CN111669409B - Sign data monitoring system - Google Patents

Sign data monitoring system Download PDF

Info

Publication number
CN111669409B
CN111669409B CN202010673776.5A CN202010673776A CN111669409B CN 111669409 B CN111669409 B CN 111669409B CN 202010673776 A CN202010673776 A CN 202010673776A CN 111669409 B CN111669409 B CN 111669409B
Authority
CN
China
Prior art keywords
sign data
data
compressed
module
analog
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
Application number
CN202010673776.5A
Other languages
Chinese (zh)
Other versions
CN111669409A (en
Inventor
黄芳
张�浩
刘海洋
赵坤
龙飞
陈立平
杨亚光
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Institute of Microelectronics of CAS
Original Assignee
Institute of Microelectronics of CAS
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Institute of Microelectronics of CAS filed Critical Institute of Microelectronics of CAS
Priority to CN202010673776.5A priority Critical patent/CN111669409B/en
Publication of CN111669409A publication Critical patent/CN111669409A/en
Application granted granted Critical
Publication of CN111669409B publication Critical patent/CN111669409B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
    • H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • 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
    • A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • 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
    • A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
    • H04L69/04—Protocols for data compression, e.g. ROHC
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/021—Measuring pressure in heart or blood vessels

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Signal Processing (AREA)
  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Biomedical Technology (AREA)
  • Pathology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Biophysics (AREA)
  • Physiology (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • Physics & Mathematics (AREA)
  • Veterinary Medicine (AREA)
  • Psychiatry (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Cardiology (AREA)
  • Computer Security & Cryptography (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Power Engineering (AREA)
  • Computing Systems (AREA)
  • Pulmonology (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)

Abstract

The application discloses sign data monitored control system, including wearable sensing equipment and platform, wherein wearable sensing equipment carries out analog signal processing and analog-to-digital conversion to the sign data of gathering, and further adopt compressed sensing mode to carry out compressed sampling to the sign data, obtain compressed sampling data compare in the data volume greatly reduced of the sign data of direct collection, wearable sensing equipment only need with a small amount of compressed sampling data send to the platform can, reduced the consumption of wearable equipment, thereby promoted operating time, can be long-time stable monitor the sign of target object. Further, the platform can restore the compressed sampling data to obtain the restored sign data, and the normal use of the sign data is ensured.

Description

Sign data monitoring system
Technical Field
The application relates to the technical field of physical sign data monitoring, in particular to a physical sign data monitoring system.
Background
In recent years, the steady development of mobile computing, integrated circuit technology, wireless sensor networks and medical devices opens up a way for miniature, low-cost, low-power consumption and multifunctional intelligent monitoring devices, and is suitable for a plurality of portable medical device application programs. Wearable body area networks based on wireless and embedded monitoring devices can continuously monitor vital sign data such as electrocardiographic, blood pressure, pulse, respiration, etc., and provide feedback to help maintain optimal health. These networks allow continuous, long-term, noninvasive, ubiquitous dynamic monitoring of vital signs, leading to revolutionary changes in healthcare.
Taking the monitoring of electrocardiographic data as an example, an electrocardiogram is a biological signal representing the electrical activity of the heart. Because of its noninvasive nature and ability to detect heart disease, it is widely used in the medical field. Electrocardiography is usually recorded in a hospital or clinical center where patients need to stay for hours or days. Portable or mobile electrocardiographic monitoring devices enable a patient to monitor his or her electrocardiogram, record electrocardiographic data and transmit it to a hospital. The transmitted data will be processed in the health center in case of any anomalies.
The existing wearable internet of things equipment needs to continuously transmit a large amount of monitoring data back to the background, so that the power consumption of the equipment is too high, the battery energy consumption is too fast, and the equipment cannot operate for a long time.
Disclosure of Invention
In view of the above problems, the present application has been proposed so as to provide a physical sign data monitoring system, which is used for solving the problems of high power consumption and short operation time of the existing equipment. The specific scheme is as follows:
a vital sign data monitoring system, comprising: wearable sensing devices and platforms;
the wearable sensing equipment comprises a physical sign data acquisition device, an analog front-end module, a compression sampling module and a first communication module, wherein the physical sign data acquisition device is used for acquiring physical sign data of an object; the analog front-end module is used for performing analog signal processing on the collected sign data to obtain processed sign data; the compressed sampling module is used for carrying out analog-to-digital conversion on the processed sign data, and carrying out compressed sampling on the converted sign data in a compressed sensing mode to obtain compressed sampling data; the first communication module is used for sending the compressed sampling data to the platform;
the platform comprises a second communication module and a compressed sensing processing module, wherein the second communication module receives the compressed sampling data; the compressed sensing processing module is used for carrying out signal recovery based on the compressed sampling data to obtain recovered sign data.
Preferably, the analog front end module includes:
and the alternating-current coupling chopper modulation instrument amplifier and the chopper peak filter are used for filtering interference noise in the sign data.
Preferably, the analog front end module further comprises:
and the programmable gain amplifier is used for amplifying the analog signals of the sign data after noise filtering.
Preferably, the analog front end module further comprises:
and the buffer is used for carrying out stable processing on the sign data amplified by the analog signal.
Preferably, the compressed sampling module includes:
the analog-to-digital converter is used for converting the sign data in the form of analog signals into digital signals;
the discrete wavelet transformation unit is used for carrying out sparsification on the sign data in the digital signal form to obtain the sparse sign data;
the compression unit is used for carrying out compression processing on the thinned physical sign data to obtain compressed sampling data.
Preferably, the analog-to-digital converter is a successive approximation register analog-to-digital converter, and the sampling rate of the analog-to-digital converter is adjustable;
the discrete wavelet transformation unit comprises a multi-stage wavelet transformation unit and a digital clock manager, and the digital clock manager generates a corresponding clock signal according to the sampling rate and the data processing power of the successive approximation register type analog converter; the multi-stage wavelet transformation unit adopts multi-stage decomposition wavelet transformation under different clock signals to sparsify the sign data in the form of digital signals, so as to obtain the sparse sign data.
Preferably, the compression unit comprises a binary sparse matrix compression unit, which is used for adopting a binary sparse matrix as a measurement matrix for compressed sensing processing, and performing compression processing on the thinned physical sign data to obtain compressed sampling data.
Preferably, the binary sparse matrix is a low density parity check matrix.
Preferably, the compressed sensing processing module comprises a signal recovery module, which is used for carrying out signal recovery on the compressed sampling data based on a block sparse Bayesian learning algorithm to obtain the recovered sign data.
Preferably, the wearable sensing device further comprises:
the channel pre-coding module is used for carrying out channel pre-coding on the compressed sampling data based on a dynamic threshold value which is returned by the platform and can reflect the spatial characteristics of the signals, so that the spatial characteristics of the pre-coded compressed sampling data are matched with the channel conditions of the first communication module;
the first communication module sends the precoded compressed sampling data and the output coefficient of channel precoding to a platform;
the compressed sensing processing module further comprises: and the dynamic threshold generating module is used for analyzing the output coefficient to generate a dynamic threshold, and the second communication module sends the generated dynamic threshold to the wearable sensing equipment.
By means of the technical scheme, the physical sign data monitoring system comprises the wearable sensing equipment and the platform, wherein the wearable sensing equipment carries out analog signal processing and analog-to-digital conversion on collected physical sign data, and further adopts a compressed sensing mode to carry out compressed sampling on the physical sign data, so that the data volume of the compressed sampling data compared with that of the directly collected physical sign data is greatly reduced, the wearable sensing equipment only needs to send a small amount of compressed sampling data to the platform, the power consumption of the wearable equipment is reduced, the running time is prolonged, and the physical sign of a target object can be monitored stably for a long time. Further, the platform can restore the compressed sampling data to obtain the restored sign data, and the normal use of the sign data is ensured.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 is a schematic structural diagram of a physical sign data monitoring system according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an analog front end module according to an embodiment of the present application;
FIG. 3 is a schematic diagram of another analog front end module according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of another embodiment of an analog front end module;
fig. 5 is a schematic structural diagram of a compressed sampling module according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a compressed sensing processing module according to an embodiment of the present application;
fig. 7 is a flowchart of a signal recovery method according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of another physical sign data monitoring system according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The utility model provides a physical sign data monitored control system solves the high power consumption of current monitored control system, the short scheduling problem of operating time, realizes long-term monitoring record's the purpose to the wearer physical sign data such as electrocardiosignal etc..
The physical sign data monitoring system disclosed in the embodiment of the present application is described with reference to fig. 1.
As shown in fig. 1, the vital sign data monitoring system may include:
a wearable sensing device 1 and a platform 2.
The wearable sensing device 1 may include the following constituent modules:
the system comprises a physical sign data acquisition unit 11, an analog front end module 12, a compression sampling module 13 and a first communication module 14.
The sign data collector 11 is used for collecting sign data of an object to be monitored. According to different types of the sign data to be acquired, the sign data acquirer 11 can adopt different sensors, for example, the sign data acquirer can be an acquirer for acquiring a plurality of different sign data such as electrocardiosignals, pulse signals, blood pressure signals and the like.
The sign data collected by the sign data collector 11 is in the form of an analog signal and can be transmitted to the analog front end module 12.
The analog front end module 12 is configured to perform analog signal processing, such as denoising, on the collected sign data, so as to obtain processed sign data. The processed vital sign data may be transmitted to the compressed sampling module 13 for subsequent processing.
The compressed sampling module 13 is used for performing analog-to-digital conversion on the sign data in the form of the received analog signal, and converting the sign data into the form of a digital signal for subsequent processing. Furthermore, in order to reduce the data size of the sign data, the compressive sampling module 13 may further perform compressive sampling on the sign data by adopting a compressive sensing manner to obtain compressive sampled data.
The compressed sensing processing mode can be understood as: when the signal is sparse or compressible in a certain transform domain, the transform coefficients can be linearly projected as low-dimensional observation vectors using a measurement matrix that is non-coherent with the transform matrix, while such projection retains the information needed to reconstruct the signal, the original high-dimensional signal can be accurately or with high probability accurately reconstructed from the low-dimensional observation vectors by further solving the sparse optimization problem.
The basic principle of the compressed sensing processing technique is explained in mathematical form as follows: modeling compressed sensing in matrix form as y M×1 ＝Φ M×N x N×1 ＝Φ M×N Ψ N×P s P×1 The method comprises the steps of carrying out a first treatment on the surface of the Wherein x is N×1 Is an input vector having N dimensions, Φ M×N Is a measurement matrix (M < N), y M×1 Is the measured vector after observation, ψ N×P Is a sparse basis for the input vector, and s P×1 Is the corresponding sparse coefficient vector phi M×N And psi is equal to N×P Constraint equidistant conditions (RIPs) are satisfied. s is(s) P×1 At most K (K < N) non-zero terms are called s P×1 Is a signal with sparsity K, x N×1 At ψ N×P The domain has sparsity. The reconstruction basis in compressed sensing is that using solution l 1 Norm minimization problem reconstruction signal
The compressed sampling module 13 performs compressed sampling on the sign data by adopting compressed sensing, and the data volume of the obtained compressed sampling data is greatly reduced compared with the original sign data.
On this basis, the compressed sampled data is transmitted to the platform 2 via the first communication module 14.
Because the data volume of the compressed sampled data is greatly reduced compared with the original physical sign data, the data volume required to be sent by the first communication module 14 is smaller, the power consumption is lower, and the operation time of the wearable sensing device is longer under the condition of the same electric quantity support.
Further, the platform 2 may be a fixed or movable terminal device, such as a server, a mobile phone, a notebook, or other terminal devices, which may be specifically selected according to actual needs.
The platform 2 may comprise the following constituent modules:
a second communication module 21 and a compressed sensing processing module 22.
The second communication module 21 establishes communication connection with the first communication module 14, so as to realize a data communication function between the wearable sensing device 1 and the platform 2.
The first communication module 14 and the second communication module 21 may be a bluetooth communication module, a radio frequency communication module, or other wireless communication modes.
The second communication module 21 is configured to receive the compressed sampled data sent by the first communication module 14, and send the compressed sampled data to the compressed sensing processing module 22.
The compressed sensing processing module 22 performs inverse signal recovery, i.e. reconstructing the sign data before compressed sampling, based on the compressed sampled data.
The utility model provides a sign data monitored control system, including wearable sensing device and platform, wherein wearable sensing device carries out analog signal processing and analog-to-digital conversion to the sign data of gathering to further adopt compressed sensing mode to carry out compressed sampling to the sign data, obtain compressed sampling data compare in the data volume greatly reduced of the sign data of direct collection, wearable sensing device only need with a small amount of compressed sampling data send to the platform can, reduced the consumption of wearable device, thereby promoted operating time, can monitor the sign of target object stable for a long time. Further, the platform can restore the compressed sampling data to obtain the restored sign data, and the normal use of the sign data is ensured.
Further, the structure of the analog front end module 12 described above in the present application will be described.
As shown in fig. 2, the analog front end module 12 may include:
an ac-coupled chopper modulated instrumentation amplifier 121 and a chopper spike filter 122 for filtering out interference noise in the vital sign data.
For the collected physical sign data, which contains various interference noises, high input impedance is required to inhibit the imbalance of the direct current differential electrode and realize high common mode rejection ratio. In this embodiment, a chopping technique is introduced, for impedances greater than 1gΩ, and a common mode rejection ratio of greater than 115dB is achieved at 50Hz mains frequency, to reject noise of interfering signals.
As further shown in connection with fig. 3, the analog front end module 12 may further include:
and the programmable gain amplifier 123 is used for amplifying the analog signal of the sign data after noise filtering.
Still further, as shown in connection with fig. 4, the analog front end module 12 may further include:
and a buffer 124 for performing a smooth processing on the sign data amplified by the analog signal.
In another embodiment of the present application, the structure of the compressed sampling module 13 is described.
As shown in fig. 5, the compressive sampling module 13 may include:
the analog-to-digital converter 131 is used for converting the sign data in the form of analog signals into digital signals.
The analog-to-digital converter 131 may be a successive approximation register analog-to-digital converter, and the sampling rate of the analog-to-digital converter is adjustable.
The discrete wavelet transform unit 132 is configured to sparse the sign data in the digital signal form, and obtain the sparse sign data.
Alternatively, the discrete wavelet transform unit 132 may include a multi-stage wavelet transform unit and a digital clock manager, wherein the digital clock manager generates a corresponding clock signal according to a sampling rate and a data processing power of the successive approximation register type analog-to-digital converter 131; the multi-stage wavelet transformation unit adopts multi-stage decomposition wavelet transformation under different clock signals to sparsify the sign data in the form of digital signals, so as to obtain the sparse sign data.
And the compression unit 133 is configured to perform compression processing on the thinned physical sign data to obtain compressed sampling data.
Specifically, the thinned physical sign data compresses the signal with the original length N into a signal with the length M by the compression unit 133, where M is much smaller than N.
Optionally, the compression unit 133 may include a binary sparse matrix compression unit, configured to use a binary sparse matrix as a measurement matrix for compressed sensing processing, and perform compression processing on the thinned sign data to obtain compressed sampling data.
The binary sparse matrix is a measurement matrix of the compressed sensing process, which may be defined by a low density parity check matrix H of size mxn.
Sparse binary sequences are those binary sequences that contain an initial random distribution of a small number 1 and a large number 0. These sequences and their sequences shifted at specified positions constitute a low density parity check matrix such that each sequence corresponds to a row in the matrix. As a simple example: there are two sparse sequences S 1 = 1100001100 and s2= 101111000100. If each sequence is cyclically shifted left by four positions, the low density parity check matrix based on the two sparse sequences is the following matrix:
the first and fourth rows of H correspond to S respectively 1 And S is 2 . The other row of H is S 1 And S is 2 We refer to those lines corresponding to the original sparse sequence as key lines. Thus we can obtain a simple matrix with a block size of 6 x 12 based on two sequences. If the matrix is large enough, the key rows of 4 and 6 periods can be deleted according to a certain rule to obtain the needed low-density parity check matrix.
In yet another embodiment of the present application, the structure of the compressed sensing processing module 22 in the platform 2 is described.
As shown in connection with fig. 6, the compressed sensing processing module 22 may include a signal recovery module 221, configured to perform signal recovery on the compressed sampled data based on a block sparse bayesian learning algorithm, to obtain recovered sign data.
The signal recovery module 221 may specifically perform signal recovery on the compressed sampled data based on a block sparse bayesian learning algorithm with boundary optimization, so as to obtain recovered sign data.
Next, a process of performing signal recovery on the compressed sampled data based on the block sparse bayesian learning algorithm by the signal recovery module 221 to obtain the recovered sign data will be described.
The compressed sensing basic model containing the noise vector epsilon can be described as:
y＝Φx+ε (1)
where y is compressed sampling data and x is recovered sign data.
If the sparse signal x has block sparsity, it can be seen as a concatenation of blocks:
equations (3) and (4) constitute a block sparse CS model. In this model the vector x can be divided into g blocks, with each x set i (i=1, 2, …, g) satisfies the parameterized multivariate gaussian distribution:
p(x i ；γ i ,B i )＝Ν(0,γ i B i )(i＝1,2,…,g) (3)
wherein: gamma ray i Is a non-negative parameter, determines block x i Sparsity of (c) when gamma i Corresponding x when=0 i ＝0；Is to x i A positive definite matrix of covariance structure modeling of (c) representing correlations between elements within a block structure. B (B) i The definition is as follows: group d-dimensional random variable x= (X) 1 ,x 2 ,…,x d ) T The covariance of the two random variables was cov [ x i ,x j ]＝E[(x i -E[x i ])(x j -E[x j ])]The covariance matrix consisting of d×d covariances is
When the block sizes are the same, the effective measures to avoid overfitting are parametrically averaged, i.e. starting with block 2To constrain, the learning rule BETA is as follows:
formula (3) is denoted as p (x) i ；{γ i ,B i } i )～Ν(0,Σ 0 ) Wherein Σ is 0 ＝diag{γ 1 B 1 ,…,γ g B g }. It is also assumed that the noise ε obeys a p (ε; λ) N (0, λI) distribution, where λ is a positive scalar. ThenWherein mu x ＝Σ 0 Φ T (λΙ+ΦΣ 0 Φ T ) -1 y，/>When the parameter lambda, ->Estimated, maximum A Posteriori (MAP) estimate of x->Can be obtained from posterior mean, i.e. +.>Estimating parameters by class II maximum likelihood estimation, equivalent to the most efficientThe following cost function L (Θ) is minimized:
wherein: theta represents all parameters, e.g
The original cost function L (Θ) in equation (6) consists of two parts, part 1 log|λI+ΦΣ 0 Φ T I atIs concave at the top, part 2 y T (λΙ+ΦΣ 0 Φ T ) -1 y is convex at gamma.gtoreq.0. Part 1 is selected to find an upper limit and then the upper limit of the cost function L (Θ) is minimized. Let gamma be * For a given point in space, then there is:
wherein the method comprises the steps of
Substitution of formula (7) into (6) yields:
using a substitution function:
the optimal x is mu x Then there is
The new function Γ (γ, x) is defined asUpper limit of (2):
note that Γ (γ, x) is convex in γ, x, as can be readily seenIs a solution of Γ (γ, x), thus eventually replacing the cost function L (Θ) with Γ (γ, x). Take Γ (γ, x) with respect to γ i The derivative of (2) can be obtained:
in order to facilitate understanding, the signal recovery module performs signal recovery on the compressed sampled data based on a block sparse bayesian learning algorithm, and the process of obtaining the recovered sign data is introduced in the form of a step flow, as follows:
as shown in connection with fig. 7, the process may include:
step S100, input: y, phi, eta.
Wherein y is compressed sampling data, phi is a set measurement matrix, eta is an exit condition, and eta=1e can be obtained -8 。
Step S110, initializing: γ=1; λ=1e -2 ||y|| 2 。
Step S120, calculating mu x 、Σ x 。
Wherein mu x 、Σ x The calculation of (2) may be referred to the foregoing description, and will not be repeated here.
Step S130, calculating
Step S140, calculating
Step S150, calculating
Step S160, judgingIf yes, go back to step S120, if no, go to step S170.
Step S170, output x=μ,
Wherein the sparse solution x=μ is the restored sign data,is a parameter estimation.
In a further embodiment of the present application, another alternative structure of the wearable sensor device 1 is presented.
As shown in connection with fig. 8, the wearable sensing device 1 may further comprise: the channel pre-coding module 15 is configured to perform channel pre-coding on the compressed sampled data based on a dynamic threshold value that is returned by the platform 2 and is capable of representing a spatial characteristic of a signal, so that the spatial characteristic of the pre-coded compressed sampled data matches with a channel condition of the first communication module 14.
Specifically, the channel pre-coding compensates some problems of the transmission channel by pre-processing the collected signals, so as to reduce the possibility of error code of the platform receiving signals. The channel pre-coding module 15 can optimize the spatial characteristics of the signal to be transmitted according to the channel condition, so that the spatial distribution characteristics of the signal are matched with the channel condition, and the dependence on the receiver algorithm can be effectively reduced.
Based on this, the first communication module 14 transmits the precoded compressed sampled data, as well as the channel precoded output coefficients, to the platform 2.
The compressed sensing processing module 22 in the platform 2 may further include: the dynamic threshold generating module 222 is configured to analyze the output coefficient to generate a dynamic threshold, and send the generated dynamic threshold to the wearable sensing device 1 by the second communication module 21.
The dynamic threshold generation module 222 may calculate performance parameters of the recovered sign data, such as a Mean Square Error (MSE), a percentage root mean square error (PRD (%)), a signal-to-noise ratio (SNR), and the like, according to the output coefficient, so as to generate a dynamic threshold according to the performance parameters, so that the performance of the recovered sign data based on the compressed sampled data meets the requirement.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the present specification, each embodiment is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and may be combined according to needs, and the same similar parts may be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A vital sign data monitoring system, comprising: wearable sensing devices and platforms;
the wearable sensing equipment comprises a physical sign data acquisition device, an analog front-end module, a compression sampling module and a first communication module, wherein the physical sign data acquisition device is used for acquiring physical sign data of an object; the analog front-end module is used for performing analog signal processing on the collected sign data to obtain processed sign data; the compressed sampling module is used for carrying out analog-to-digital conversion on the processed sign data, and carrying out compressed sampling on the converted sign data in a compressed sensing mode to obtain compressed sampling data; the first communication module is used for sending the compressed sampling data to the platform; the compressed sensing mode is that when a signal is sparse or compressible in any transform domain, a measurement matrix incoherent with a transformation matrix is utilized to linearly project the transformation coefficient into a low-dimensional observation vector; wherein, each information needed for reconstructing the signal is maintained during the projection process;
the platform comprises a second communication module and a compressed sensing processing module, wherein the second communication module receives the compressed sampling data; the compressed sensing processing module is used for carrying out signal recovery based on the compressed sampling data to obtain recovered sign data;
the process of signal recovery for the compressed sampled data is as follows:
step S100, input: y, phi, eta;
wherein y is compressed sampling data, phi is a set measurement matrix, eta is an exit condition, and eta=1e (-8);
step S110, initializing: γ=1; λ=1e (-2) y|is known as a whole 2 The method comprises the steps of carrying out a first treatment on the surface of the Wherein gamma is non-negativeParameters, initial value is 1; λ is a positive scalar;
step S120, calculating mu x 、Σ x ；
Step S130, calculatingT is matrix transposition operation; vector x in the model may be divided into g blocks, i=1, 2, …, g, denoted as i-th block data; m is the signal length;
step S140, calculatingWherein, gamma i For a non-negative parameter of the ith block, determine block x i Sparsity of (c) when gamma i Corresponding x when=0 i ＝0；/>For gamma is * Mathematical operations for a given point in space;
step S150, calculatingWhen the block sizes are the same, the effective measures to avoid overfitting are parametrically averaged, i.e. starting with block 2 with +.>To constrain;
step S160, judgingIf yes, returning to the execution step S120, if not, executing the step S170; wherein, gamma new The latest estimated value of the non-negative parameter;
step S170, output x=μ,
Wherein the sparse solution x=μ is the restored sign data,is a parameter estimation.
2. The system of claim 1, wherein the analog front end module comprises:
and the alternating-current coupling chopper modulation instrument amplifier and the chopper peak filter are used for filtering interference noise in the sign data.
3. The system of claim 2, wherein the analog front end module further comprises:
and the programmable gain amplifier is used for amplifying the analog signals of the sign data after noise filtering.
4. The system of claim 3, wherein the analog front end module further comprises:
and the buffer is used for carrying out stable processing on the sign data amplified by the analog signal.
5. The system of claim 1, wherein the compressed sampling module comprises:
the analog-to-digital converter is used for converting the sign data in the form of analog signals into digital signals;
the discrete wavelet transformation unit is used for carrying out sparsification on the sign data in the digital signal form to obtain the sparse sign data;
the compression unit is used for carrying out compression processing on the thinned physical sign data to obtain compressed sampling data.
6. The system of claim 5, wherein the analog-to-digital converter is a successive approximation register analog-to-digital converter with an adjustable sampling rate;
the discrete wavelet transformation unit comprises a multi-stage wavelet transformation unit and a digital clock manager, and the digital clock manager generates a corresponding clock signal according to the sampling rate and the data processing power of the successive approximation register type analog converter; the multi-stage wavelet transformation unit adopts multi-stage decomposition wavelet transformation under different clock signals to sparsify the sign data in the form of digital signals, so as to obtain the sparse sign data.
7. The system of claim 5, wherein the compression unit comprises a binary sparse matrix compression unit configured to use a binary sparse matrix as a measurement matrix for compressed sensing processing, and perform compression processing on the thinned physical sign data to obtain compressed sampled data.
8. The system of claim 7, wherein the binary sparse matrix is a low density parity check matrix.
9. The system of claim 1, wherein the compressed sensing processing module includes a signal recovery module configured to perform signal recovery on the compressed sampled data based on a block sparse bayesian learning algorithm to obtain recovered sign data.
10. The system of claim 9, wherein the wearable sensing device further comprises:
the channel pre-coding module is used for carrying out channel pre-coding on the compressed sampling data based on a dynamic threshold value which is returned by the platform and can reflect the spatial characteristics of the signals, so that the spatial characteristics of the pre-coded compressed sampling data are matched with the channel conditions of the first communication module;
the first communication module sends the precoded compressed sampling data and the output coefficient of channel precoding to a platform;
the compressed sensing processing module further comprises: and the dynamic threshold generating module is used for analyzing the output coefficient to generate a dynamic threshold, and the second communication module sends the generated dynamic threshold to the wearable sensing equipment.
CN202010673776.5A 2020-07-14 2020-07-14 Sign data monitoring system Active CN111669409B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010673776.5A CN111669409B (en) 2020-07-14 2020-07-14 Sign data monitoring system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010673776.5A CN111669409B (en) 2020-07-14 2020-07-14 Sign data monitoring system

Publications (2)

Publication Number Publication Date
CN111669409A CN111669409A (en) 2020-09-15
CN111669409B true CN111669409B (en) 2024-02-27

Family

ID=72391931

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010673776.5A Active CN111669409B (en) 2020-07-14 2020-07-14 Sign data monitoring system

Country Status (1)

Country Link
CN (1) CN111669409B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113197582B (en) * 2021-04-27 2022-03-25 浙江大学 High-universality electrocardio data compression sensing system, terminal and storage medium
CN113384242A (en) * 2021-07-22 2021-09-14 中南大学湘雅三医院 Self-adaptive compression acquisition and transmission method and device for operation physiological monitoring signals
CN113925514A (en) * 2021-09-28 2022-01-14 西安交通大学 Portable multi-lead ECG acquisition system and data processing method thereof

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102375144A (en) * 2011-09-22 2012-03-14 北京航空航天大学 Single-photon-counting compression-sampling laser three-dimensional imaging method
CN103281087A (en) * 2013-06-09 2013-09-04 中国科学院微电子研究所 System and method of signal sampling based on multistage compressed sensing
CN104933846A (en) * 2015-06-04 2015-09-23 中国科学院苏州生物医学工程技术研究所 Body sensor network system based on compressed sensing
CN204797851U (en) * 2015-01-26 2015-11-25 郑州恒之杰电子科技有限公司 Wearable many first physiological parameters of human inferior health spirit pressure analysis appearance
CN107147397A (en) * 2017-04-24 2017-09-08 电子科技大学 Towards the Fast Compression sensing reconstructing method of wearable device
CN108419083A (en) * 2018-03-22 2018-08-17 南京邮电大学 A kind of full subband compressed sensing encryption algorithm of image multilevel wavelet
CN109905129A (en) * 2019-03-06 2019-06-18 湖南大学 Low overhead electric power data acquisition method based on distributed compression sensing
CN110276726A (en) * 2019-05-13 2019-09-24 南昌大学 A kind of image deblurring method based on the guidance of multichannel network prior information
CN110742647A (en) * 2019-10-25 2020-02-04 南京大学 Portable ultrasonic nondestructive testing system based on compressed sensing
CN110956192A (en) * 2019-04-02 2020-04-03 南京邮电大学 Method and device for classifying non-reconstruction compressed sensing physiological data

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106663316A (en) * 2016-08-30 2017-05-10 深圳大学 Block sparse compressive sensing-based infrared image reconstruction method and system thereof
US10575788B2 (en) * 2016-10-18 2020-03-03 Arizona Board Of Regents On Behalf Of Arizona State University Compressive sensing of quasi-periodic signals using generative models

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102375144A (en) * 2011-09-22 2012-03-14 北京航空航天大学 Single-photon-counting compression-sampling laser three-dimensional imaging method
CN103281087A (en) * 2013-06-09 2013-09-04 中国科学院微电子研究所 System and method of signal sampling based on multistage compressed sensing
CN204797851U (en) * 2015-01-26 2015-11-25 郑州恒之杰电子科技有限公司 Wearable many first physiological parameters of human inferior health spirit pressure analysis appearance
CN104933846A (en) * 2015-06-04 2015-09-23 中国科学院苏州生物医学工程技术研究所 Body sensor network system based on compressed sensing
CN107147397A (en) * 2017-04-24 2017-09-08 电子科技大学 Towards the Fast Compression sensing reconstructing method of wearable device
CN108419083A (en) * 2018-03-22 2018-08-17 南京邮电大学 A kind of full subband compressed sensing encryption algorithm of image multilevel wavelet
CN109905129A (en) * 2019-03-06 2019-06-18 湖南大学 Low overhead electric power data acquisition method based on distributed compression sensing
CN110956192A (en) * 2019-04-02 2020-04-03 南京邮电大学 Method and device for classifying non-reconstruction compressed sensing physiological data
CN110276726A (en) * 2019-05-13 2019-09-24 南昌大学 A kind of image deblurring method based on the guidance of multichannel network prior information
CN110742647A (en) * 2019-10-25 2020-02-04 南京大学 Portable ultrasonic nondestructive testing system based on compressed sensing

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
便携式多参数生物信号测量系统设计与实现;张浩;《CNKI》;20170215(第2期);全文 *
徐焕章.编码精度自调节的心电压缩ASIC.《CNKI》.2018,(第6期), *
编码精度自调节的心电压缩ASIC;徐焕章;《CNKI》;20180615(第6期);第2节 *

Also Published As

Publication number Publication date
CN111669409A (en) 2020-09-15

Similar Documents

Publication Publication Date Title
CN111669409B (en) Sign data monitoring system
KR101528541B1 (en) Method and apparatus for low complexity compression of signals
Baali et al. Empowering technology enabled care using IoT and smart devices: a review
US10575788B2 (en) Compressive sensing of quasi-periodic signals using generative models
US20130317377A1 (en) Generative Model-Driven Resource-Efficient Monitoring in Body Sensor Networks
Nabar et al. GeM-REM: Generative model-driven resource efficient ecg monitoring in body sensor networks
Bera et al. Hybrid encoding algorithm for real time compressed electrocardiogram acquisition
Šaliga et al. ECG compressed sensing method with high compression ratio and dynamic model reconstruction
Balouchestani et al. Low power wireless body area networks with compressed sensing theory
Lal et al. Compressed sensing approach for physiological signals: A review
Elsayed et al. FPGA implementation of DWT EEG data compression for wireless body sensor networks
Allstot et al. Compressed sensing of ECG bio-signals using one-bit measurement matrices
CN111329468A (en) Real-time monitoring system and monitoring method for cardiology department
Lalouani et al. Energy-efficient collection of wearable sensor data through predictive sampling
Hussein et al. Energy-efficient EEG monitoring systems for wireless epileptic seizure detection
Balouchestani et al. Robust compressive sensing algorithm for wireless surface electromyography applications
Marnane et al. Energy efficient on-sensor processing in body sensor networks
Hussein et al. Adaptive compression and optimization for real-time energy-efficient wireless EEG monitoring systems
Bortolotti et al. Quantifying the benefits of compressed sensing on a WBSN-based real-time biosignal monitor
Shoaib et al. Digital pacer detection in diagnostic grade ecg
CN104579361A (en) Low-power-consumption multi-source physiological signal mixed compression method for wireless body area network
Chen et al. Wearable algorithms: An overview of a truly multi-disciplinary problem
KR101526774B1 (en) A method for transmitting an electrocardiography signal using compressed sensing and a monitoring system using thereof
Chang et al. Microcontroller implementation of low-power compression for wearable biosignal transmitter
JP2021115429A (en) Signal measuring system, measurement signal processing device and program

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