CN112168176B - Electrocardiosignal-based identity recognition method, device and equipment - Google Patents
Electrocardiosignal-based identity recognition method, device and equipment Download PDFInfo
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Abstract
The invention discloses an electrocardiosignal-based identity recognition method, an electrocardiosignal-based identity recognition device, equipment, a computer storage medium and computer equipment, wherein the identity recognition method comprises the following steps: receiving an electrocardiosignal to be identified of a user to be identified at preset time and performing heart beat division on the electrocardiosignal to be identified; carrying out mode calculation on the electrocardiosignals to be identified divided by the heart beats and collecting heart beat data of a first quantity; performing data processing on the first quantity of heart beat data, performing deviation calculation on the first quantity of heart beat data and the pre-stored base mode data respectively, and obtaining a first quantity of deviation distribution; and performing difference judgment on the first number of deviation distribution and the prestored basic mode deviation distribution to verify the identity of the user to be identified. According to the characteristic parameters and waveforms of the electrocardiosignal, the embodiment provided by the invention can improve the speed of identity identification, improve the accuracy of the identity identification and has better noise resistance through statistical deviation distribution.
Description
Technical Field
The present invention relates to the field of identity authentication and identification technologies, and in particular, to an electrocardiosignal-based identity identification method, apparatus, device, computer readable storage medium, and computer device.
Background
The biometric identification technology refers to a technology for personal identification by using physiological signals or behavioral characteristics of a human body. Physiological features currently commercialized for identification include fingerprints, palmprints, veins, facial forms, irises, DNA, etc., and behavioral features include signatures, voices, gait, etc.
The existing biological feature recognition technology has a plurality of points which need to be improved in actual use. For example, face recognition can be broken by twins or even photographs or videos, fingerprints can be forged by silica gel, sounds can be imitated or recorded, gait can be imitated, etc., which all reduce security. In addition, although DNA and iris have high safety and identification, they have drawbacks of high cost and long identification time, and iris technology cannot be used for blind persons and eye disease patients.
Therefore, there is a need to propose a new identification method for identifying biological features.
Disclosure of Invention
In order to solve at least one of the above problems, a first aspect of the present invention provides an identification method based on electrocardiographic signals, including:
receiving an electrocardiosignal to be identified of a user to be identified at preset time and performing heart beat division on the electrocardiosignal to be identified;
Carrying out mode calculation on the electrocardiosignals to be identified divided by the heart beats and collecting heart beat data of a first quantity;
performing data processing on the first quantity of heart beat data, performing deviation calculation on the first quantity of heart beat data and the pre-stored base mode data respectively, and obtaining a first quantity of deviation distribution;
and performing difference judgment on the first number of deviation distribution and the prestored basic mode deviation distribution to verify the identity of the user to be identified.
Further, before the receiving the electrocardiosignal to be identified of the user to be identified in the preset time and performing heart beat division on the electrocardiosignal to be identified, the method further comprises:
collecting a sample electrocardiosignal of a user and performing heart beat division;
and performing base mode calculation on the sample electrocardiosignals divided by the heart beat, and acquiring and storing the base mode data and the base mode deviation distribution.
Further, the performing a base mode calculation on the sample electrocardiographic signal divided by the heartbeat, and obtaining and storing the base mode data and the base mode deviation distribution includes:
collecting a second number of cardiac beat data from the cardiac signal of the sample divided by the cardiac beats;
interpolating the second number of beat data into a data set of the same length using spline interpolation;
Dividing the heart beat data of the second data quantity after spline interpolation into a third quantity of average heart beat data and a fourth quantity of deviation heart beat data, wherein the third quantity is larger than a preset heart beat quantity threshold value;
calculating a corresponding average voltage value according to the third number of average heart beat data and taking the average voltage value as the base mode data;
calculating an average deviation voltage value according to the fourth number of deviation heartbeat data;
and taking the probability density of the deviation value calculated by the average deviation voltage value and the average voltage value as the base mode deviation distribution.
Further, after the probability density of the deviation value calculated by the average deviation voltage value and the average voltage value is used as the base pattern deviation distribution, the identification method further includes:
encrypting and storing the base pattern data and the base pattern deviation distribution;
before the data processing is performed on the first number of heart beat data, the deviation calculation is performed on the first number of heart beat data and the pre-stored base pattern data respectively, and the first number of deviation distribution is obtained, the identification method further comprises the steps of:
decrypting the encrypted base pattern data and base pattern deviation distribution.
Further, encrypting and storing the base pattern data and the base pattern deviation distribution includes:
performing byte substitution on the base pattern data and the base pattern deviation distribution;
performing line shift substitution on the base pattern data and the base pattern deviation distribution after the byte substitution;
column confusion is carried out on the base pattern data and the base pattern deviation distribution after the line shift substitution;
performing round key addition on the base mode data and the base mode deviation distribution after the column confusion substitution;
and storing the base pattern data and the base pattern deviation distribution after the round key addition.
Further, the performing data processing on the first number of heart beat data, performing deviation calculation on the first number of heart beat data and the pre-stored base mode data respectively, and obtaining a first number of deviation distribution includes:
interpolating the first number of beat data into a dataset of the same length as the base pattern data using spline interpolation;
respectively carrying out deviation calculation on the first quantity of heart beat data subjected to spline interpolation and pre-stored basic mode data to obtain first quantity of deviation;
and calculating the deviation distribution of each deviation respectively.
Further, the performing the difference determination on the first number of deviation distributions and the pre-stored base pattern deviation distribution to verify the identity of the user to be identified includes:
checking the difference between each deviation distribution in the first number of deviation distributions and the base mode deviation distribution through a K-S checking algorithm and judging whether the difference meets a deviation threshold value;
and if the number meeting the deviation threshold and the ratio of the first data volume are larger than the identification threshold, completing the authentication of the user to be identified, otherwise, failing the authentication.
Further, the lead mode of the electrocardio-signal detection equipment for collecting the electrocardio-signal to be identified and/or the sample electrocardio-signal is one of single lead, three lead, five lead, seven lead, twelve lead or fifteen lead; and/or
The wearing mode of the electrocardiograph detection equipment for collecting the electrocardiograph signals to be identified and/or the sample electrocardiograph signals is one of chest-mounted type, finger-pressed type or wrist-strap type; and/or
Collecting the electrocardiosignals to be identified and/or the sample electrocardiosignals, wherein the sampling rate of the electrocardiosignals to be identified and/or the sample electrocardiosignals is greater than or equal to 128Hz, and the voltage division value is less than or equal to 1mv; and/or
The heart beat dividing method is one of a difference method, a band-pass filtering method or a wavelet transformation method.
A second aspect of the present invention provides an electrocardiographic signal-based identification device, including:
the heart beat dividing module is used for receiving the electrocardiosignals and dividing the electrocardiosignals into heart beats;
the mode calculation module is used for carrying out mode calculation on the electrocardiosignals divided by the heart beats and collecting a preset number of heart beat data;
the deviation calculation module is used for carrying out data processing on the preset number of heart beat data, carrying out deviation calculation on the preset number of heart beat data after the data processing and the pre-stored basic mode data respectively, and obtaining preset number of deviation distribution;
and the difference judging module is used for carrying out difference judgment on the preset number of deviation distribution and the prestored base mode deviation distribution so as to verify the identity of the user to be identified.
Further, the method further comprises the following steps:
the base mode calculation module is used for carrying out base mode calculation on the electrocardiosignals divided by the heart beat, and acquiring and storing the base mode data and the base mode deviation distribution;
the encryption module is used for encrypting the base mode data and the base 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 an identification device based on electrocardiosignals, which comprises an electrocardiosignal acquisition device and the identification device in the second aspect; wherein the method comprises the steps of
The electrocardio acquisition device is used for acquiring electrocardio signals of a user to be identified;
the identity recognition device is used for recognizing according to the electrocardiosignal 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, implements a method as described in 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 beneficial effects of the invention are as follows:
aiming at the existing problems at present, the invention establishes an identification method, a device, equipment, a computer storage medium and computer equipment based on electrocardiosignals, and can improve the identification speed, the identification accuracy and the anti-noise performance by counting deviation distribution according to the characteristic parameters and waveforms of the electrocardiosignals.
Drawings
The following describes the embodiments of the present invention in further detail with reference to the drawings.
FIG. 1 shows a flow chart of a method of identification according to an embodiment of the invention;
FIG. 2 shows a schematic diagram of an electrocardiographic detection device according to an embodiment of the present invention;
FIG. 3 shows a flow chart of an encryption process according to an embodiment of the invention;
FIGS. 4a-4d are schematic diagrams of signals in identification according to one embodiment of the invention;
FIG. 5 is a block diagram illustrating an identification device according to an embodiment of the present invention;
FIG. 6 is a block diagram illustrating an identification device according to another embodiment of the present 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 present invention, the present invention will be further described with reference to preferred embodiments and the accompanying drawings. Like parts in the drawings 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 that this invention is not limited to the details given herein.
The Electrocardiograph (ECG) signals have periodicity, and each periodic electrocardiograph curve comprises a P wave, a QRS wave group, 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 section, P-R interval and the like, and the characteristic parameters reflect the contraction and relaxation states of the heart at different stages. The electrocardiosignals can be influenced by various factors such as heart position, size, structure, age, sex, weight, thoracic cavity structure and the like of an individual, and meanwhile, the electrocardiosignals have four important characteristics required by identity recognition: (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 of an adult individual is of a substantially fixed configuration and size, and the rate of change of heart growth of an underadult individual is similar to the rate of change of body rhythm and is substantially constant over a time span of years and months. (4) scalability: electrocardiographs are available in all hospitals, portable electrocardiograph detection equipment is also very common, and wrist straps and patch type equipment further enable the acquisition cost of ECG to be reduced and the time to be greatly shortened.
At present, two main methods for carrying out identity recognition by using ECG signals are available, one is based on characteristic points, and the other is based on waveforms. The method for extracting the characteristic parameters based on the characteristic points only can utilize the characteristic parameters such as R wave peak value, RR interval, S-T section, P-R interval and the like in the electrocardiosignal, the characteristic parameters have no standard boundary definition, the characteristic cannot completely cover the information in the electrocardiosignal, and extracting excessive characteristics increases the computational complexity and increases redundant data and processing time; therefore, there are problems of poor stability and high error rate. The method based on waveform extraction adopts methods such as autocorrelation coefficients, genetic algorithms, artificial neural networks and the like to classify waveforms, so that on one hand, the computational complexity is high, on the other hand, the classification of single electrocardiosignals is difficult, the interpretation of a model is poor, and the method based on waveform shape is excessively dependent on waveforms and is not influenced by electrocardiosignal distortion, in particular, the influence of heart rate variation is easy.
In order to solve the above problems, as shown in fig. 1, an embodiment of the present invention provides an identification method based on electrocardiographic signals, including: receiving an electrocardiosignal to be identified of a user to be identified at preset time and performing heart beat division on the electrocardiosignal to be identified; carrying out mode calculation on the electrocardiosignals to be identified divided by the heart beats and collecting heart beat data of a first quantity; performing data processing on the first quantity of heart beat data, performing deviation calculation on the first quantity of heart beat data and the pre-stored base mode data respectively, and obtaining a first quantity of deviation distribution; and performing difference judgment on the first number of deviation distribution and the prestored basic mode deviation distribution to verify the identity of the user to be identified.
Performing heart beat division on the input electrocardiosignals to be identified, and processing heart beat data into an identity characteristic mode to be identified through a mode calculation algorithm; and extracting the basic mode data (namely the stored sample data) of the stored identity characteristic mode, performing deviation distribution calculation on the stored basic mode data (namely the stored sample data) and the identity characteristic mode to be identified, and completing the identity verification of the user to be identified through the difference judgment according to the difference between the electrocardiosignal to be identified and the prestored electrocardiosignal.
In an alternative embodiment, before the receiving the electrocardiographic signal to be identified of the preset time of the user to be identified and performing heart beat division on the electrocardiographic signal to be identified, the method further includes: collecting a sample electrocardiosignal of a user and performing heart beat division; and performing base mode calculation on the sample electrocardiosignals divided by the heart beat, and acquiring and storing the base mode data and the base mode deviation distribution.
The identity recognition method further comprises an identity acquisition process, wherein identity acquisition is performed before identity recognition, for example, a large number of known users are subjected to identity acquisition so as to facilitate subsequent identity recognition, sample electrocardiosignals of the users are acquired, heart beat division is performed, 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 the base mode data are used as sample data of identity recognition of corresponding users.
In a specific example, identity acquisition 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 the electrocardio signals acquired by the electrocardio detection equipment meeting the requirements can be used for identity verification. The lead mode of the electrocardiograph detection device can be one of a single lead, a three lead, a five lead, a seven lead, a twelve lead or a fifteen lead, and the wearing mode of the electrocardiograph detection device is one of chest-mounted type, finger-pressed type or wrist-belt type. In this embodiment, in order to ensure the collection quality of the electrocardiograph signal to the greatest extent, the user is required to collect electrocardiograph data in a state of stable emotion and rest when collecting electrocardiograph data, as shown in fig. 2, the electrocardiograph detection device is a finger push type detection device, the lead mode is a second lead mode, and the electrocardiograph signal of the user is collected as a sample electrocardiograph signal, and the mode is relatively simple and stable, so that the collection of the electrocardiograph signal of the user can be realized. Those skilled in the art should understand that, according to the actual application scenario, an appropriate electrocardiograph detection device and a lead manner are selected to meet the collection of electrocardiograph signals as a design criterion, which is not described herein.
Secondly, collecting a sample electrocardiosignal of a user and performing heart beat division.
The electrocardiosignals acquired by the electrocardiosignal detection equipment are continuous one-dimensional time sequence signals, and the electrocardiosignals are subjected to heart beat data extraction for facilitating subsequent processing.
In this embodiment, a differential method (differential) is adopted, and the characteristics of large R-wave amplitude and high slope are used to perform judgment so as to realize beat division. Specifically, let the acquired raw electrocardiographic signals (ECG) be: { x (N), n=1, 2, …, N }, the difference operator is as follows: y0[ n ] = |x [ n ] -x [ n-2] |; y1[ n ] = |x [ n ] -2xn-2 ] +xn-4|; y2n=1.3y0n+1.1y1n; when y2[ n ] reaches or exceeds a preset threshold value of 0.6, namely y2[ n ] > = 0.6, searching for a maximum value of x (n) near t=n, setting the maximum value as a single-point R value, and setting data between two adjacent R values as a heart beat period, wherein the data of the heart beat period is heart beat data. The difference method has the characteristics of simplicity and rapidness, can automatically identify the heart beat period, and is worth noting that the traditional methods such as a band-pass filter method (Bandpass filter) and wavelet transform (Wavelet Transfrom) can also realize the automatic identification of the heart beat period of the collected electrocardiosignal, and a person skilled in the art should select a proper identification algorithm according to the actual application scene, so that the description is omitted.
And finally, performing base mode calculation on the sample electrocardiosignals divided by the heart beat, and acquiring and storing the base mode data and the base mode deviation distribution.
The method specifically comprises the following steps:
first, a second amount of cardiac beat data is acquired from the sample cardiac signal divided by the cardiac beat.
In this embodiment, 30s electrocardiographic signals are collected, and after being divided by heart beats, 11 pieces of complete heart beat data are randomly obtained from the electrocardiographic signals.
Second, the second number of beat data is interpolated into a data set of the same length using spline interpolation.
In this embodiment, two RR points of the beat data are used as alignment points, that is, the positioning is performed according to the highest point of the R wave, and 11 beat data are interpolated into a data set with the same length by using a cubic spline interpolation method.
And thirdly, dividing the second quantity of heart beat data subjected to spline interpolation into a third quantity of average heart beat data and a fourth quantity of deviation heart beat data, wherein the third quantity is larger than a preset heart beat quantity threshold value. I.e. the second number of heart beat data sets is divided into a first sample set for taking average voltage values and a second sample set for taking average offset voltage values. In the present embodiment, 10 of the 11 beat data sets are used as a first sample set for calculating the average voltage value, and the other 1 is used as a second sample set for calculating the average offset voltage value. When the number of samples used for calculating the average voltage value is larger than a preset heart beat number threshold value, the first sample set can cover random deviation caused by fluctuation of electrocardiosignals so as to obtain a stable average value. In this embodiment, the heart beat number threshold is 6, and the number of second sample sets for calculating the average deviation voltage value is smaller than the number of first sample sets for calculating the average voltage value.
And step four, calculating a corresponding average voltage value according to the third number of average heart beat data and taking the average voltage value as the base mode data.
In this embodiment, 10 pieces of heart beat data are randomly selected from the 11 pieces of heart beat data sets, and the voltage values at corresponding moments are averaged to obtain the averaged data of one heart beat, that is, the average voltage value is used as the base mode data of the electrocardiograph signal of the user.
And fifthly, calculating an average deviation voltage value according to the deviation heartbeat data of the fourth quantity.
When the fourth number is greater than 1, the average deviation voltage value is calculated according to the average voltage value acquisition method. In the present embodiment, only 1 beat data is used as the deviation beat data, and the deviation calculation is directly performed.
And a sixth step of taking the probability density of the deviation value calculated by the average deviation voltage value and the average voltage value as the base mode deviation distribution.
In this embodiment, the probability density distribution of the obtained deviation value is calculated by subtracting the average voltage value from the other 1 cardiac beat data, and is used as the base pattern 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 signals of the user, preventing data from leaking out or for effective external attack resistance, in an alternative embodiment, after the calculating of the 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 comprises: and encrypting and storing the base pattern data and the base pattern deviation distribution.
In an alternative embodiment, as shown in fig. 3, the encrypting and storing the base pattern data and the base pattern deviation profile includes: performing byte substitution on the base pattern data and the base pattern deviation distribution; performing line shift substitution on the base pattern data and the base pattern deviation distribution after the byte substitution; column confusion is carried out on the base pattern data and the base pattern deviation distribution after the line shift substitution; performing round key addition on the base mode data and the base mode deviation distribution after the column confusion substitution; and storing the base pattern data and the base pattern deviation distribution after the round key addition.
In this embodiment, the base pattern data and the base pattern deviation distribution are encrypted by using Rijndael Algorithm (AES) and then stored securely.
The method comprises the following specific steps:
and the first step is to take the base pattern data and the base pattern deviation distribution as plaintext input and perform byte substitution on the plaintext. The substitution according to bytes 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 step two, performing row shift substitution on the base pattern data and the base pattern deviation distribution after the byte substitution. Namely, the line shift substitution is specifically represented by circularly shifting the data in the matrix according to the corresponding line number to form a line shift substitution matrix.
And thirdly, performing column confusion on the base pattern data and the base pattern deviation distribution after the line shift substitution. Wherein, the column confusion refers to the independent operation of each column of data of the row shift substitution matrix, that is, the substitution of the arithmetic feature on the field GF (2^8), for example, each byte of the first column is mapped into a new value, and the new value is obtained by calculating 4 bytes in the column through a preset function, so as to form the column confusion matrix.
And step four, carrying out round key addition on the base pattern data and the base pattern deviation distribution after the column confusion substitution. I.e. bitwise exclusive or (XOR) with the column confusion matrix and a part of the preset spreading key.
And fifthly, storing the base mode data and the base mode deviation distribution after the round key addition. And finally, storing the encrypted base pattern data and the base pattern deviation distribution for subsequent identification.
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, differential and linear password analysis, and has good safety and high operation efficiency; and the Rijndael algorithm principle is clear and can be realized through a Crypto++/Crypto open source library of C++/Python. Based on the above characteristics, the algorithm is adapted to encrypt the electrocardiographic base pattern data of the one-dimensional data stream.
Thus, the identity acquisition of the user is completed. It should be noted that in practical application, a large number of users may be collected in a centralized manner in advance, or a new user may be collected separately, so that subsequent identity recognition may be facilitated, for example, a function selection module is added, and the identity collection and the identity recognition are switched through a selection function, which is not described herein.
After the identity acquisition of the user is completed, carrying out identity identification according to the received electrocardiosignal:
firstly, receiving an electrocardiosignal to be identified of a user to be identified at preset time and performing heart beat 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 repeated here.
And secondly, carrying out mode calculation on the electrocardiosignals to be identified which are divided by the heart beats and collecting heart beat data of a first quantity.
In this embodiment, 30s electrocardiographic signals are collected, and after being divided by heart beats, 5 complete heart beat data are randomly obtained from the electrocardiographic signals.
In an alternative embodiment, before the data processing is performed on the first number of heart beat data and the deviation calculation is performed on the first number of heart beat data and the pre-stored base mode data respectively, and the first number of deviation distributions are obtained, the identification method further includes: decrypting the encrypted base pattern data and base pattern deviation distribution.
In this embodiment, the base pattern data and the base pattern deviation distribution are obtained by decrypting using the Rijndael Algorithm (AES).
And thirdly, carrying out data processing on the first quantity of heart beat data, respectively carrying out deviation calculation on the first quantity of heart beat data and the pre-stored base pattern data, and obtaining a first quantity of deviation distribution.
The method specifically comprises the following steps:
the first number of beat data is interpolated to a dataset of the same length as the base pattern data using spline interpolation.
In this embodiment, 5 pieces of complete beat data obtained randomly 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 adopted to interpolate the 5 pieces of beat data into a data set with the same length as the base mode data.
And respectively carrying out deviation calculation on the first quantity of heart beat data subjected to spline interpolation and the prestored base mode data to obtain first quantity of deviation.
In this embodiment, deviation calculation is performed on the 5 beat data of the spline difference and the base pattern data, for example, the deviation of the first beat data is obtained by subtracting the first beat data from the base pattern data, and similarly, the deviation of the 5 beat data is obtained.
And calculating the deviation distribution of each deviation respectively.
In the present embodiment, the probability density distribution of each deviation, that is, the deviation distribution is calculated from the deviations of the 5 pieces of heartbeat data.
And finally, performing difference judgment on the first number of deviation distribution and the prestored base mode deviation distribution to verify the identity of the user to be identified.
The method specifically comprises the following steps:
first, checking the difference between each deviation distribution in the first number of deviation distributions and the base mode deviation distribution through a K-S checking algorithm and judging whether the difference meets a deviation threshold value.
In this embodiment, the difference between the deviation distribution of the 5 beat data and the base pattern deviation distribution is checked by using a K-S checking algorithm, respectively. The K-S test algorithm (Kolmogorov-Smirnov test) is used for testing whether the deviation distribution of the heart beat data accords with the basic mode deviation distribution, namely, the K-S test algorithm is used for comparing the difference D of the deviation distribution f (x) of the heart beat data and the basic mode deviation distribution g (x), D=max|f (x) -g (x) |, if the difference D is larger than a preset difference threshold value, the deviation distribution of the heart beat data is considered to be different from the basic mode deviation distribution, and otherwise, the deviation distribution is considered to be the same. The K-S test adopted in this embodiment is used as a test method for determining the difference between the deviation distribution of the beat data and the base pattern deviation distribution, and the difference between the deviation distribution of each beat data and the base pattern deviation distribution is obtained. In this embodiment, the K-S test is a non-parametric test method, i.e., the K-S test does not need to know the distribution of the deviation distribution of the beat data, compared to other test methods such as t-test.
Specifically, according to deviation distribution of the 5 heart beat data, the difference between each deviation distribution and the base mode deviation distribution is checked through a K-S checking algorithm, and whether each difference meets a deviation threshold value is judged. The deviation threshold value of this embodiment is 0.5, and the distribution judges whether each difference is smaller than the deviation threshold value, and counts the number smaller than the deviation threshold value.
And step two, if the number meeting the deviation threshold and the duty ratio of the first data volume are larger than the recognition threshold, completing the identity verification of the user to be recognized, otherwise, failing the verification.
In this embodiment, if K-S statistics of 3 or more deviation distributions and base mode deviation distributions are smaller than 0.5, that is, 60% or more deviation distributions are the same as the base mode deviation distributions, the electrocardiographic data to be authenticated and the base mode data are considered to be electrocardiographic data of the same individual, and the authentication of the user to be authenticated is passed; otherwise, the verification fails.
In an example of identity recognition by using an electrocardiosignal, as shown in fig. 4a, a schematic diagram of stored basic mode data, as shown in fig. 4b, a schematic diagram of stored basic mode deviation distribution, as shown in fig. 4c, a schematic diagram of mode data of a user to be recognized, as shown in fig. 4d, a deviation distribution schematic diagram of mode data of the user to be recognized, and a K-S test algorithm is used for testing, wherein the K-S statistic is 0.91, and if the K-S statistic is greater than a preset deviation threshold, the electrocardio data of the user to be recognized and the stored basic mode data are electrocardiograph data of different individuals, and 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 by the above embodiments, an embodiment of the present application further provides an identity recognition device, and since the identity recognition device provided by the embodiment of the present application corresponds to the identity recognition method provided by the above embodiments, the foregoing implementation manner is also applicable to the identity recognition device provided by the embodiment, and will not be described in detail in the embodiment.
As shown in fig. 5, an embodiment of the present application further provides an identification device, including: the heart beat dividing module is used for receiving the electrocardiosignals and dividing the electrocardiosignals into heart beats; the mode calculation module is used for carrying out mode calculation on the electrocardiosignals divided by the heart beats and collecting a preset number of heart beat data; the deviation calculation module is used for carrying out data processing on the preset number of heart beat data, carrying out deviation calculation on the preset number of heart beat data after the data processing and the pre-stored basic mode data respectively, and obtaining preset number of deviation distribution; and the difference judging module is used for carrying out difference judgment on the preset number of deviation distribution and the prestored base mode deviation distribution so as to verify the identity of the user to be identified.
In an alternative embodiment, as shown in fig. 6, the identification device further includes: the base mode calculation module is used for carrying out base mode calculation on the electrocardiosignals divided by the heart beat, and acquiring and storing the base mode data and the base mode deviation distribution; the encryption module is used for encrypting the base mode data and the base 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 application also provides an identification device based on the electrocardiosignal, which comprises an electrocardiosignal acquisition device and the identification device; the electrocardiographic acquisition device is used for acquiring electrocardiographic signals of a user to be identified, namely an acquisition device capable of acquiring electrocardiographic signals, such as a finger pressing type detection device shown in fig. 2; the identity recognition device is used for recognizing according to the electrocardiosignals of the users to be recognized so as to verify the identities of the users to be recognized. It should be noted that, the identity recognition device may be in an integral structure or a split structure, and those skilled in the art should design the identity recognition device according to actual application requirements to satisfy the identity recognition function as a design criterion, which is not described herein.
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 at preset time and performing heart beat division on the electrocardiosignal to be identified; carrying out mode calculation on the electrocardiosignals to be identified divided by the heart beats and collecting heart beat data of a first quantity; performing data processing on the first quantity of heart beat data, performing deviation calculation on the first quantity of heart beat data and the pre-stored base mode data respectively, and obtaining a first quantity of deviation distribution; and performing difference judgment on the first number of deviation distribution and the prestored basic mode deviation distribution to verify the identity of the user to be identified.
In practical applications, the computer-readable storage medium may take the form of 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. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (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 this 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.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations 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 ++ 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected 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 be construed as limiting the functionality and scope of use of embodiments of the invention.
As shown in fig. 7, the computer device 12 is in the form of a general purpose computing device. Components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
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. The 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 or write to non-removable, nonvolatile magnetic media (not shown in FIG. 7, commonly referred to as a "hard disk drive"). Although not shown in fig. 7, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules 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 in, for example, 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 or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 20. As shown in fig. 7, the network adapter 20 communicates with other modules of the computer device 12 via the bus 18. It should be appreciated that although not shown in fig. 7, other hardware and/or software modules may be used in connection with computer device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processor unit 16 executes various functional applications and data processing by running 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 at present, the invention establishes an electrocardiosignal-based identity recognition method, an electrocardiosignal-based identity recognition system, computer equipment and a medium, and can accelerate the calculation speed, improve the recognition accuracy and have better anti-noise performance through statistic deviation distribution according to the characteristic parameters and waveforms of the electrocardiosignal.
It should be understood that the foregoing examples of the present invention are provided merely for clearly illustrating the present invention and are not intended to limit the embodiments of the present invention, and that various other changes and modifications may be made therein by one skilled in the art without departing from the spirit and scope of the present invention as defined by the appended claims.
Claims (7)
1. An electrocardiosignal-based identification method is characterized by comprising the following steps:
collecting a sample electrocardiosignal of a user and performing heart beat division;
performing base mode calculation on the sample electrocardiosignals divided by the heart beat to acquire and store the base mode data and base mode deviation distribution;
receiving an electrocardiosignal to be identified of a user to be identified at preset time and performing heart beat division on the electrocardiosignal to be identified;
Carrying out mode calculation on the electrocardiosignals to be identified divided by the heart beats and collecting heart beat data of a first quantity;
performing data processing on the first quantity of heart beat data, performing deviation calculation on the first quantity of heart beat data and the pre-stored base mode data respectively, and obtaining a first quantity of deviation distribution;
performing difference judgment on the first number of deviation distributions and pre-stored base mode deviation distributions to verify the identity of the user to be identified;
the step of performing base mode calculation on the sample electrocardiosignals divided by the heart beat, and the step of obtaining and storing the base mode data and the base mode deviation distribution comprises the following steps:
collecting a second number of cardiac beat data from the cardiac signal of the sample divided by the cardiac beats;
interpolating the second number of beat data into a data set of the same length using spline interpolation;
dividing the second quantity of heart beat data subjected to spline interpolation into a third quantity of average heart beat data and a fourth quantity of deviation heart beat data, wherein the third quantity is larger than a preset heart beat quantity threshold value;
calculating a corresponding average voltage value according to the third number of average heart beat data and taking the average voltage value as the base mode data;
Calculating an average deviation voltage value according to the fourth number of deviation heartbeat data;
taking the probability density of the deviation value calculated by the average deviation voltage value and the average voltage value as the base mode deviation distribution;
after the probability density of the deviation value calculated by the average deviation voltage value and the average voltage value is used as the base mode deviation distribution, the identity recognition method further comprises the following steps:
encrypting and storing the base pattern data and the base pattern deviation distribution;
before the data processing is performed on the first number of heart beat data, the deviation calculation is performed on the first number of heart beat data and the pre-stored base pattern data respectively, and the first number of deviation distribution is obtained, the identification method further comprises the steps of:
decrypting the encrypted base pattern data and base pattern deviation distribution;
the performing a difference determination on the first number of deviation distributions and a pre-stored base pattern deviation distribution to verify the identity of the user to be identified includes:
checking the difference between each deviation distribution in the first number of deviation distributions and the base mode deviation distribution through a K-S checking algorithm and judging whether the difference meets a deviation threshold value;
If the ratio of the number meeting the deviation threshold to the first number is larger than the identification threshold, completing the authentication of the user to be identified, otherwise, failing the authentication;
the data processing of the first number of heart beat data, and the deviation calculation of the first number of heart beat data and the prestored base mode data respectively, and the obtaining of the first number of deviation distribution comprise the following steps:
interpolating the first number of beat data into a dataset of the same length as the base pattern data using spline interpolation;
respectively carrying out deviation calculation on the first quantity of heart beat data subjected to spline interpolation and pre-stored basic mode data to obtain first quantity of deviation;
and calculating the deviation distribution of each deviation respectively.
2. The identification method of claim 1, wherein encrypting and storing the base pattern data and base pattern bias distribution comprises:
performing byte substitution on the base pattern data and the base pattern deviation distribution;
performing line shift substitution on the base pattern data and the base pattern deviation distribution after the byte substitution;
column confusion is carried out on the base pattern data and the base pattern deviation distribution after the line shift substitution;
Performing round key addition on the base pattern data and the base pattern deviation distribution after the column confusion;
and storing the base pattern data and the base pattern deviation distribution after the round key addition.
3. The method for identifying a person according to claim 1, wherein,
the lead mode of the electrocardiograph detection device for collecting the electrocardiograph signals to be identified and/or the sample electrocardiograph signals is one of single lead, three lead, five lead, seven lead, twelve lead or fifteen lead; and/or
The wearing mode of the electrocardiograph detection equipment for collecting the electrocardiograph signals to be identified and/or the sample electrocardiograph signals is one of chest-mounted type, finger-pressed type or wrist-strap type; and/or
Collecting the electrocardiosignals to be identified and/or the sample electrocardiosignals, wherein the sampling rate of the electrocardiosignals to be identified and/or the sample electrocardiosignals is greater than or equal to 128Hz, and the voltage division value is less than or equal to 1mv; and/or
The heart beat dividing method is one of a difference method, a band-pass filtering method or a wavelet transformation method.
4. An electrocardiosignal-based identification device, which is characterized by comprising:
the heart beat dividing module is used for receiving the electrocardiosignals and dividing the electrocardiosignals into heart beats;
the mode calculation module is used for carrying out mode calculation on the electrocardiosignals divided by the heart beats and collecting a preset number of heart beat data;
The deviation calculation module is used for carrying out data processing on the preset number of heart beat data, carrying out deviation calculation on the preset number of heart beat data after the data processing and the pre-stored basic mode data respectively, and obtaining preset number of deviation distribution;
the step of performing data processing on the preset number of heart beat data, and performing deviation calculation on the preset number of heart beat data after data processing and the prestored base mode data respectively to obtain preset number of deviation distribution comprises the following steps:
carrying out mode calculation on the electrocardiosignals to be identified divided by the heart beats and collecting heart beat data of a first quantity;
performing data processing on the first quantity of heart beat data, performing deviation calculation on the first quantity of heart beat data and the pre-stored base mode data respectively, and obtaining a first quantity of deviation distribution;
performing differential judgment on the first number of deviation distributions and pre-stored base mode deviation distributions to verify the identity of the user to be identified;
collecting a second number of cardiac beat data from the cardiac signal of the sample divided by the cardiac beats;
interpolating the second number of beat data into a data set of the same length using spline interpolation;
dividing the second quantity of heart beat data subjected to spline interpolation into a third quantity of average heart beat data and a fourth quantity of deviation heart beat data, wherein the third quantity is larger than a preset heart beat quantity threshold value;
Calculating a corresponding average voltage value according to the third number of average heart beat data and taking the average voltage value as the base mode data;
calculating an average deviation voltage value according to the fourth number of deviation heartbeat data;
taking the probability density of the deviation value calculated by the average deviation voltage value and the average voltage value as the base mode deviation distribution;
a difference judging module, configured to judge the difference between the predetermined number of deviation distributions and a pre-stored base pattern deviation distribution to verify the identity of the user to be identified;
said differentially determining the predetermined number of deviation profiles from a pre-stored base pattern deviation profile to verify the identity of the user to be identified comprises:
checking the difference between each deviation distribution in the first number of deviation distributions and the base mode deviation distribution through a K-S checking algorithm and judging whether the difference meets a deviation threshold value;
if the ratio of the number meeting the deviation threshold to the first number is larger than the identification threshold, completing the authentication of the user to be identified, otherwise, failing the authentication;
the base mode calculation module is used for carrying out base mode calculation on the electrocardiosignals divided by the heart beat, and acquiring and storing the base mode data and the base mode deviation distribution;
The step of performing base mode calculation on the electrocardiographic signals divided by the heart beat, and the step of obtaining and storing the base mode data and the base mode deviation distribution comprises the following steps:
interpolating the first number of beat data into a dataset of the same length as the base pattern data using spline interpolation;
respectively carrying out deviation calculation on the first quantity of heart beat data subjected to spline interpolation and pre-stored basic mode data to obtain first quantity of deviation;
calculating a deviation distribution of each deviation;
the encryption module is used for encrypting the base mode data and the base mode deviation distribution;
and the decryption module is used for decrypting the encrypted base mode data and the base mode deviation distribution.
5. An electrocardiosignal-based identification device, which is characterized by comprising an electrocardio acquisition device and the identification device as claimed in claim 4, wherein
The electrocardio acquisition device is used for acquiring electrocardio signals of a user to be identified;
the identity recognition device is used for recognizing according to the electrocardiosignal so as to verify the identity of the user to be recognized.
6. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-3.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-3 when the program is executed by the processor.
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