CN112019530B - Physiological signal safe compression method and system suitable for body area network - Google Patents
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Abstract
The invention discloses a physiological signal safety compression method and system suitable for a body area network. The encryption compression method comprises the following steps: the method comprises the steps that a receiving end denoises an acquired physiological signal, divides the physiological signal into short data sections with fixed length based on a signal complete characteristic cycle, encrypts and compresses each short data section by adopting a disposable random measurement matrix, and sends the short data sections to the receiving end in batches through a public wireless channel; and the receiving end acquires the encrypted data segment, reconstructs a corresponding random measurement matrix by using the key received by the secure channel, decrypts and decompresses the encrypted data at one time, and reconstructs the original new signal. The method realizes the simultaneous completion of compression and encryption, reduces the computational complexity by adopting short data segments at the body area network end, realizes the real-time performance by carrying out one-time decryption and decompression on a plurality of data segments at the server end, and is suitable for the body area network health medical application with limited computational resources.
Description
Technical Field
The invention relates to the field of biomedical information processing, in particular to a compression and reconstruction method and an encryption system of a physiological signal.
Background
In recent years, wireless Body Area Network (BAN) based telemedicine monitoring systems have become a key infrastructure for intelligent medical care. A typical body area network node is a biosensor that is worn on the body surface or implanted in the body and integrates physiological data acquisition and processing, wireless communication, and other functions. The sensor nodes form a wireless sensor network, the network topology structure generally adopts a simple star topology structure or a tree structure with no more than two hops, nodes with sufficient resources play the role of a main node, and the nodes are uploaded to a server side of a remote medical structure through a wireless communication technology. However, there are a plurality of technical difficulties in the transmission process of the remote physiological signal, which mainly include: 1) large data size: taking the conventional electrocardiographic signal monitoring as an example, for a single-lead electrocardiographic sensor, if the analog/digital conversion sampling rate is 1KHz and the resolution is 12 bits, the data volume acquired in 24 hours is about 124 MB. And various physiological signals (electrocardio, electroencephalogram, myoelectricity, blood oxygen and acceleration) of one patient can reach 13GB easily within 24 hours. 2) The safety problem is as follows: the data security and user privacy protection problems brought by wireless communication will be increasingly highlighted along with the wide application of body feeling networks in the medical health field. However, the security of communication in the body area network is restricted by the overall power consumption, area, complexity, and the like of the device, and an encryption scheme for network security and the like cannot be directly migrated. Therefore, there is a need to develop compression and security techniques for body area networks during the remote transmission of physiological signals.
Compressed Sensing (CS) theory, by oneM×NCompression matrix of dimensionΦWill beNSignals of dimensionXProjected asMSignals of dimensionY(Y＝ΦX). Due to the fact thatM<<NThereby achieving compression of the signal. As long asXWith sparsity, the original signal can be recovered through a reconstruction algorithmX. Wherein the measurement matrix not only determines the compression ratio and the signal reconstruction quality, but also can be used as a key for encrypting the signal, and simultaneously completes the compression and encryption. However, if the measurement matrix is directly used as the shared key, large storage and transmission resources are required, which is not suitable for the body feeling network nodes with limited resources. Therefore, a security compression strategy suitable for body area networks and oriented to limited computing resources needs to be developed.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a system and a method for safely compressing physiological signals suitable for body area network nodes, which improve transmission efficiency and provide data safety guarantee. The technical characteristics are as follows: 1) compared with the traditional signal compression, encryption, decryption and decompression processes, the system realizes the extremely simplified processing flow of simultaneously completing the compression and encryption of the sending end and the decryption and decompression of the receiving end, and simultaneously ensures the real-time performance and the safety of physiological signal transmission; 2) considering different computing capacities of a body area network and a remote medical center, physiological signals are divided into signal segments with fixed lengths at nodes of the body area network, encryption and compression are carried out in batches, a plurality of signal segments are received at a server end at one time, and meanwhile decryption and decompression are carried out, so that the computing performance and the real-time performance are improved.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
the system comprises a sending end and a receiving end, wherein the sending end is a body area network node and comprises a signal acquisition module, a preprocessing module, a random measurement matrix generation module, an encryption compression module and a wireless sending module; the receiving end is a medical institution server end and comprises a wireless receiving module, a preprocessing module, a measurement matrix reconstruction module, a decryption and decompression module and a signal reconstruction module;
obtaining, at the signal acquisition module, using a sensortSecond (e.g., 10 seconds) physiological signals and filtering. Then converted into digital signals by samplingX。
At the preprocessing module, the data is processedtThe second physiological signal is divided into short data segments with fixed length, and the segmentation algorithm is carried out according to the period of the specific physiological signal. The method has three goals: 1) based on the data segment of the signal period, the complete characteristics of a specific physiological signal can be reserved, and invalid redundant signals are removed; 2) each short data segment will use a unique random measurement matrix for encryption and compression; 3) and the body feeling network node carries out encryption compression on the short data segment, thereby reducing the computational complexity. First, the iA short data segment of a physiological signal is marked as
。nThe number of samples before decompression.
At the random measurement matrix generation module, the output of the generation moduleiA random measurement matrix marked
. The random sequence generation module generates by a linear shift feedback register (LFSR)Then, the random sequence is converted into a random matrix through a specific matching function. A chaotic module is added to the module to increase the seed randomness of the LFSR. The seed of the random sequence is used as a key and is sent through a key security channel, so that the sender and the receiver jointly hold the seed.
The encryption compression module comprises a storage module for storing a fixed hidden sensing matrix
For increased safety; the output of the preprocessing module and the output of the random measurement matrix generating module are operated to obtain the secondiA compressed and encrypted physiological signal, marked as
WhereinαAndβin order to be able to adjust the parameters,mthe number of samples after compression.
At the wireless transmission module, williA signal
A remote transmission is made to the recipient.
At the receiving party, the data is acquired at one time through a wireless receiving moduletAn encrypted fragment
。
The preprocessing module is used for dividing each segment 
The following treatments were carried out:
(ii) a Then will betThe segments are linked to form a long vector
。
The measurement matrix reconstruction module utilizes a senderReconstructing a key commonly held by a receivertCorresponding random measurement matrix
. Will be provided withtThe individual measurement matrixes are combined in the following way to obtain an integrated measurement matrix
：
Wherein 0 ism×nThe zero matrix of (2).
The decryption decompression module is based on the existing reconstruction algorithm
Reconstructing sparse vector representation of original physiological signalθ. WhereinΨIs a sparse radical and is related to phi T Satisfying the limited Isometry Property (RIP).
The signal reconstruction module represents sparse vector of physiological signalθAccording to
Conversion to reconstructed physiological data
。
Drawings
Fig. 1 is an architecture diagram of a physiological signal safety compression system according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for safely compressing a physiological signal according to an embodiment of the present invention;
fig. 3 is an architecture diagram of a random measurement matrix generation module according to an embodiment of the present invention.
Detailed Description
The following is a specific embodiment of the present invention, taking an electrocardiographic signal (ECG) as an example, and further describes the technical solution of the present invention with reference to the accompanying drawings. It should be noted that terms used herein, such as measurement matrix, sparse representation, logisc mapping, LFSR seed, and signal reconstruction, are defined in the prior art and are not described herein again. The specific data involved, such as sampling rate, resolution, acquisition duration, compression rate, etc., are merely for the purpose of describing a specific algorithm in connection with a particular physiological signal instance. The physiological signals as described are not limited to cardiac electrical signals, but also include other physiological signals that may be acquired using a body area network, and are not intended to limit the exemplary embodiments disclosed in accordance with the present invention.
Fig. 1 is a block diagram of a system for safely compressing a physiological signal according to this embodiment, which includes a transmitting end and a receiving end, and is detailed as follows with reference to the accompanying drawings: the sending end comprises a signal acquisition module 11, a preprocessing module 12, a random measurement matrix generation module 13, an encryption compression module 14 and a wireless sending module 15; the receiving end comprises a wireless receiving module 21, a preprocessing module 22, a measurement matrix reconstruction module 23, a decryption decompression module 24 and a signal reconstruction module 25.
The signal acquisition module of the transmitting end is used for acquiring through the body surface sensortSecond, original signals are filtered; the preprocessing module is used for dividing the signal into short data segments with fixed length according to the signal period; the random measurement matrix generation module is used for generating a disposable random measurement matrix for each short data segment; the encryption compression module is used for multiplying each short data segment by using a random measurement matrix to obtain compressed and encrypted data of the physiological signal; and the wireless transmission module is used for transmitting the compressed and encrypted data to a receiving end in batches.
The wireless transmission module of the receiving end is used for obtainingtRemotely transmitted encrypted compressed data and combined into a vector; the measurement matrix reconstruction module is used for reconstructing tGenerating a random measurement matrix and an integrated measurement matrix; the decryption decompression module is used for reconstructing sparse vectors of the physiological data through a reconstruction algorithm by using the integrated measurement matrix; a reconstruction module for transforming the sparse vectorAnd (4) converting into reconstructed physiological data.
Fig. 2 is a flowchart of a method for safely compressing a physiological signal according to the present embodiment. The body area network end of the user is a sending end and sends the encrypted and compressed electrocardiosignals; and the server of the medical service center is a receiving end, acquires the user electrocardiosignals transmitted remotely and reconstructs the electrocardiosignals.
The specific process of the transmitting end is described as follows:
step S101: the signal acquisition module adopts a single-channel BMD101 electrocardio sensor to acquire 10-second electrocardio signals. The sampling rate is 512HZ and the resolution is 16 bits.
Step S102: the signal acquisition module carries out denoising processing on the digital electrocardiosignals acquired by the acquisition module. The frequency of electrocardiosignals detected by the body surface is in the range of 0.05 Hz-100 Hz, and most energy is in the range of 0.5 Hz-45 Hz. In order to remove interference such as power frequency interference, baseline drift, myoelectric interference and random noise in the acquisition process, a filter is adopted to remove 0.02 Hz-2 Hz baseline drift, 50Hz power frequency interference, high-frequency-band noise above 100Hz and myoelectric interference, and smooth and high-fidelity electrocardiosignals are obtained.
Step S103: the preprocessing module firstly detects the heartbeat period. A complete electrocardio cycle consists of a P wave, a QRS wave group and a T wave, and the heartbeat cycle detection algorithm can adopt but is not limited to a Pan-Tompkins algorithm which realizes the heartbeat cycle segmentation by detecting the R peak in the QRS wave group. One heartbeat cycle selects a data segment 200ms before the R peak and 300ms after the R peak for a total of 500 ms. With one data segment per 2 heartbeat cycles, there are a total of 10 data segments, 512 samples per data segment. Marking as
。
Step S104: the random measurement matrix generation module is used for generating a random measurement matrix for each data segmentiGenerating a disposable random measurement matrix, labeled
. Assuming a desired compression ratio of 0.5, the matrix size is 256 × 512. A total of 10 random measurement matrices are required.
The random measurement matrix generation module is shown in fig. 3, and includes a chaotic module, a linear shift feedback register (LFSR), and a clock module. The linear shift feedback register (LFSR) is a linear shift feedback register of lengthlControlled by an external clock, the register number being shifted to the right by one stage in each clock cycle, the output of which is determined by the feedback factor and the initial state L 0(referred to as the LFSR seed) is uniquely determined. LFSR generates one in each statelA random number of bits. Setting the feedback polynomial as the primitive polynomial, then the length islWill generate 2 l -1 random number. And converting the random sequence generated by the LFSR into a random Gaussian matrix. Due to the periodicity of the LFSR, i.e.L=2 l 1, over this period, the random number will repeat, thus adding to the chaotic module. The chaotic function may employ, without limitation, a one-dimensional logistic mapping. The one-dimensional logistic map is a chaotic map, the output of which depends on the state at the previous moment, and the mathematical formula is as follows:
whereinμ∈[0,4]Referred to as Logistic parameter;c n is the firstnThe state of the secondary iteration;c 0 ∈[0,1]it is the initial state of the one-dimensional logistic map. These two parameters are sent as keys to the receiving end through a secure channel so that the receiving end can reconstruct the random measurement matrix.
Step S104-1: for the first embodiment, selectl=16, setμAndc 0are respectively 16-bit, and are respectively provided with,μ=4，c 0= 0.32。
step S104-2: operation K1One-dimensional next to the logical mapping from random position K2Random selectionlA generated value
As a seed for LFSR, where K1And K2Is a positive integer.
Step S104-3: the LFSR generates a random sequence from the seed.
Step S104-4: the random sequence is converted into a random matrix, such as a random gaussian matrix or a bernoulli matrix, that satisfies the RIP attribute.
Step S105: the encryption compression module is used for performing operation on the data segment by using a random measurement matrix to obtain compressed and encrypted data; the calculation method is as follows:
step S106: and the wireless transmission module transmits the compressed and encrypted data to a receiving end 10 times at a time.
The specific process of the receiving end is described as follows:
step S201: the receiver obtains 10 encrypted segments through a wireless receiving moduleY i ，i=1, 2, ⋯,10。
Step S202: the preprocessing module is used for dividing each segmentY i The following treatments were carried out:
。
then 10 segments are linked to form a long vector
。
Step S203: the measurement matrix reconstruction module reconstructs 10 corresponding random measurement matrices by using a key commonly held by a sender and a receiverΦ i ，i=1,…,10。
Combining the 10 measurement matrixes in the following mode to obtain an integrated measurement matrix
：
Where 0 is a 256 x 512 zero matrix.
Step S204: the decryption decompression module is based on the existing reconstruction algorithm
Reconstructing sparse vector representation of original physiological signalθ. WhereinψIs a sparse radical, andΦ T satisfying the limited Isometry Property (RIP).
Step S205: the signal reconstruction module represents sparse vectors of physiological signals θAccording to
Conversion to physiological data
。
In summary, the secure compression technology adopts the short data segment with the fixed length at the sending end of the body area network to perform compression and encryption, thereby reducing the computational complexity; at the receiving end of the server end, the data segments are decrypted and decompressed at one time, so that the real-time performance is realized, and the method is suitable for body area network health medical application with limited computing resources. Meanwhile, the method only needs to share the secret key at the sending end and the receiving end, namely the initial state of the LFSR, namely the seed, and the chaotic module, only needs to add 32-bit secret key more, and almost has no pressure on the system. In addition, the adopted encryption mode is a mode of adopting one random measurement matrix for each data segment, and each measurement matrix is disposable and only related to a secret key of a random sequence; if the cipher key needs to be replaced, only the key needs to be replaced at the sending end and the receiving end, and the cipher text can be changed in an avalanche mode according to the characteristic that the chaotic module is sensitive to the initial state, so that the safety is greatly improved.
Claims (2)
1. A safe compression method of physiological signals suitable for a body area network is characterized by comprising an encryption compression algorithm and a decryption decompression algorithm, wherein the encryption compression algorithm realizes that the encryption compression of the physiological signals is carried out at the same time, and the decryption decompression algorithm realizes that the decryption and the decompression are completed at the same time;
The encryption compression algorithm is characterized by comprising the following steps:
step S101: collecting physiological signals with fixed length by adopting a sensor;
step S102: carrying out denoising processing on the acquired physiological signals;
step S103: dividing the denoised physiological signal into fixed lengthstA short data segment, the segmentation algorithm following a specific physiological signal period, the secondiA short data segment of a physiological signal is marked as
，i=1,2,…, t，nThe number of samples before the compression is not carried out;
step S104: is as followsiGenerating a short data segment of the physiological signaliAn encrypted random measurement matrix, marked
；
Step S105: is as followsiThe short data segment of the physiological signal is subjected to compressed and encrypted data operation, and the calculation method comprises the following steps:
，i=1,2,…, twhereinαAndβin order to be able to adjust the parameters,
in order to hide the matrix, the matrix is hidden,mis the number of samples after compression;
step S106: dividing the compressed encrypted data into batchesY i Transmitting to a receiving end;
the encrypted random measurement matrix of step S104 is characterized by comprising the following steps:
step S104-1: setting LFSR lengthlSetting parameters of the chaotic functionμAnd initial statec 0 ；
Step S104-2: operation K1Iterative secondary chaos from random position K2Random selectionlA generated value: (c 0 , c 1 ,…,c l-1 ) As a seed for LFSR, where K 1And K2Is a positive integer;
step S104-3: LFSR generates random sequence according to the seed;
step S104-4: converting the random sequence into a random matrix satisfying the RIP attribute, such as a random Gaussian matrix or a Bernoulli matrix;
the decryption decompression algorithm is characterized by comprising the following steps:
step S201: obtaining all encrypted segments at one timeY i ，i=1,2,⋯t；
Step S202: each data segment is divided intoY i The following treatments were carried out:
whereinα、β、Φ M The isoparametric definition is as described in step S105; then will betThe data segments are linked to form a long vector
；
Step S203: reconstruction using a key commonly held by a sender and a receivertCorresponding random measurement matrix is obtained bytThe individual measurement matrixes are combined in the following way to obtain an integrated measurement matrix
Wherein 0 ism×nA zero matrix of (c);
step S204: by means of an existing reconstruction algorithm, based on
Reconstructing sparse vector representation of original physiological signalθWhereinψIs an orthogonal rarefaction radical and isΦ T Satisfying finite equidistant properties (RIP);
step S205: representing sparse vectors of physiological signalsθAccording to
Conversion to reconstructed physiological data
。
2. A system for safely compressing physiological signals suitable for a body area network comprises a sending end and a receiving end, wherein the sending end is a body area network node and comprises a signal acquisition module, a preprocessing module, a random measurement matrix generation module, an encryption compression module and a wireless sending module, and the encryption compression module is used for operating an encryption compression algorithm according to claim 1; the receiving end is a medical institution server end and comprises a wireless receiving module, a preprocessing module, a measurement matrix reconstruction module, a decryption decompression module and a signal reconstruction module, wherein the wireless receiving module, the preprocessing module, the measurement matrix reconstruction module, the decryption decompression module and the signal reconstruction module are used for operating the decryption decompression algorithm according to claim 1.
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