CN113397533B - Weak life signal extraction method and device, electronic equipment and storage medium - Google Patents
Weak life signal extraction method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN113397533B CN113397533B CN202110723144.XA CN202110723144A CN113397533B CN 113397533 B CN113397533 B CN 113397533B CN 202110723144 A CN202110723144 A CN 202110723144A CN 113397533 B CN113397533 B CN 113397533B
- Authority
- CN
- China
- Prior art keywords
- signal
- signals
- time domain
- domain echo
- frequency
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/0816—Measuring devices for examining respiratory frequency
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Physiology (AREA)
- Artificial Intelligence (AREA)
- Signal Processing (AREA)
- Psychiatry (AREA)
- Pulmonology (AREA)
- Computational Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Dentistry (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
Abstract
The present disclosure provides a weak vital signal extraction method, the vital signal comprising a respiratory signal, the method comprising: acquiring a life signal, wherein the life signal is a time domain echo signal; preprocessing the time domain echo signals, and performing Fourier transform on the preprocessed time domain echo signals to obtain pure time domain echo signals; intercepting signals in a preset frequency range from the pure time domain echo signals to obtain time-frequency signals, wherein the time-frequency signals comprise noise signals and breathing signals; the relation among the time-frequency signal, the breathing signal and the noise signal is represented by a convex optimization problem form, and a signal relation formula is obtained; a respiration signal is determined from the signal relationship using an alternating direction method. According to the weak vital signal extraction method, the vital signal is captured by solving the low-rank component of the time-frequency signal through the robust principal component analysis method, so that the output signal-to-noise ratio of the vital signal can be improved, and the weak vital signal extraction method is possible under the condition of low signal-to-noise ratio.
Description
Technical Field
The disclosure relates to the field of through-wall human vital signal detection, in particular to a weak vital signal extraction method, a weak vital signal extraction device, electronic equipment and a storage medium.
Background
In earthquake rescue (or wall-penetrating) life detection, chest vibration amplitude caused by human breathing is weak, and the signals are greatly attenuated by penetrating through a wall body, so that the signal-to-noise ratio of human life echo signals is low. In addition, ambient noise and radar instability also reduce the output signal-to-noise ratio of the vital signal. The traditional vital signal extraction method is fourier transform (Fast Fourier Transform, FFT), but it is difficult to eliminate the influence of environmental noise. Scholars have proposed analysis of the time-frequency characteristics of respiratory signals by FFT methods and Hilbert Huang transform. This approach effectively improves SNR but has greater computational complexity. Furthermore, scholars have developed a treatment for breath detection under low SNR conditions. The main step is to extract the respiratory signal from the noisy time-frequency signal by principal component analysis (Singular Value Decomposition, SVD). SVD assumes that the respiratory signal is concentrated in larger singular values, but some noise remains in these singular values, which will result in noise in the same frequency band as the respiratory signal still being present. It is therefore necessary to develop a method for extracting human vital signals under the condition of low signal-to-noise ratio.
BRIEF SUMMARY OF THE PRESENT DISCLOSURE
First, the technical problem to be solved
In view of the foregoing deficiencies of the prior art, a primary object of the present disclosure is to provide a weak vital signal extraction method, apparatus, electronic device and storage medium, with the aim of at least partially solving at least one of the above-mentioned technical problems.
(II) technical scheme
In order to achieve the above object, according to one aspect of the present disclosure, there is provided a weak vital signal extraction method including:
collecting a life signal, wherein the life signal is a time domain echo signal;
intercepting signals in a preset frequency range from the time domain echo signals to obtain time-frequency signals, wherein the time-frequency signals comprise noise signals and breathing signals;
the relation among the time-frequency signal, the respiratory signal and the noise signal is represented in a convex optimization problem form, so that a signal relation formula is obtained;
the respiration signal is determined from the signal relation by means of an alternating direction method.
In some embodiments, p points are sampled in fast time, q points are sampled in slow time, and a p×q time domain echo signal matrix is obtained;
the time domain echo signal is expressed as:
x e (p，q)＝s(p，q)+h(p，q)+n(p，q)；
wherein x is e (p, q) represents the time domain echo signal, s (p, q) represents the respiratory signal, h (p, q) represents the reflected waveform of a stationary target in the sampling environment, and n (p, q) represents clutter in the sampling environment.
In some embodiments, preprocessing the time-domain echo signals, and performing fourier transform on the preprocessed time-domain echo signals to obtain pure time-domain echo signals;
the clean time domain echo signal is expressed as:
X(p，k)＝S(p，k)+N(p，k)；
where X (p, K) represents a clean time domain echo signal, S (p, K) represents a clean respiratory signal after fourier transformation, N (p, K) represents a clean noise signal after fourier transformation, K is an index of the frequency dimension of the clean respiratory signal S (p, K), k=1, 2, …, K.
In some embodiments, the relationship between the time-frequency signal, the respiratory signal, and the noise signal is expressed in the form of a convex optimization problem, resulting in a signal relationship:
min S，N ‖S‖ * +λ‖N‖ 1 s.t.‖X-S-N‖ F ≤ε；
wherein II * Is the kernel norm, |II 1 Is 1 norm, λ is used to trade-off +| * And II 1 Parameter II F Is a French Luo Beini Usne norm, representing the reconstruction error, ε representing a preset iteration stop condition.
In some embodiments, before the respiration signal is obtained by using the alternating direction method, the method further includes:
the signal relation is converted by using a minimized extended Lagrangian function to obtain the following formula:
where Y is the Lagrangian multiplier, β is a positive penalty parameter, and < · > represents the standard inner product.
In some embodiments, the determining the respiratory signal by using an alternating direction method specifically includes:
solving the above using singular value decomposition
Fixing the noise signal N (p, k), the respiration signal S (p, k) is expressed as:
solving using singular value decomposition
Fixing the respiratory signal S (p, k), the noise signal N (p, k) is expressed as:
updating the Lagrangian multiplier Y through residual errors X-S-N;
up to the noise signal N p+1 And the respiratory signal S p+1 When the preset condition is satisfied, the noise signal N is output p+1 And the respiratory signal S p+1 ；
Wherein U sigma V T Is X-N-beta -1 Singular value decomposition of Y;
X∈X p×k ，N 0 ＝Y 0 ＝0,
β＝0.25/‖X‖ 1 ；
the preset conditions are as follows: II X-S p+1 -N p+1 ‖ F /‖X‖ F And epsilon is less than or equal to epsilon, wherein epsilon represents a preset iteration stop condition.
In some embodiments, the preset frequency range includes an actual respiratory frequency range of the human body;
and intercepting signals including the actual respiratory frequency range of the human body from the pure time domain echo signals to obtain time-frequency signals.
In another aspect, the present disclosure also provides a weak vital signal extraction apparatus, the apparatus comprising:
the acquisition module is used for acquiring life signals, wherein the life signals are time domain echo signals;
the preprocessing module is used for preprocessing the time domain echo signals and carrying out Fourier transform on the preprocessed time domain echo signals to obtain pure time domain echo signals;
the intercepting module is used for intercepting signals in a preset frequency range from the time domain echo signals to obtain time-frequency signals, wherein the time-frequency signals comprise noise signals and breathing signals;
the relation module is used for expressing the relation among the time-frequency signal, the breathing signal and the noise signal in the form of convex optimization problem to obtain a signal relation formula;
and the extraction module is used for obtaining the respiratory signal from the signal relation by adopting an alternating direction method.
In another aspect, the present disclosure further provides an electronic device, including:
a communicator for communicating with the server;
a processor;
and a memory storing a computer executable program which, when executed by the processor, causes the processor to execute the weak vital signal extraction method described above.
In another aspect, the present disclosure also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a weak vital signal extraction method as described above.
(III) beneficial effects
According to the weak vital signal extraction method, the vital signal is captured by solving the low-rank component of the time-frequency signal through the robust principal component analysis method, so that the output signal-to-noise ratio of the vital signal can be improved, and the weak vital signal extraction method is possible under the condition of low signal-to-noise ratio.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 schematically illustrates a flowchart of a weak vital signal extraction method according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a time-frequency plot of a vital signal after a conventional Fourier transform process provided by an embodiment of the present disclosure;
FIG. 3 schematically illustrates a time-frequency diagram of a vital signal after being processed by a singular value decomposition method according to an embodiment of the present disclosure;
fig. 4 schematically illustrates a time-frequency diagram of a vital signal processed by a robust principal component analysis method according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates output signal-to-noise ratios provided by an embodiment of the present disclosure employing conventional Fourier transforms, singular value decomposition, and robust principal component analysis at different signal-to-noise ratios;
FIG. 6 schematically illustrates a simulated scene graph of a weak vital signal extraction method according to an embodiment of the disclosure;
fig. 7 schematically illustrates a structural diagram of a weak vital signal extraction device according to an embodiment of the present disclosure;
fig. 8 schematically shows a hardware configuration diagram of an electronic device.
Detailed Description
For a better understanding of the objects, features, aspects and advantages of the present disclosure, reference is made to the following detailed description of specific embodiments, which is to be taken in conjunction with the accompanying drawings, it being apparent that the embodiments described are only some, but not all, of the embodiments of the disclosure. All other embodiments, which can be made by those skilled in the art without the inventive effort, are intended to be within the scope of the present disclosure, based on the embodiments herein.
Fig. 1 schematically illustrates a flowchart of a weak vital signal extraction method according to an embodiment of the present disclosure, as shown in fig. 1, in an embodiment of the present disclosure, the method includes:
s101, acquiring a life signal, wherein the life signal is a time domain echo signal.
S102, preprocessing the time domain echo signals, and performing Fourier transform on the preprocessed time domain echo signals to obtain pure time domain echo signals.
S103, intercepting signals in a preset frequency range from the pure time domain echo signals to obtain time-frequency signals, wherein the time-frequency signals comprise noise signals and breathing signals.
And S104, expressing the relation among the time-frequency signal, the respiratory signal and the noise signal in the form of convex optimization problem, and obtaining a signal relation formula.
S105, obtaining the respiration signal from the signal relation by adopting an alternating direction method.
In this embodiment, a time domain echo signal is collected, where the time domain echo signal includes a respiratory signal, a noise signal and some clutter, in order to reduce the amount of processed data, the collected time domain echo signal is preprocessed, then a part of signals in a preset frequency range are extracted from the preprocessed time domain echo signal, and then the obtained part of signals are processed to obtain the respiratory signal.
In this embodiment, preprocessing is performed on the acquired time domain echo signal to remove a background target of the time domain echo signal, inhibit temperature drift caused by a radar system, and eliminate high-frequency clutter, so as to obtain a preprocessed time domain echo signal, where the specific steps of preprocessing the time domain echo signal include: (1) Removing reflected waves of the fixed environment using adaptive background subtraction; (2) Compensating for linear trend of temperature induced in slow time dimension with linear trend suppression; (3) Filtering high-frequency noise caused by filtering and unnecessary low-frequency signals by adopting a distance filter; (4) The self-adaptive normalization processing can enhance the signal-to-noise ratio of the weak target response through the step of segmentation normalization; and carrying out Fourier transform on the preprocessed time domain echo signals along slow time, and intercepting clean time domain echo signals.
In an embodiment of the disclosure, p points are sampled in fast time, q points are sampled in slow time, and a p×q time domain echo signal matrix is obtained; the time domain echo signal is expressed as: x is x e (p, q) =s (p, q) +h (p, q) +n (p, q); wherein x is e (p, q) represents the time domain echo signal, s (p, q) represents the respiratory signal, h (p, q) represents the reflected waveform of a stationary target in the sampling environment, and n (p, q) represents clutter in the sampling environment.
In this embodiment, the ultra wideband radar is used to acquire the time domain echo signals, and the echo model of the time domain UWB radar can be expressed as x e (p, q) =s (p, q) +h (p, q) +n (p, q), wherein x e (P, q) is data received by the radar, s (P, q) represents human breathing signals, h (P, q) is a reflected waveform of other fixed targets, n (P, q) is clutter in the environment, p=1, 2, …, P; q=1, 2, …, Q.
In an embodiment of the disclosure, the clean time domain echo signal is expressed as: x (p, K) =s (p, K) +n (p, K), where X (p, K) represents a clean time domain echo signal, S (p, K) represents a clean respiratory signal after fourier transformation, N (p, K) represents a clean noise signal after fourier transformation, K is an index of the frequency dimension of the clean respiratory signal S (p, K), k=1, 2, …, K.
In this embodiment, the background has been eliminated after the pre-processed time domain echo signal has been subjected to a slow time windowed fourier transform. The clean time-domain echo signal X (p, k) consists of a respiration signal S (p, k) and a noise signal N (p, k).
In an embodiment of the present disclosure, the relationship among the time-frequency signal, the respiratory signal, and the noise signal is expressed in the form of a convex optimization problem, so as to obtain a signal relationship formula: min S，N ‖S‖ * +λ‖N‖ 1 s.t.‖X-S-N‖ F Less than or equal to epsilon, wherein II * Is the kernel norm, |II 1 Is 1 norm, λ is used to trade-off +| * And II 1 Parameter II F Is a French Luo Beini Usne norm, representing the reconstruction error, ε representing a preset iteration stop condition.
In the present embodiment, S (p, k) is regarded as a low-rank component and N (p, k) is regarded as a sparse component, and therefore, the signal relation between the time-frequency signal, the respiratory signal, and the noise signal is: min S，N ‖S‖ * +λ‖N‖ 1 s.t.‖X-S-N‖ F And less than or equal to epsilon, estimating a low-rank matrix S (p, k) from the matrix X (p, k) by adopting a robust principal component analysis method, namely extracting a respiration signal S (p, k) from the time-frequency signal X (p, k).
In an embodiment of the disclosure, before the respiration signal is obtained by using the alternating direction method, the method further includes: the signal relation is converted by using a minimized extended Lagrangian function to obtain the following formula:
where Y is the Lagrangian multiplier, beta is a positive penalty parameter,<·>representing the standard inner product.
In an embodiment of the disclosure, the obtaining the respiratory signal by using an alternating direction method specifically includes: solving the above using singular value decomposition
Fixing the noise signal N (p, k), the respiration signal S (p, k) is expressed as: />
Solving for +.Using singular value decomposition method>
Fixing the respiratory signal S (p, k), the noise signal N (p, k) is expressed as: />
By residual errorX-S-N updates the Lagrangian multiplier Y; up to the noise signal N p+1 And the respiratory signal S p+1 When the preset condition is satisfied, the noise signal N is output p+1 And the respiratory signal S p+1 The method comprises the steps of carrying out a first treatment on the surface of the Wherein U sigma V T Is X-N-beta -1 Singular value decomposition of Y;
X∈X p×k ，N 0 ＝Y 0 ＝0,/>
β＝0.25/‖X‖ 1 the method comprises the steps of carrying out a first treatment on the surface of the The preset conditions are as follows: II X-S p+1 -N p+1 ‖ F /‖X‖ F And epsilon is less than or equal to epsilon, wherein epsilon represents a preset iteration stop condition.
In the present embodiment, parameters are set and adjusted according to the experimental data results
And β=0.25 i X i 1 Wherein P and K are maximum index values of the fast time and respiratory frequency dimensions in the time-frequency signal X (P, K), respectively, ε is 10 -4 。
It should be noted that epsilon represents a preset iteration stop condition, and epsilon is 10 -4 Merely by way of example, to assist those skilled in the art in understanding the technical content of the present disclosure, but it is not meant that epsilon of the present disclosure be merely a value in the above-described embodiments.
In an embodiment of the disclosure, the preset frequency range includes an actual respiratory frequency range of a human body; and intercepting signals including the actual respiratory frequency range of the human body from the pure time domain echo signals to obtain time-frequency signals.
In this embodiment, the preset frequency range includes an actual respiratory frequency range of the human body, or a frequency range when the frequency is intercepted may be set according to an actual requirement, and the specific frequency value is not limited herein.
Fig. 2 schematically illustrates a time-frequency diagram of a vital signal processed by a conventional fourier transform provided by an embodiment of the present disclosure, fig. 3 schematically illustrates a time-frequency diagram of a vital signal processed by a singular value decomposition method provided by an embodiment of the present disclosure, fig. 4 schematically illustrates a time-frequency diagram of a vital signal processed by a robust principal component analysis method provided by an embodiment of the present disclosure, and fig. 2-4 illustrate, in an embodiment of the present disclosure, the fig. 2-4 represent the same signal-to-noise ratio, the conventional fourier transform, the singular value decomposition method enhancement, and the robust principal component analysis method based vital detection result provided by the present disclosure, in the conventional fourier transform method, the surrounding of the vital signal is filled with noise, the noise is suppressed after the singular value decomposition method enhancement, but the noise in the same frequency band as the vital signal still exists, and the noise is well eliminated after the robust principal component analysis method based weak vital signal enhancement method provided by the present disclosure is processed, and the vital signal is clearly visible.
Fig. 5 schematically illustrates output signal-to-noise ratios provided by an embodiment of the present disclosure when a conventional fourier transform, a singular value decomposition method, and a robust principal component analysis method are adopted under different signal-to-noise ratios, as shown in fig. 5, in an embodiment of the present disclosure, the output signal-to-noise ratios after processing by the conventional fourier transform, the singular value decomposition method, and the robust principal component analysis method provided by the present disclosure are at least 12dB higher than the fourier transform method and 10dB higher than the singular value decomposition method.
Fig. 6 schematically illustrates a simulation scene diagram of a weak vital signal extraction method according to an embodiment of the present disclosure, as shown in fig. 6, in an embodiment of the present disclosure, a Radar (Radar) is used, a center frequency is 500MHz, a Wall (Wall) thickness is 24cm, a human Breathing model (Breathing model) is simulated by a cylinder, a radius of the cylinder is modulated by a cosine signal with a frequency of 0.18Hz, and a distance between the center of the cylinder and the Radar is 2.2m.
It should be noted that the above radar center frequency, wall thickness, cylinder center and radar interval are only examples to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the radar center frequency, wall thickness, cylinder center and radar interval of the present disclosure are only values in the above embodiments.
Fig. 7 schematically illustrates a structural diagram of a weak vital signal extraction apparatus according to an embodiment of the present disclosure, as shown in fig. 7, the apparatus includes: the system comprises an acquisition module 710, a preprocessing module 720, an interception module 730, a relation module 740 and an extraction module 750.
The acquisition module 710 is configured to acquire a life signal, where the life signal is a time domain echo signal;
the preprocessing module 720 is configured to preprocess the time domain echo signal, and perform fourier transform on the preprocessed time domain echo signal to obtain a pure time domain echo signal;
the intercepting module 730 is configured to intercept signals within a preset frequency range from the time domain echo signal to obtain a time-frequency signal, where the time-frequency signal includes a noise signal and a respiratory signal;
a relation module 740, configured to represent the relation among the time-frequency signal, the respiratory signal, and the noise signal in the form of a convex optimization problem, so as to obtain a signal relation formula;
the extracting module 750 is configured to obtain the respiration signal from the signal relation by using an alternating direction method.
The present disclosure also provides an electronic device 800 comprising:
a communicator 810 for communicating with a server;
a processor 820;
a memory 830 storing a computer executable program that, when executed by the processor, causes the processor to perform a weak vital signal extraction method as described above.
Fig. 8 schematically shows a hardware configuration of an electronic device, and as shown in fig. 8, the electronic device 800 includes a communicator 810, a processor 820 and a memory 830. The electronic device 800 may perform methods according to embodiments of the present disclosure.
In particular, processor 820 may include, for example, a general purpose microprocessor, an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 820 may also include on-board memory for caching purposes. Processor 820 may be a single processing unit or multiple processing units for performing different actions of the method flows according to embodiments of the disclosure.
The present disclosure also provides a computer-readable storage medium having stored thereon a computer program comprising a weak vital signal extraction method as described above. The computer-readable storage medium may be embodied in the apparatus/device described in the above embodiments; or may exist alone without being assembled into the apparatus/device. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, fiber optic cable, radio frequency signals, or the like, or any suitable combination of the foregoing.
While the foregoing embodiments have been described in some detail to illustrate the purposes, aspects and advantages of the present disclosure, it should be understood that the foregoing embodiments are merely illustrative of the present disclosure and are not limiting, and that various combinations and/or modifications of the various embodiments and/or features set forth in the claims, even though not explicitly recited in the disclosure, are intended to be within the spirit and principles of the disclosure.
Claims (6)
1. A weak vital signal extraction method, comprising:
acquiring a life signal, wherein the life signal is a time domain echo signal;
preprocessing the time domain echo signals, and performing Fourier transform on the preprocessed time domain echo signals to obtain pure time domain echo signals, wherein the pure time domain echo signals are expressed as:
X(p，k)＝S(p，k)+N(p，k)；
wherein X (p, K) represents a clean time domain echo signal, S (p, K) represents a clean respiratory signal after fourier transform, N (p, K) represents a clean noise signal after fourier transform, K is an index of the clean respiratory signal S (p, K) in frequency dimension, k=1, 2.
Intercepting signals in a preset frequency range from the pure time domain echo signals to obtain time-frequency signals, wherein the time-frequency signals comprise noise signals and breathing signals;
and expressing the relation among the time-frequency signal, the respiratory signal and the noise signal in the form of convex optimization problem to obtain a signal relation formula:
wherein I II * Is the core norm of the number of kernels, I.I 1 Is a 1-norm, λ is used to trade-off |·| * And|| | 1 Is used for the control of the temperature of the liquid crystal display device, I.I F Is a Fu Luo Beini Usnea norm, represents a reconstruction error, and epsilon represents a preset iteration stop condition;
and converting the signal relation by adopting a minimized extended Lagrangian function to obtain the following formula:
wherein Y is Lagrangian multiplier, beta is positive penalty parameter, and < DEG > represents standard inner product;
obtaining the respiratory signal from the signal relation by adopting an alternating direction method comprises the following steps: solving the min using singular value decomposition S ，
Fixing the noise signal N (p, k), the respiration signal S (p, k) being expressed as:
solving using singular value decomposition
-fixing the respiratory signal S (p, k), the noise signal N (p, k) being expressed as:
updating the Lagrangian multiplier Y through residual errors X-S-N;
up to the noise signal N p+1 And the respiratory signal S p+1 When the preset condition is satisfied, outputting the noise signal N p+1 And the respiratory signal S p+1 ；
Wherein U sigma V T Is X-N-beta -1 Singular value decomposition of Y;
X∈X p×k ，N 0 ＝Y 0 ＝0，
β＝0.25/||X|| 1 ；
the preset conditions are as follows: X-S p+1 -N p+1 || F /||X|| F And epsilon is less than or equal to epsilon, wherein epsilon represents a preset iteration stop condition.
2. The weak vital sign extraction method of claim 1,
sampling p points in fast time and q points in slow time to obtain a p multiplied by q time domain echo signal matrix;
the time domain echo signal is expressed as:
x e (p，q)＝s(p，q)+h(p，q)+n(p，q)；
wherein x is e (p, q) represents the time domain echo signal, s (p, q) represents the respiratory signal, h (p, q) represents the reflected waveform of a stationary target in the sampling environment, and n (p, q) represents clutter in the sampling environment.
3. The weak vital sign extraction method of claim 1,
the preset frequency range comprises an actual human respiratory frequency range;
and intercepting signals including the actual respiratory frequency range of the human body from the pure time domain echo signals to obtain time-frequency signals.
4. A weak vital signal extraction apparatus, comprising:
the acquisition module is used for acquiring a life signal, wherein the life signal is a time domain echo signal;
the preprocessing module is used for preprocessing the time domain echo signals, carrying out Fourier transform on the preprocessed time domain echo signals to obtain pure time domain echo signals, and the pure time domain echo signals are expressed as:
X(p，k)＝S(p，k)+N(p，k)；
wherein X (p, K) represents a clean time domain echo signal, S (p, K) represents a clean respiratory signal after fourier transform, N (p, K) represents a clean noise signal after fourier transform, K is an index of the clean respiratory signal S (p, K) in frequency dimension, k=1, 2.
The intercepting module is used for intercepting signals in a preset frequency range from the time domain echo signals to obtain time-frequency signals, wherein the time-frequency signals comprise noise signals and breathing signals;
the relation module is used for expressing the relation among the time-frequency signal, the respiratory signal and the noise signal in the form of convex optimization problem to obtain a signal relation formula:
wherein I II * Is the core norm of the number of kernels, I.I 1 Is a 1-norm, λ is used to trade-off |·| * And|| | 1 Is used for the control of the temperature of the liquid crystal display device, I.I F Is a Fu Luo Beini Usnea norm, represents a reconstruction error, and epsilon represents a preset iteration stop condition;
the extraction module is used for obtaining the respiratory signal from the signal relation by adopting an alternating direction method, and specifically comprises the following steps:
and converting the signal relation by adopting a minimized extended Lagrangian function to obtain the following formula:
wherein Y is Lagrangian multiplier, beta is positive penalty parameter, and < DEG > represents standard inner product;
solving the min using singular value decomposition S ，
Fixing the noise signal N (p, k), the respiration signal S (p, k) being expressed as:
solving using singular value decomposition
-fixing the respiratory signal S (p, k), the noise signal N (p, k) being expressed as:
updating the Lagrangian multiplier Y through residual errors X-S-N;
up to the noise signal N p+1 And the respiratory signal S p+1 When the preset condition is satisfied, outputting the noise signal N p+1 And the respiratory signal S p+1 ；
Wherein U sigma V T Is X-N-beta -1 Singular value decomposition of Y;
the preset conditions are as follows: X-S p+1 -N p+1 || F /||X|| F And epsilon is less than or equal to epsilon, wherein epsilon represents a preset iteration stop condition.
5. An electronic device, the electronic device comprising:
a communicator for communicating with the server;
a processor;
a memory storing a computer executable program that, when executed by the processor, causes the processor to perform the weak vital signal extraction method of any of claims 1-3.
6. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the weak vital signal extraction method according to any of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110723144.XA CN113397533B (en) | 2021-06-28 | 2021-06-28 | Weak life signal extraction method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110723144.XA CN113397533B (en) | 2021-06-28 | 2021-06-28 | Weak life signal extraction method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113397533A CN113397533A (en) | 2021-09-17 |
| CN113397533B true CN113397533B (en) | 2023-06-20 |
Family
ID=77679956
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110723144.XA Active CN113397533B (en) | 2021-06-28 | 2021-06-28 | Weak life signal extraction method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113397533B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117970322B (en) * | 2024-04-02 | 2024-06-11 | 中国科学院空天信息创新研究院 | Low-rank block sparse decomposition-based vital signal enhancement method |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10735298B2 (en) * | 2012-12-05 | 2020-08-04 | Origin Wireless, Inc. | Method, apparatus, server and system for vital sign detection and monitoring |
| WO2016058248A1 (en) * | 2014-10-15 | 2016-04-21 | 国家电网公司 | Bi-linearity robust estimation method based on bi-linearity convex optimization theory for electric power system |
| JP6709116B2 (en) * | 2016-06-27 | 2020-06-10 | 公立大学法人会津大学 | Respiration detection device, respiration detection method, and respiration detection program |
| CN110192850A (en) * | 2019-05-31 | 2019-09-03 | 湖南省顺鸿智能科技有限公司 | Extracting method and system based on heartbeat signal under radar return strong noise background |
| CN110507293B (en) * | 2019-07-26 | 2022-01-07 | 中国电子科技集团公司第三十八研究所 | Ultra-wideband through-wall radar human respiration and heartbeat detection method and system |
| CN112137620B (en) * | 2020-08-27 | 2021-06-11 | 广东省地震局 | Ultra-wideband radar-based human body weak respiration signal detection method |
-
2021
- 2021-06-28 CN CN202110723144.XA patent/CN113397533B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113397533A (en) | 2021-09-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | De-noising low-frequency magnetotelluric data using mathematical morphology filtering and sparse representation | |
| Sun et al. | Cross-correlation analysis and time delay estimation of a homologous micro-seismic signal based on the Hilbert–Huang transform | |
| Li et al. | Dictionary learning and shift-invariant sparse coding denoising for controlled-source electromagnetic data combined with complementary ensemble empirical mode decomposition | |
| CN103454622B (en) | Broadband radar target complex echo de-noising method based on sparseness constraints | |
| CN110448290B (en) | Terahertz through-wall radar-based remote personnel heart rate detection method, device and system | |
| CN103529436A (en) | Method for carrying out separation and time-frequency analysis on respiration and heartbeat signals in non-contact life detection on basis of HHT (Hilbert Huang Transform) | |
| WO2022057305A1 (en) | Signal processing method and apparatus, terminal device and storage medium | |
| CN103190898A (en) | Cardiac magnetic signal noise adaptive filtering and eliminating design method | |
| CN113397533B (en) | Weak life signal extraction method and device, electronic equipment and storage medium | |
| CN105182429A (en) | Marine controlled-source electromagnetic data seawater disturbance noise correction method | |
| CN110515063A (en) | Underwater acoustic signal processing method and apparatus based on the steady wavelet transform of iteration | |
| CN117574062A (en) | Small loop transient electromagnetic signal denoising method based on VMD-DNN model | |
| Wang et al. | De-noising magnetotelluric data using variational mode decomposition combined with mathematical morphology filtering and wavelet thresholding | |
| CN116610930A (en) | Weak vital signal enhancement algorithm based on non-convex regularization | |
| Zhang et al. | A reverberation noise suppression method of sonar image based on shearlet transform | |
| Tian et al. | Underwater multi-target passive detection based on transient signals using adaptive empirical mode decomposition | |
| CN102332268A (en) | Speech signal sparse representation method based on self-adaptive redundant dictionary | |
| CN109085547A (en) | A kind of denoising method and relevant apparatus of surface penetrating radar echo-signal | |
| Pei et al. | Research on PD signals denoising based on EMD method | |
| Ou et al. | Frame-based time-scale filters for underwater acoustic noise reduction | |
| Malhotra et al. | Electrocardiogram signals denoising using improved variational mode decomposition | |
| Jianhua et al. | Impulse interference processing for MT data based on a new adaptive wavelet threshold de-noising method | |
| CN117970322B (en) | Low-rank block sparse decomposition-based vital signal enhancement method | |
| Fang et al. | A Multi-target Underwater Acoustic Signals Denoising Method Based on Wavelet | |
| CN110703089B (en) | Wavelet threshold denoising method for low-frequency oscillation Prony analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |