. Then, the waveforms corresponding to different frequencies are extracted linearly through frequency domain digital beamforming. After summation and filtering, a pure DCG signal and a chest cavity motion signal are obtained. The aforementioned data reconstruction refers to: during the detection duration T, at a sampling rate f s After collecting a total of N+M-1 sampling points, they are reorganized into a virtual frequency domain array with M channels, each channel containing N snapshots. , where: A(f) = [a(f1), a(f2),…,a(f K )] is the array manifold vector, a(f k ) = [1, exp(j2πf k t s ),…, exp(j2πf k (M-1)t s )] T As the guiding vector, X(t) = [x1(t), x2(t),…, x K (t)] T For the corresponding signal vector, To reconstruct the motion signals at different frequencies after grouping, N(t) is additive white Gaussian noise; For S Array Spectral distribution obtained by frequency domain spatial beam scanning , i is the frequency index, S and S Array They are the same matrix, and the superscript H indicates the conjugate transpose of this matrix; The aforementioned frequency domain digital beamforming refers to: from the frequency domain array S Array Extract the time-domain waveform related to each frequency, specifically: Wherein: the time-domain displacement, i.e., the time-domain waveform, corresponds to different respiratory frequencies. p is the index of the basic respiratory rate and its harmonics. ; The pure DCG signal mentioned above refers to: , where X(t) is the thoracic motion signal.

2. The DCG detection method based on K-band continuous wave frequency domain digital beamforming according to claim 1, characterized in that, The K-band continuous wave millimeter-wave radar includes: a monolithic microwave integrated unit (MMIC) and a microcontroller unit (MCU), a transmitting antenna (TX), and a receiving antenna (RX) connected thereto. The microcontroller unit (MCU) includes a digital-to-analog converter (DAC), an analog-to-digital converter (ADC), and a DC adjustment unit.

3. The DCG detection method based on K-band continuous wave frequency domain digital beamforming according to claim 1, characterized in that, The intermediate frequency signal Where: A is the amplitude, λ is the wavelength, x(t) is the acquired thoracic cavity motion signal, d0 is the distance between the radar and the target, and Δθ(t) is the residual phase. The thoracic cavity motion signal is obtained by demodulating the intermediate frequency signal. K is the total number of harmonics, while A k f k and These represent the amplitude, frequency, and initial phase of each harmonic.

4. A DCG detection system for implementing the method of any one of claims 1-3, characterized in that, include: The system comprises a displacement demodulation unit, a frequency domain array reconstruction unit, a frequency domain array processing unit, and a DCG extraction unit. Specifically: the displacement demodulation unit performs phase demodulation processing on the CW intermediate frequency signal to obtain the thoracic cavity motion; the frequency domain array reconstruction unit performs group reconstruction based on the phase characteristics of the intermediate frequency signal to obtain a virtual frequency domain array; the frequency domain array processing unit performs frequency domain beamforming and frequency domain digital beamforming processing based on the phase relationship between the frequency domain array elements to obtain the spectrum of the thoracic cavity motion and extract the time-domain waveforms corresponding to the respiratory fundamental and harmonics; and the DCG extraction unit removes the acquired respiratory waveforms of different frequencies from the thoracic cavity motion signal to obtain a clean Doppler echocardiogram (DCG).

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