CN112168176A - Identity recognition method, device and equipment based on electrocardiosignals - Google Patents
Identity recognition method, device and equipment based on electrocardiosignals Download PDFInfo
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Abstract
The invention discloses an identity recognition method, an identity recognition device, identity recognition equipment, a computer storage medium and computer equipment based on electrocardiosignals, wherein the identity recognition method comprises the following steps: receiving an electrocardiosignal to be identified of a user to be identified within a preset time, and performing heartbeat division on the electrocardiosignal to be identified; performing mode calculation on the electrocardiosignals to be identified divided by the heartbeat and acquiring a first amount of heartbeat data; performing data processing on the first amount of heart beat data, performing deviation calculation on the heart beat data and pre-stored basic mode data respectively, and acquiring a first amount of deviation distribution; and performing difference judgment on the deviation distribution of the first quantity and a pre-stored basic mode deviation distribution to verify the identity of the user to be identified. According to the embodiment provided by the invention, the speed of identity recognition can be increased and the accuracy of identity recognition can be improved by counting the deviation distribution according to the characteristic parameters and the waveform of the electrocardiosignal, and the electrocardiosignal has better anti-noise performance.
Description
Technical Field
The invention relates to the technical field of identity authentication and identification, in particular to an identity identification method, device, equipment, computer readable storage medium and computer equipment based on electrocardiosignals.
Background
The biometric identification technology is a technology for identifying an individual by using physiological signals or behavior characteristics of a human body. Physiological characteristics currently commercialized for identification include fingerprints, palm prints, veins, face shapes, irises, DNA, etc., and behavioral characteristics include signatures, voice, gait, etc.
There are many points in which the existing biometric technology needs improvement in practical use. For example, face recognition can be broken with twins or even photographs or videos, fingerprints can be forged with silica gel, sounds can be imitated or recorded, gait can be imitated, etc., all of which reduce security. In addition, although the safety and identification of DNA and iris are high, there are disadvantages of high cost and long recognition time, and iris technology cannot be used for blind persons and patients with eye diseases.
Therefore, there is a need to propose a new identification method for recognizing a biometric feature.
Disclosure of Invention
In order to solve at least one of the above problems, a first aspect of the present invention provides an identity recognition method based on an electrocardiographic signal, including:
receiving an electrocardiosignal to be identified of a user to be identified within a preset time, and performing heartbeat division on the electrocardiosignal to be identified;
performing mode calculation on the electrocardiosignals to be identified divided by the heartbeat and acquiring a first amount of heartbeat data;
performing data processing on the first amount of heart beat data, performing deviation calculation on the heart beat data and pre-stored basic mode data respectively, and acquiring a first amount of deviation distribution;
and performing difference judgment on the deviation distribution of the first quantity and a pre-stored basic mode deviation distribution to verify the identity of the user to be identified.
Further, before the receiving the to-be-identified electrocardiosignals of the to-be-identified user at the preset time and performing heartbeat division on the to-be-identified electrocardiosignals, the method further comprises the following steps:
collecting sample electrocardiosignals of a user and carrying out heart beat division;
and carrying out basic mode calculation on the sample electrocardiosignals divided by the heart beat, and acquiring and storing the basic mode data and basic mode deviation distribution.
Further, the step of performing a base pattern calculation on the sample electrocardiosignals divided by the heartbeat, and acquiring and storing the base pattern data and the base pattern deviation distribution includes:
acquiring a second amount of heartbeat data from the sample electrocardiosignals divided by the heartbeats;
interpolating the second amount of heart beat data into a data set of the same length using spline interpolation;
dividing the heartbeat data of the second data volume after spline interpolation into a third number of average heartbeat data and a fourth number of deviation heartbeat data, wherein the third number is larger than a preset heartbeat volume threshold value;
calculating a corresponding average voltage value according to the third amount of average heart beat data and taking the average voltage value as the basic mode data;
calculating an average offset voltage value according to the fourth amount of offset heartbeat data;
and taking the probability density of the average deviation voltage value and the deviation value calculated by the average voltage value as the deviation distribution of the basic mode.
Further, after the taking the probability density of the deviation value calculated by the average deviation voltage value and the average voltage value as the deviation distribution of the base pattern, the identity identification method further includes:
encrypting and storing the base mode data and the base mode deviation distribution;
before the performing data processing on the first amount of heartbeat data, performing deviation calculation respectively with pre-stored basic mode data, and obtaining a first amount of deviation distribution, the identity identification method further includes:
decrypting the encrypted base pattern data and base pattern bias distribution.
Further, the encrypting and storing the base pattern data and the base pattern bias distribution includes:
performing byte substitution on the base mode data and the base mode deviation distribution;
performing row shift substitution on the base mode data subjected to the byte substitution and the base mode deviation distribution;
performing column confusion on the basic mode data subjected to the row shift substitution and basic mode deviation distribution;
performing round key addition on the base mode data subjected to the row confusion substitution and the base mode deviation distribution;
and storing the base mode data and the base mode deviation distribution after the round key is added.
Further, the performing data processing on the first amount of heartbeat data, and performing deviation calculation respectively with pre-stored basic pattern data to obtain a first amount of deviation distribution includes:
interpolating the first amount of heart beat data into a data set of the same length as the base pattern data using spline interpolation;
respectively carrying out deviation calculation on the first amount of heart beat data after spline interpolation and pre-stored basic mode data to obtain a first amount of deviation;
the deviation distribution of each deviation is calculated.
Further, the differentially determining the first number of deviation distributions and a pre-stored basic pattern deviation distribution to verify the identity of the user to be identified includes:
detecting differences between each deviation distribution in the first quantity of deviation distributions and the deviation distribution of the base mode through a K-S detection algorithm and judging whether the differences meet deviation threshold values;
and if the ratio of the quantity meeting the deviation threshold value to the first data volume is greater than an identification threshold value, completing the identity authentication of the user to be identified, otherwise, failing to authenticate.
Furthermore, the lead mode of the electrocardio detection equipment for collecting the electrocardiosignals to be identified and/or the sample electrocardiosignals is one of single lead, three lead, five lead, seven lead, twelve lead or fifteen lead; and/or
The wearing mode of the electrocardio detection equipment for collecting the electrocardiosignals to be identified and/or the sample electrocardiosignals is one of a chest patch type, a finger pressing type or a wrist strap type; and/or
The sampling rate of the electrocardiosignals to be identified and/or the sample electrocardiosignals is more than or equal to 128Hz, and the voltage division value is less than or equal to 1 mv; and/or
The heart beat division method is one of a difference method, a band-pass filtering method or a wavelet transformation method.
The second aspect of the present invention provides an identity recognition apparatus based on electrocardiographic signals, comprising:
the heart beat dividing module is used for receiving the electrocardiosignals and carrying out heart beat division on the electrocardiosignals;
the mode calculation module is used for carrying out mode calculation on the electrocardiosignals divided by the heart beats and acquiring a preset amount of heart beat data;
the deviation calculation module is used for carrying out data processing on the predetermined amount of heart beat data, carrying out deviation calculation on the predetermined amount of heart beat data after the data processing and pre-stored basic mode data respectively, and obtaining a predetermined amount of deviation distribution;
and the difference judging module is used for performing difference judgment on the deviation distribution with the preset number and a pre-stored basic mode deviation distribution so as to verify the identity of the user to be identified.
Further, the method also comprises the following steps:
the basic mode calculation module is used for performing basic mode calculation on the electrocardiosignals divided by the heart beat, and acquiring and storing the basic mode data and basic mode deviation distribution;
the encryption module is used for encrypting the basic mode data and the basic mode deviation distribution;
and the decryption module is used for decrypting the encrypted base mode data and the base mode deviation distribution.
The third aspect of the invention provides identity recognition equipment based on electrocardiosignals, which comprises an electrocardio acquisition device and the identity recognition device of the second aspect; wherein
The electrocardiosignal acquisition device is used for acquiring electrocardiosignals of a user to be identified;
and the identity recognition device is used for recognizing according to the electrocardiosignals so as to verify the identity of the user to be recognized.
A fourth aspect of the invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method according to the first aspect.
A fifth aspect of the invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to the first aspect when executing the program.
The invention has the following beneficial effects:
aiming at the existing problems, the invention provides an identification method, a device, equipment, a computer storage medium and computer equipment based on electrocardiosignals, according to the characteristic parameters and the waveform of the electrocardiosignals, the identification speed can be improved, the identification accuracy can be improved and the anti-noise performance is better by counting the deviation distribution.
Drawings
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method for identification according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an electrocardiographic detection device in accordance with an embodiment of the present invention;
FIG. 3 shows a flow diagram of an encryption process according to one embodiment of the invention;
FIGS. 4a-4d are schematic diagrams illustrating signals in identification according to an embodiment of the present invention;
FIG. 5 is a block diagram of an identification device according to an embodiment of the invention;
FIG. 6 is a block diagram of an identification device according to another embodiment of the invention;
fig. 7 is a schematic structural diagram of a computer device according to another embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the invention, the invention is further described below with reference to preferred embodiments and the accompanying drawings. Similar parts in the figures are denoted by the same reference numerals. It is to be understood by persons skilled in the art that the following detailed description is illustrative and not restrictive, and is not to be taken as limiting the scope of the invention.
The Electrocardiogram (ECG) signal has periodicity, and the electrocardiogram curve of each period comprises a P wave, a QRS complex, a T wave and a U wave, and the basic wave bands comprise characteristic parameters such as R wave peak value, RR interval, S-T segment, P-R interval and the like, and the characteristic parameters reflect the contraction and relaxation states of the heart at different stages. Many factors such as individual heart position, size, structure, age, sex, weight and thorax structure all can influence electrocardiosignal, and simultaneously, electrocardiosignal possesses four important characteristics that identification needs: (1) universality: generating an ECG signal for each heart instant of the living subject; (2) uniqueness: the electrocardiosignals generated by different individuals have uniqueness; (3) stability: the heart structure and size of adult individuals are basically fixed, and the heart growth change speed of the immature individuals is similar to the body change rhythm and is basically unchanged in a time range spanning years and months. (4) Scalability: every hospital all has the electrocardio picture machine, and portable electrocardio check out test set has also been very common, and wrist strap and SMD equipment make the collection cost reduction of ECG, time shorten greatly more.
There are two broad categories of methods currently under investigation for identification using ECG signals, one based on feature points and one based on waveforms. The method for extracting the characteristic parameters based on the characteristic points can only utilize the characteristic parameters such as R wave peak value, RR interphase, S-T section, P-R interphase and the like in the electrocardiosignals, the characteristic parameters have no standard boundary definition, the characteristics cannot completely cover the information in the electrocardiosignals, and the extraction of excessive characteristics can increase the calculation complexity and the redundant data and processing time; therefore, there are problems of poor stability and high error rate. The waveform extraction-based method adopts methods such as autocorrelation coefficients, genetic algorithms, artificial neural networks and the like to classify waveforms, on one hand, the calculation complexity is high, on the other hand, the classification of single electrocardiosignals is difficult, the model interpretation is poor, and the waveform shape-based method depends on waveforms excessively, so that the influence of electrocardiosignal distortion is difficult to avoid, and particularly the influence of heart rate change is easy to realize.
To solve the above problem, as shown in fig. 1, an embodiment of the present invention provides an identity recognition method based on an electrocardiographic signal, including: receiving an electrocardiosignal to be identified of a user to be identified within a preset time, and performing heartbeat division on the electrocardiosignal to be identified; performing mode calculation on the electrocardiosignals to be identified divided by the heartbeat and acquiring a first amount of heartbeat data; performing data processing on the first amount of heart beat data, performing deviation calculation on the heart beat data and pre-stored basic mode data respectively, and acquiring a first amount of deviation distribution; and performing difference judgment on the deviation distribution of the first quantity and a pre-stored basic mode deviation distribution to verify the identity of the user to be identified.
The method comprises the steps that heart beat division is carried out on input electrocardiosignals to be recognized, and heart beat data are processed into identity characteristic patterns to be recognized through a pattern calculation algorithm; and extracting the stored basic mode data (namely the stored sample data) of the identity characteristic mode and the identity characteristic mode to be recognized to perform deviation distribution calculation, and finishing the identity verification of the user to be recognized through difference judgment according to the difference between the electrocardiosignals to be recognized and the prestored electrocardiosignals.
In an optional embodiment, before the receiving the to-be-identified cardiac electrical signal of the to-be-identified user at a preset time and performing heartbeat division on the to-be-identified cardiac electrical signal, the method further includes: collecting sample electrocardiosignals of a user and carrying out heart beat division; and carrying out basic mode calculation on the sample electrocardiosignals divided by the heart beat, and acquiring and storing the basic mode data and basic mode deviation distribution.
The identity recognition method also comprises an identity collection process, wherein before identity recognition, identity collection is carried out, for example, identity collection is carried out on a large number of known users so as to facilitate subsequent identity recognition, sample electrocardiosignals of the users are collected and subjected to heart beat division, heart beat data are processed into base mode data of an identity characteristic mode and base mode deviation distribution through a base mode calculation algorithm and are used as sample data of the identity recognition of corresponding users.
In one specific example, the identity collection is performed as follows:
first, an electrocardiographic signal is acquired using an electrocardiographic detection device.
The electrocardio detection equipment can adopt various existing electrocardio detection equipment, specifically, the sampling rate of the electrocardio detection equipment is more than or equal to 128Hz, the voltage division value is less than or equal to 1mv, and electrocardiosignals collected by the electrocardio detection equipment meeting the requirements can be used for identity verification. The lead mode of the electrocardio detection equipment can be one of single lead, three lead, five lead, seven lead, twelve lead or fifteen lead, and the wearing mode of the electrocardio detection equipment is one of chest patch type, finger pressing type or wrist strap type. In this embodiment, in order to ensure the acquisition quality of the electrocardiographic signals to the maximum extent, when performing electrocardiographic data acquisition, the user is required to perform electrocardiographic data acquisition in a state of stable emotion and rest, as shown in fig. 2, the electrocardiographic detection device used is a finger-pressing type detection device, the lead mode is a second lead mode, and the electrocardiographic signals of the user are acquired as sample electrocardiographic signals, which is relatively simple and stable, and can realize acquisition of the electrocardiographic signals of the user. Those skilled in the art should understand that, according to the actual application scenario, an appropriate electrocardiograph detection device and lead mode are selected to meet the requirement of acquiring electrocardiograph signals as a design criterion, which is not described herein again.
Secondly, sampling electrocardiosignals of the user and dividing the cardiac beat.
The electrocardiosignals acquired by the electrocardio detection equipment are continuous one-dimensional time sequence signals, and the electrocardiosignals are subjected to heartbeat data extraction for facilitating subsequent processing.
In this embodiment, a difference method (derivation) is used to perform determination by using the characteristic of a large R-wave amplitude and a high slope to realize beat division. Specifically, the acquired original electrocardiographic signals (ECG) are set as follows: { x (N) ═ 1,2, …, N }, the difference operator is as follows: y0[ n ] ═ x [ n ] -x [ n-2] |; y1[ n ] ═ x [ n ] -2 x [ n-2] + x [ n-4] |; y2[ n ] ═ 1.3y0[ n ] +1.1y1[ n ]; when y2[ n ] reaches or exceeds a preset threshold value of 0.6, that is, y2[ n ] > (0.6), a maximum value of x (n) is searched for in the vicinity of t ═ n, the maximum value is set as a single-point R value, data between two adjacent R values is set as a heartbeat cycle, and the data of the heartbeat cycle is heartbeat data. The difference method has the characteristics of simplicity and rapidness, the heart beat period can be automatically identified, and it is worth explaining that the traditional methods such as a band pass filter (Bandpass filter) and Wavelet transform (Wavelet transform) can also be used for automatically identifying the heart beat period of the acquired electrocardiosignals, and a person skilled in the art should select a proper identification algorithm according to an actual application scene, and details are not repeated herein.
And finally, performing basic mode calculation on the sample electrocardiosignals divided by the heart beat to acquire and store the basic mode data and basic mode deviation distribution.
The method specifically comprises the following steps:
first, a second amount of heartbeat data is collected from the sample electrocardiosignals divided by the heartbeats.
In this embodiment, 30s electrocardiographic signals are acquired, and 11 complete pieces of heartbeat data are randomly acquired from the electrocardiographic signals after heartbeat division.
And a second step of interpolating the second amount of heart beat data into a data set of the same length using spline interpolation.
In this embodiment, two RR points of the heartbeat data are used as alignment points, that is, positioning is performed according to the highest point of the R wave, and a cubic spline interpolation method is used to interpolate 11 pieces of heartbeat data into a data set with the same length.
And thirdly, dividing the second amount of heart beat data after spline interpolation into a third amount of average heart beat data and a fourth amount of deviation heart beat data, wherein the third amount is larger than a preset heart beat amount threshold value. I.e. a second number of heart beat data sets is divided into a first set of samples for finding the mean voltage value and a second set of samples for finding the mean deviation voltage value. In this embodiment, 10 of the 11 cardiac data sets are used as the first sample set for obtaining the average voltage value, and the other 1 is used as the second sample set for obtaining the average offset voltage value. When the number of samples for obtaining the average voltage value is larger than the preset heart beat number threshold, the first sample set can cover random deviation caused by the fluctuation of the electrocardiosignal so as to obtain a stable average value. In this embodiment, the threshold value of the number of heartbeats is 6, and the number of second sample sets for obtaining the average deviation voltage value is smaller than the number of first sample sets for obtaining the average voltage value.
And fourthly, calculating a corresponding average voltage value according to the third amount of average heart beat data and taking the average voltage value as the basic mode data.
In this embodiment, 10 heart beat data sets are randomly selected from 11 heart beat data sets, and the voltage values at corresponding times are averaged to obtain averaged data of one heart beat, that is, an average voltage value, as the base mode data of the electrocardiographic signal of the user.
And fifthly, calculating an average deviation voltage value according to the fourth quantity of deviation heart beat data.
And when the fourth number is larger than 1, calculating the average deviation voltage value according to the average voltage value acquisition method. In the present embodiment, the deviation calculation is directly performed using only 1 beat data as the deviation beat data.
And sixthly, taking the probability density of the deviation value calculated by the average deviation voltage value and the average voltage value as the deviation distribution of the basic mode.
In this embodiment, the average voltage value is subtracted from another 1 piece of cardiac beat data, and the probability density distribution of the obtained deviation value is calculated as the base mode deviation distribution of the electrocardiographic signal of the user.
In view of protection of the base pattern data and the base pattern deviation distribution of the electrocardiographic signal of the user, preventing data leakage or effectively resisting external attack, in an optional embodiment, after the calculating a probability density of deviation values according to the average deviation voltage value and the average voltage value as the base pattern deviation distribution, the identification method further includes: and encrypting and storing the base mode data and the base mode deviation distribution.
In an alternative embodiment, as shown in fig. 3, the encrypting and storing the base pattern data and the base pattern bias distribution includes: performing byte substitution on the base mode data and the base mode deviation distribution; performing row shift substitution on the base mode data subjected to the byte substitution and the base mode deviation distribution; performing column confusion on the basic mode data subjected to the row shift substitution and basic mode deviation distribution; performing round key addition on the base mode data subjected to the row confusion substitution and the base mode deviation distribution; and storing the base mode data and the base mode deviation distribution after the round key is added.
In this embodiment, the basic mode data and the basic mode bias distribution are encrypted by using the Rijndael Algorithm (AES) and then are stored securely.
The method comprises the following specific steps:
and step one, taking the basic mode data and the basic mode deviation distribution as plaintext input, and carrying out byte substitution on the plaintext. Namely, the byte-by-byte substitution in the grouping is completed by adopting an S box, wherein the S box is a pre-designed 16x16 lookup table, a substitution rule is defined in the lookup table, and the plaintext is subjected to byte substitution according to the substitution rule of the lookup table to form a byte substitution matrix.
And secondly, performing row shift substitution on the base mode data subjected to the byte substitution and the base mode deviation distribution. Namely, the row shift substitution is performed, specifically, the data in the matrix is circularly shifted according to the corresponding row number to form a row shift substitution matrix.
And thirdly, performing column confusion on the basic mode data subjected to the row shift substitution and the basic mode deviation distribution. The column obfuscation refers to performing independent operation on each column of data of the row shift substitution matrix, i.e. substitution of arithmetic features over the field GF (2^8), for example, mapping each byte of the first column to a new value, which is obtained by 4 bytes in the column through a preset function calculation, to form a column obfuscation matrix.
And fourthly, performing round key addition on the base mode data and the base mode deviation distribution after the row confusion substitution. I.e. bitwise exclusive-or (XOR) with the column confusion matrix and a part of the preset extended key.
And fifthly, storing the basic mode data and the basic mode deviation distribution after the round key is added. And finally, storing the encrypted base mode data and the base mode deviation distribution for subsequent identity recognition.
The Rijndael algorithm adopted in the embodiment is easy to realize, stable in performance, strong in key flexibility and high in safety, can effectively resist strong attack, difference and linear cryptanalysis, and has good safety and high operation efficiency; in addition, the Rijndael algorithm has clear principle and can be realized by a Crypto + +/Crypto open source library of C + +/Python. Based on the characteristics, the algorithm is suitable for encrypting the electrocardio-base mode data of the one-dimensional data stream.
And finishing the identity acquisition of the user. It should be noted that, in practical applications, the identity acquisition may be performed on a large number of users in advance, or may be performed on newly added users separately, so as to facilitate subsequent identity identification, for example, a function selection module is added, and the switching between the identity acquisition and the identity identification is realized by selecting a function, which is not described herein again.
After the identity acquisition of the user is finished, the identity recognition is carried out according to the received electrocardiosignals:
firstly, receiving an electrocardiosignal to be identified of a user to be identified within a preset time, and performing heartbeat division on the electrocardiosignal to be identified;
the specific steps are the same as the heart beat division in the identity acquisition, and are not described herein again.
Secondly, carrying out mode calculation on the electrocardiosignals to be identified divided by the heartbeat and collecting a first amount of heartbeat data.
In this embodiment, 30s electrocardiographic signals are acquired, and 5 complete pieces of heartbeat data are randomly acquired from the electrocardiographic signals after heartbeat division.
In view of the fact that the encryption operation is performed in the identity acquisition, in an optional embodiment, before performing the data processing on the first amount of heartbeat data and performing the deviation calculation respectively with the pre-stored base pattern data and obtaining the first amount of deviation distribution, the identity identification method further includes: decrypting the encrypted base pattern data and base pattern bias distribution.
In this embodiment, the Rijndael Algorithm (AES) is used for decryption to obtain the base pattern data and the base pattern bias distribution.
And thirdly, performing data processing on the first amount of heart beat data, performing deviation calculation on the heart beat data and pre-stored basic mode data respectively, and acquiring a first amount of deviation distribution.
The method specifically comprises the following steps:
interpolating the first amount of heart beat data into a data set of the same length as the base pattern data using spline interpolation.
In this embodiment, randomly acquired 5 complete pieces of heartbeat data are positioned according to the highest point of the R wave, two RR points are used as alignment points, and a cubic spline interpolation method is used to interpolate the 5 pieces of heartbeat data into a data set having the same length as the base mode data.
And respectively carrying out deviation calculation on the first amount of heart beat data after spline interpolation and pre-stored basic mode data to obtain a first amount of deviation.
In this embodiment, deviation calculation is performed on 5 pieces of heartbeat data subjected to spline difference and the base mode data, for example, a first piece of heartbeat data is subtracted from the base mode data to obtain a deviation of the first piece of heartbeat data, and similarly, deviations of 5 pieces of heartbeat data are obtained respectively.
The deviation distribution of each deviation is calculated.
In this embodiment, from the deviations of the 5 pieces of heartbeat data, a probability density distribution of each deviation, that is, a deviation distribution, is calculated.
And finally, performing difference judgment on the deviation distribution of the first quantity and a pre-stored basic mode deviation distribution to verify the identity of the user to be identified.
The method specifically comprises the following steps:
first, the difference between each deviation distribution in the first number of deviation distributions and the base mode deviation distribution is checked through a K-S checking algorithm, and whether the difference meets a deviation threshold value is judged.
In this embodiment, the difference between the deviation distribution of the 5 pieces of heartbeat data and the deviation distribution of the base mode is respectively checked by using a K-S checking algorithm. The K-S test algorithm (Kolmogorov-Smirnov test) is used to test whether the deviation distribution of the heartbeat data conforms to the deviation distribution of the base mode, that is, the K-S test algorithm is used to compare the deviation distribution f (x) of the heartbeat data with the difference D of the deviation distribution g (x) of the base mode, where D is max | f (x) -g (x) |, and if the difference D is greater than a preset difference threshold, the deviation distribution of the heartbeat data is considered to be different from the deviation distribution of the base mode, otherwise, the deviation distribution of the heartbeat data is considered to be the same. In this embodiment, the K-S test is used as a test method for determining the difference between the deviation distribution of the heartbeat data and the deviation distribution of the base pattern, and the difference between the deviation distribution of each heartbeat data and the deviation distribution of the base pattern is obtained. In the embodiment, compared with other test methods such as t test, the K-S test is a nonparametric test method, that is, the K-S test does not need to know the distribution of the deviation distribution of the heartbeat data.
Specifically, according to the deviation distribution of the 5 pieces of heart beat data, the difference between each deviation distribution and the base mode deviation distribution is checked through a K-S (K-S) checking algorithm, and whether each difference meets a deviation threshold value is judged. In this embodiment, the deviation threshold is 0.5, and the distribution determines whether each difference is smaller than the deviation threshold, and counts the number smaller than the deviation threshold.
And secondly, if the ratio of the number meeting the deviation threshold value to the first data volume is larger than an identification threshold value, completing the identity verification of the user to be identified, otherwise, failing to verify.
In this embodiment, if the K-S statistic of 3 or more deviation distributions and the basic pattern deviation distribution is less than 0.5, that is, more than 60% of deviation distributions are the same as the basic pattern deviation distribution, the electrocardiographic data to be authenticated and the basic pattern data are considered to be electrocardiographic data of the same individual, and the identity of the user to be identified passes the authentication; otherwise, the verification fails.
In an example of performing identity recognition by using an electrocardiographic signal, as shown in fig. 4a, a schematic diagram of stored base mode data is shown, as shown in fig. 4b, a schematic diagram of stored base mode deviation distribution is shown, as shown in fig. 4c, a schematic diagram of mode data of a user to be recognized is shown, as shown in fig. 4d, a schematic diagram of deviation distribution of mode data of a user to be recognized is shown, and after being tested by a K-S test algorithm, the K-S statistic is 0.91 and is greater than a preset deviation threshold, it is considered that the electrocardiographic data of the user to be recognized and the stored base mode data are electrocardiographic data of different individuals, and the identity verification of the user to be recognized fails. Therefore, the embodiment provided by the application can realize the identity recognition function through the electrocardiosignal and can overcome the problems existing in the existing identity recognition mode.
Corresponding to the identity recognition method provided in the foregoing embodiment, an embodiment of the present application further provides an identity recognition apparatus, and since the identity recognition apparatus provided in the embodiment of the present application corresponds to the identity recognition methods provided in the foregoing several embodiments, the foregoing embodiment is also applicable to the identity recognition apparatus provided in the embodiment, and is not described in detail in the embodiment.
As shown in fig. 5, an embodiment of the present application further provides an identification apparatus, including: the heart beat dividing module is used for receiving the electrocardiosignals and carrying out heart beat division on the electrocardiosignals; the mode calculation module is used for carrying out mode calculation on the electrocardiosignals divided by the heart beats and acquiring a preset amount of heart beat data; the deviation calculation module is used for carrying out data processing on the predetermined amount of heart beat data, carrying out deviation calculation on the predetermined amount of heart beat data after the data processing and pre-stored basic mode data respectively, and obtaining a predetermined amount of deviation distribution; and the difference judging module is used for performing difference judgment on the deviation distribution with the preset number and a pre-stored basic mode deviation distribution so as to verify the identity of the user to be identified.
As shown in fig. 6, in an optional embodiment, the identification apparatus further includes: the basic mode calculation module is used for performing basic mode calculation on the electrocardiosignals divided by the heart beat, and acquiring and storing the basic mode data and basic mode deviation distribution; the encryption module is used for encrypting the basic mode data and the basic mode deviation distribution; and the decryption module is used for decrypting the encrypted base mode data and the base mode deviation distribution.
An embodiment of the present application further provides an identification apparatus based on an electrocardiographic signal, which includes an electrocardiographic acquisition device and the identification device; the electrocardiosignal acquisition device is used for acquiring electrocardiosignals of a user to be identified, namely the electrocardiosignal acquisition device can acquire the electrocardiosignals, such as finger pressing type detection equipment shown in fig. 2; the identity recognition device is used for recognizing according to the electrocardiosignals of the user to be recognized so as to verify the identity of the user to be recognized. It should be noted that the identity recognition device may be an integrated structure or a split structure, and those skilled in the art should design the identity recognition device according to the actual application requirements to satisfy the identity recognition function as a design criterion, which is not described herein again.
Another embodiment of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements: receiving an electrocardiosignal to be identified of a user to be identified within a preset time, and performing heartbeat division on the electrocardiosignal to be identified; performing mode calculation on the electrocardiosignals to be identified divided by the heartbeat and acquiring a first amount of heartbeat data; performing data processing on the first amount of heart beat data, performing deviation calculation on the heart beat data and pre-stored basic mode data respectively, and acquiring a first amount of deviation distribution; and performing difference judgment on the deviation distribution of the first quantity and a pre-stored basic mode deviation distribution to verify the identity of the user to be identified.
In practice, the computer-readable storage medium may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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 present embodiment, 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.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
As shown in fig. 7, another embodiment of the present invention provides a schematic structural diagram of a computer device. The computer device 12 shown in fig. 7 is only an example and should not bring any limitations to the functionality or scope of use of the embodiments of the present invention.
As shown in FIG. 7, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 7, and commonly referred to as a "hard drive"). Although not shown in FIG. 7, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processor unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, implementing an identification method provided by an embodiment of the present invention.
Aiming at the existing problems, the invention provides an identification method, a system, computer equipment and a medium based on electrocardiosignals, and according to the characteristic parameters and the waveform of the electrocardiosignals, the calculation speed can be accelerated, the identification accuracy can be improved and the better anti-noise performance can be realized by counting the deviation distribution.
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention, and it will be obvious to those skilled in the art that other variations or modifications may be made on the basis of the above description, and all embodiments may not be exhaustive, and all obvious variations or modifications may be included within the scope of the present invention.
Claims (13)
1. An identity recognition method based on electrocardiosignals is characterized by comprising the following steps:
receiving an electrocardiosignal to be identified of a user to be identified within a preset time, and performing heartbeat division on the electrocardiosignal to be identified;
performing mode calculation on the electrocardiosignals to be identified divided by the heartbeat and acquiring a first amount of heartbeat data;
performing data processing on the first amount of heart beat data, performing deviation calculation on the heart beat data and pre-stored basic mode data respectively, and acquiring a first amount of deviation distribution;
and performing difference judgment on the deviation distribution of the first quantity and a pre-stored basic mode deviation distribution to verify the identity of the user to be identified.
2. The identity recognition method according to claim 1, wherein before the receiving the cardiac electrical signal to be recognized of the user to be recognized at the preset time and performing heartbeat division on the cardiac electrical signal to be recognized, the method further comprises:
collecting sample electrocardiosignals of a user and carrying out heart beat division;
and carrying out basic mode calculation on the sample electrocardiosignals divided by the heart beat, and acquiring and storing the basic mode data and basic mode deviation distribution.
3. The method of claim 2, wherein the performing a base pattern calculation on the cardiac signal of the sample divided by the heartbeat, and the obtaining and storing the base pattern data and the base pattern deviation distribution comprises:
acquiring a second amount of heartbeat data from the sample electrocardiosignals divided by the heartbeats;
interpolating the second amount of heart beat data into a data set of the same length using spline interpolation;
dividing the heartbeat data of the second data volume after spline interpolation into a third number of average heartbeat data and a fourth number of deviation heartbeat data, wherein the third number is larger than a preset heartbeat volume threshold value;
calculating a corresponding average voltage value according to the third amount of average heart beat data and taking the average voltage value as the basic mode data;
calculating an average offset voltage value according to the fourth amount of offset heartbeat data;
and taking the probability density of the average deviation voltage value and the deviation value calculated by the average voltage value as the deviation distribution of the basic mode.
4. The identification method according to claim 3,
after the taking the average deviation voltage value and the probability density of the deviation value calculated by the average voltage value as the deviation distribution of the base mode, the identity identification method further comprises the following steps:
encrypting and storing the base mode data and the base mode deviation distribution;
before the performing data processing on the first amount of heartbeat data, performing deviation calculation respectively with pre-stored basic mode data, and obtaining a first amount of deviation distribution, the identity identification method further includes:
decrypting the encrypted base pattern data and base pattern bias distribution.
5. The method of claim 4, wherein the encrypting and storing the base pattern data and base pattern bias distributions comprises:
performing byte substitution on the base mode data and the base mode deviation distribution;
performing row shift substitution on the base mode data subjected to the byte substitution and the base mode deviation distribution;
performing column confusion on the basic mode data subjected to the row shift substitution and basic mode deviation distribution;
performing round key addition on the base mode data subjected to the row confusion substitution and the base mode deviation distribution;
and storing the base mode data and the base mode deviation distribution after the round key is added.
6. The identity recognition method according to any one of claims 2 to 5,
the step of performing data processing on the first amount of heartbeat data, and performing deviation calculation respectively on the first amount of heartbeat data and pre-stored basic mode data to obtain a first amount of deviation distribution includes:
interpolating the first amount of heart beat data into a data set of the same length as the base pattern data using spline interpolation;
respectively carrying out deviation calculation on the first amount of heart beat data after spline interpolation and pre-stored basic mode data to obtain a first amount of deviation;
the deviation distribution of each deviation is calculated.
7. The identification method according to claim 6,
the differentially determining the first number of deviation distributions and a pre-stored basic pattern deviation distribution to verify the identity of the user to be identified includes:
detecting differences between each deviation distribution in the first quantity of deviation distributions and the deviation distribution of the base mode through a K-S detection algorithm and judging whether the differences meet deviation threshold values;
and if the ratio of the quantity meeting the deviation threshold value to the first data volume is greater than an identification threshold value, completing the identity authentication of the user to be identified, otherwise, failing to authenticate.
8. The identification method according to claim 7,
the lead mode of the electrocardio detection equipment for collecting the electrocardiosignals to be identified and/or the sample electrocardiosignals is one of single lead, three lead, five lead, seven lead, twelve lead or fifteen lead; and/or
The wearing mode of the electrocardio detection equipment for collecting the electrocardiosignals to be identified and/or the sample electrocardiosignals is one of a chest patch type, a finger pressing type or a wrist strap type; and/or
The sampling rate of the electrocardiosignals to be identified and/or the sample electrocardiosignals is more than or equal to 128Hz, and the voltage division value is less than or equal to 1 mv; and/or
The heart beat division method is one of a difference method, a band-pass filtering method or a wavelet transformation method.
9. An identification device based on electrocardiosignals, which is characterized by comprising:
the heart beat dividing module is used for receiving the electrocardiosignals and carrying out heart beat division on the electrocardiosignals;
the mode calculation module is used for carrying out mode calculation on the electrocardiosignals divided by the heart beats and acquiring a preset amount of heart beat data;
the deviation calculation module is used for carrying out data processing on the predetermined amount of heart beat data, carrying out deviation calculation on the predetermined amount of heart beat data after the data processing and pre-stored basic mode data respectively, and obtaining a predetermined amount of deviation distribution;
and the difference judging module is used for performing difference judgment on the deviation distribution with the preset number and a pre-stored basic mode deviation distribution so as to verify the identity of the user to be identified.
10. The identification device of claim 9, further comprising:
the basic mode calculation module is used for performing basic mode calculation on the electrocardiosignals divided by the heart beat, and acquiring and storing the basic mode data and basic mode deviation distribution;
the encryption module is used for encrypting the basic mode data and the basic mode deviation distribution;
and the decryption module is used for decrypting the encrypted base mode data and the base mode deviation distribution.
11. An identification device based on electrocardiosignals, which is characterized by comprising an electrocardiosignal acquisition device and an identification device as claimed in claim 9 or 10, wherein
The electrocardiosignal acquisition device is used for acquiring electrocardiosignals of a user to be identified;
and the identity recognition device is used for recognizing according to the electrocardiosignals so as to verify the identity of the user to be recognized.
12. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-8.
13. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1-8 when executing the program.
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