CN107819926B - Electrocardio identity authentication device and method based on mobile phone - Google Patents
Electrocardio identity authentication device and method based on mobile phone Download PDFInfo
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Abstract
An electrocardio identity authentication device and method based on a mobile phone comprises an electrocardiosignal acquisition module and an electrocardiosignal processing and identity authentication module, wherein the electrocardiosignal acquisition module comprises a sensor used for acquiring electrocardiosignals, the electrocardiosignal processing and identity authentication module comprises a preprocessing module, a feature extraction module and an authentication module, the preprocessing module is used for filtering the electrocardiosignals acquired by the sensor to eliminate interference, the feature extraction module is used for detecting all datum points in the electrocardiosignals to extract quasi-periodic heartbeat signals as original electrocardio features, after the heartbeat is subjected to segmented waveform correction, the PCA is used for reducing the dimension and extracting coefficient features as final electrocardio features, and the authentication module is used for judging whether identity authentication of a test sample is successful or not. The invention can ensure the authenticity of user identity authentication, realize the reliable identification of living organisms and reduce the cost of professional equipment required by the existing scheme.
Description
Technical Field
The invention relates to the technical field of identity authentication, in particular to an electrocardio identity authentication device and method based on a mobile phone.
Background
Today's society has an increasing demand for biometric identification, and various methods for identifying an identity using biometric features have emerged. The most common are the following:
1. fingerprint and palm print identification
Fingerprint and palm print recognition is far from long. Fingerprints are almost the pronoun of biometric identification for a long time due to their lifelong invariance, uniqueness and convenience. The fingerprint refers to lines generated by convex and concave unevenness on the front skin at the tail end of a human finger. The lines are regularly arranged to form different line types. The starting point, ending point, junction point and bifurcation point of the striae are called minutiae points (minutiae) of the fingerprint.
2. Iris recognition
The eye structure is composed of parts such as sclera, iris, pupil lens, retina, etc. The iris is an annular segment between the black pupil and the white sclera containing many details characteristic of interlaced spots, filaments, coronaries, stripes, crypts, etc. And the iris will remain unchanged throughout life span after it is formed during the fetal development stage. These features determine the uniqueness of the iris features and also the uniqueness of the identification. Therefore, the iris feature of the eye can be used as an identification target for each person.
3. Face recognition
The human face recognition refers in particular to a computer technology utilizing analysis and comparison. Face recognition is a popular computer technology research field, and comprises face tracking detection, automatic image amplification adjustment, night infrared detection and automatic exposure intensity adjustment; it belongs to biological characteristic identification, and is used for distinguishing organism individuals from biological characteristics of organisms (generally, specially, people).
Besides the above recognition modes, there are also recognition modes such as voiceprint, gait, handwriting, and the like.
Most of the existing identification methods have defects, except for iris identification, other identification methods are not living body collection and can be pretended in a certain mode. And professional equipment is needed for iris recognition, so that the technical requirement is high. Therefore, there is a need for an identification method that can perform in vivo acquisition with low equipment requirements.
Disclosure of Invention
The invention mainly aims to overcome the defects of the prior art, and provides an electrocardio identity authentication device and method based on a mobile phone, which are used for carrying out living body identification, reducing the requirements on identification equipment and improving the safety and convenience of identification.
In order to achieve the purpose, the invention adopts the following technical scheme:
an electrocardio identity authentication device based on a mobile phone comprises an electrocardio signal acquisition module arranged on the front surface, the back surface and/or the peripheral side of the mobile phone and an electrocardio signal processing and identity authentication module arranged in the mobile phone, the electrocardiosignal acquisition module comprises a sensor for acquiring electrocardiosignals, the electrocardiosignal processing and identity authentication module comprises a preprocessing module, a feature extraction module and an authentication module, wherein the preprocessing module is used for filtering the electrocardiosignals acquired by the sensor to eliminate interference, the feature extraction module is used for detecting each reference point in the electrocardiosignal to extract quasi-periodic heart beat signals as original electrocardio features, after the heart beat is corrected by sectional waveform, PCA is used for dimensionality reduction and coefficient characteristics are extracted as final electrocardio characteristics, the authentication module uses a template matching based approach to determine whether the test sample is successfully authenticated.
Preferably, the sensor of the electrocardiosignal acquisition module comprises at least one pair of electrodes, and the pair of electrodes are distributed in the middle of the front side and the back side of the mobile phone, or distributed on two side edges of the mobile phone, or distributed on the top and the bottom of the mobile phone, or distributed on two ends of the top of the mobile phone, or distributed on two ends of the bottom of the mobile phone, or distributed on two corners above the front side or the back side of the mobile phone, or distributed on two corners below the front side or the back side of the mobile phone, or distributed on two diagonal angles of the front side or the back side of the mobile phone. The electrode include by the flexible material encapsulation as an organic whole touch the surface course and with touch the connecting terminal that the surface course electricity is connected, touch the surface course and be used for contacting in order to gather electrocardiosignal with finger skin, connecting terminal will electrocardiosignal conveys cell-phone inner circuit, flexible material is food level or medical grade's liquid silica gel or solid-state silica gel and wraps the shaping completely, touch the surface course and be food level or medical grade's stainless steel, copper sheet, nickel strap, iron or manganese steel.
The electrocardiosignal acquisition module further comprises touch sensors which are correspondingly arranged according to the distribution positions of the electrodes, the touch sensors are used for detecting whether fingers contact the electrodes or not and sending signals to a processor in the mobile phone when the fingers contact the electrodes, and the processor controls the electrocardiosignal acquisition module to acquire the signals after detecting the signals.
The respective reference points include a P-wave start point (Ps), a P-wave end point (Pe), an R-wave peak (R), a J-wave start point (J), a T-wave peak (Tp), and a T-wave end point (Te) of the heartbeat.
The feature extraction module comprises a reference point detection and segmentation module, a segmented waveform correction module and a PCA dimension reduction and feature extraction module, wherein the reference point detection and segmentation module is used for detecting each reference point in the electrocardiosignals and segmenting the waveforms based on the reference points, the segmented waveform correction module is used for performing segmented waveform correction to eliminate heart beat differences caused by heart rate variation, and the PCA dimension reduction and feature extraction module performs dimension reduction by using principal component analysis and extracts coefficient features as final electrocardiofeatures;
wherein the fiducial point detection and segmentation module performs fiducial point detection and waveform segmentation by:
the electrocardiosignal determines the position of R wave of heart beat by wavelet transformation, or determines the rough position of R wave of heart beat by the minimum value of the second order difference signal of electrocardiosignal, then determines the point where the first order difference signal at the rough position of R wave is closest to zero, and positions the position of R wave crest (R) according to the point;
taking one position within the range of 160-180 milliseconds from the left side of each R wave peak (R) as a P wave starting point (Ps); taking one position within the range of 80-100 milliseconds from the left side of each R wave peak (R) as a P wave terminal point (Pe); taking a position within 80-100 milliseconds from the right side of each R wave crest as a J wave starting point (J); taking the maximum value in a section of region on the right side of each R wave peak as a T wave peak (Tp), wherein the section of region starts from a J wave starting point (J) and is cut off at 2/3 current RR intervals; the position of the first-order differential signal on the right side of the T wave peak (Tp) from negative to positive for the first time is taken as a T wave end point (Te).
The segmented waveform correction module carries out segmented waveform correction in the following way:
carrying out segmented resampling on the heart beat signals, wherein each P wave band is subjected to up-sampling, and the duration of the P wave band is prolonged after the up-sampling, so that the durations of the P wave bands are unified into 460-500 milliseconds; the duration of each QRS wave band is kept unchanged; and for each T wave band, respectively carrying out down sampling on the J-Tp section and the Tp-Tp section, so that the respective time length of the two small sections after resampling of each T wave band is unified to 10-20 milliseconds.
The PCA dimension reduction and feature extraction module extracts the number of axes keeping the contribution rate above a set threshold as coefficient features, and the set threshold is preferably 99%.
The sensor comprises strip-shaped sensors arranged on two sides of a mobile phone frame.
The method comprises a preprocessing step, a feature extraction step and an authentication step, wherein the preprocessing step comprises the step of filtering electrocardiosignals acquired by a sensor to eliminate interference, the feature extraction step comprises the step of detecting each reference point in the electrocardiosignals to extract quasi-periodic heartbeat signals serving as original electrocardio features, PCA is used for reducing the dimension and extracting coefficient features serving as final electrocardio features after the heart beats are subjected to segmented waveform correction, and the authentication step comprises the step of judging whether identity authentication of a test sample is successful by using a template matching-based method.
Preferably, the respective reference points include a P-wave start point (Ps), a P-wave end point (Pe), an R-wave peak (R), a J-wave start point (J), a T-wave peak (Tp), and a T-wave end point (Te) of the heartbeat.
In the feature extraction step, the reference point detection and waveform segmentation are performed in the following manner:
determining the rough position of the R wave of the heart beat by using the minimum value of the second-order difference signal of the electrocardio signal, and then determining the point, at the rough position of the R wave, of which the first-order difference signal is closest to zero, thereby positioning the position of an R wave peak (R);
taking one position within the range of 160-180 milliseconds from the left side of each R wave peak (R) as a P wave starting point (Ps); taking one position within the range of 80-100 milliseconds from the left side of each R wave peak (R) as a P wave terminal point (Pe); taking a position within 80-100 milliseconds from the right side of each R wave crest as a J wave starting point (J); taking the maximum value in a section of region on the right side of each R wave peak as a T wave peak (Tp), wherein the section of region starts from a J wave starting point (J) and is cut off at 2/3 current RR intervals; the position of the first-order differential signal on the right side of the T wave peak (Tp) from negative to positive for the first time is taken as a T wave end point (Te).
In the feature extraction step, the segmented waveform correction is performed in the following manner:
carrying out segmented resampling on the heart beat signals, wherein each P wave band is subjected to up-sampling, and the duration of the P wave band is prolonged after the up-sampling, so that the durations of the P wave bands are unified into 460-500 milliseconds; the duration of each QRS wave band is kept unchanged; and for each T wave band, respectively carrying out down sampling on the J-Tp section and the Tp-Tp section, so that the respective time length of the two small sections after resampling of each T wave band is unified to 10-20 milliseconds.
In the feature extraction step, the number of axes maintaining the contribution ratio at a set threshold or more is extracted as a coefficient feature, and the set threshold is preferably 99%.
The invention has the beneficial effects that:
the invention utilizes the electrocardiosignal acquisition module arranged on the shell of the mobile phone and the electrocardiosignal processing and identity authentication module arranged in the mobile phone to carry out biological feature identification by using the electrocardiosignals, thereby ensuring the authenticity of user identity authentication, avoiding the problem of impersonation in the existing identification modes of fingerprints and the like, reducing the cost of professional equipment required by iris identification and the like, realizing living body biological identification, and greatly improving the safety and the efficiency-cost ratio compared with the traditional identification mode. The electrocardiosignal is preprocessed and feature extracted, particularly, each datum point in the electrocardiosignal is detected to extract a quasi-periodic heartbeat signal as an original electrocardio feature, after the heart beat is subjected to segmented waveform correction, PCA is used for dimensionality reduction, and coefficient features are extracted as final electrocardio features, so that the electrocardio features have very high uniqueness and identifiability, the electrocardio signals are used for identity verification, and the reliability and the safety of identity identification are guaranteed. Moreover, the electrocardiosignal is used for identity authentication, so that the acquisition and authentication of the identity of the living body can be ensured.
Drawings
Fig. 1 is a schematic perspective view of an electrocardiographic authentication device based on a mobile phone according to an embodiment of the present invention;
fig. 2 is a schematic front view of an electrocardiographic authentication device based on a mobile phone according to an embodiment of the present invention;
fig. 3 is a block diagram of an electrocardiographic signal acquisition module according to an embodiment of the present invention.
FIG. 4 is a block diagram of an exemplary ECG signal processing and identity authentication module;
FIG. 5 is a schematic diagram of cardiac signal fiducial point extraction in an embodiment of the present invention showing two quasi-periodic heart beats;
fig. 6 is a block diagram of an electrocardiographic signal processing and identity authentication module according to another embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary in nature and is not intended to limit the scope of the invention or its application.
Referring to fig. 1 to 4, in an embodiment, an electrocardiographic identity authentication device based on a mobile phone includes an electrocardiographic signal acquisition module installed on the front, back and/or peripheral side (the peripheral side of the mobile phone includes two side edges, a top and a bottom) of the mobile phone 1, and an electrocardiographic signal processing and identity authentication module installed in the mobile phone, the electrocardiographic signal acquisition module includes a sensor for acquiring electrocardiographic signals, the electrocardiographic signal processing and identity authentication module includes a preprocessing module, a feature extraction module and an authentication module, wherein the preprocessing module is configured to filter electrocardiographic signals acquired by the sensor to eliminate interference, the feature extraction module is configured to detect each reference point in the electrocardiographic signals to extract quasi-periodic heart beat signals as original electrocardiographic features, and after a beat is subjected to a segmented waveform correction, and then, using PCA to reduce dimensions and extract coefficient features as final electrocardio features, and using a template matching-based method to judge whether the identity authentication of the test sample is successful by the authentication module.
Preferably, the sensor of the electrocardiographic signal acquisition module comprises at least one pair of electrodes 2 and 2', and the pair of electrodes may be distributed at the middle position between the front side and the back side of the mobile phone, or at the two side edges of the mobile phone, or at the top and the bottom of the mobile phone, or at the two ends of the top of the mobile phone, or at the two ends of the bottom of the mobile phone, or at two corners above the front side or the back side of the mobile phone, or at two corners below the front side or the back side of the mobile phone, or at two diagonal angles of the front side or the back side of the mobile phone.
In the embodiment shown in fig. 2, a pair of electrodes 2, 2' are distributed at two corners below the front face of the handset.
In the preferred embodiment, electrode 2, 2' include by flexible material encapsulation as an organic whole touch the surface course and with touch the connecting terminal that the surface course is connected electrically, touch the surface course and be used for contacting in order to gather electrocardiosignal with finger skin, connecting terminal will electrocardiosignal conveys cell-phone internal circuit, flexible material is food level or medical grade liquid silica gel or solid-state silica gel and wraps the injection moulding entirely, touch the surface course and be food level or medical grade stainless steel, copper sheet, nickel strap, iron or manganese steel.
As shown in fig. 2 and 3, in a preferred embodiment, the electrocardiographic signal acquisition module further includes touch sensors 3 and 3 ' correspondingly disposed according to the distribution positions of the electrodes 2 and 2 ', where the touch sensors 3 and 3 ' are configured to detect whether a finger contacts the electrodes 2 and 2 ', and send a signal to a processor in the mobile phone when detecting that the finger contacts the electrodes 2 and 2 ', and the processor controls the electrocardiographic signal acquisition module to acquire the signal after detecting the signal. Because the touch sensor is arranged, the electrocardiosignal acquisition module does not need to maintain the working state of acquiring electrocardiosignals all the time, and only when a finger touches an electrode, namely the area where the touch sensor is located, the mobile phone controls the electrocardiosignal acquisition module to work to start signal acquisition, so that the design is favorable for reducing power consumption and reducing device loss. Of course, the position and size of the touch sensor with respect to the electrodes are not limited to the form shown in fig. 2, and any arrangement that can achieve the above-described effects may be adopted.
In a preferred embodiment, the respective reference points include a P-wave start point (Ps), a P-wave end point (Pe), an R-wave peak (R), a J-wave start point (J), a T-wave peak (Tp), and a T-wave end point (Te) of a heartbeat.
As shown in fig. 4, in a preferred embodiment, the feature extraction module includes a reference point detection and segmentation module, a segmented waveform correction module, and a PCA dimension reduction and feature extraction module, where the reference point detection and segmentation module is configured to detect each reference point in the electrocardiographic signal and segment the waveform based on the reference point, the segmented waveform correction module is configured to perform segmented waveform correction to eliminate heart beat differences caused by heart rate variability, and the PCA dimension reduction and feature extraction module performs dimension reduction by using principal component analysis and extracts coefficient features as final electrocardiographic features;
as shown in fig. 5, the fiducial detection and segmentation module performs fiducial detection and waveform segmentation by:
the electrocardiosignal determines the position of R wave of heart beat by wavelet transformation, or determines the rough position of R wave of heart beat by the minimum value of the second order difference signal of electrocardiosignal, then determines the point where the first order difference signal at the rough position of R wave is closest to zero, and positions the position of R wave crest (R) according to the point;
taking one position within the range of 160-180 milliseconds from the left side of each R wave peak (R) as a P wave starting point (Ps); taking one position within the range of 80-100 milliseconds from the left side of each R wave peak (R) as a P wave terminal point (Pe); taking a position within 80-100 milliseconds from the right side of each R wave crest as a J wave starting point (J); taking the maximum value in a section of region at the right side of each R wave peak (R) as a T wave peak (Tp), wherein the section of region starts from the J wave starting point (J) and is cut off at 2/3 current RR intervals (namely the time length between two adjacent R wave peaks); the position of the first-order differential signal on the right side of the T wave peak (Tp) from negative to positive for the first time is taken as a T wave end point (Te).
In a preferred embodiment, the segmented waveform correction module performs segmented waveform correction by:
carrying out segmented resampling on the heart beat signals, wherein each P wave band is subjected to up-sampling, and the duration of the P wave band is prolonged after the up-sampling, so that the durations of the P wave bands are unified into 460-500 milliseconds; the duration of each QRS wave band is kept unchanged; and for each T wave band, respectively carrying out down sampling on the J-Tp section and the Tp-Tp section, so that the respective time length of the two small sections after resampling of each T wave band is unified to 10-20 milliseconds.
In a preferred embodiment, the PCA dimensionality reduction and feature utilizes principal component analysis to reduce the dimensionality and extract the number of axes that keep the contribution rate above a set threshold as coefficient features, and the set threshold is preferably 99%.
The sensor comprises strip-shaped sensors arranged on two sides of a mobile phone frame.
The method comprises a preprocessing step, a feature extraction step and an authentication step, wherein the preprocessing step comprises the step of filtering electrocardiosignals acquired by a sensor to eliminate interference, the feature extraction step comprises the step of detecting each reference point in the electrocardiosignals to extract quasi-periodic heartbeat signals serving as original electrocardio features, PCA is used for reducing the dimension and extracting coefficient features serving as final electrocardio features after the heart beats are subjected to segmented waveform correction, and the authentication step comprises the step of judging whether identity authentication of a test sample is successful by using a template matching-based method. As shown in fig. 5, preferably, the respective reference points include a P-wave start point (Ps), a P-wave end point (Pe), an R-wave peak (R), a J-wave start point (J), a T-wave peak (Tp), and a T-wave end point (Te) of the heartbeat.
In a preferred embodiment, in the feature extraction step, the reference point detection and the waveform segmentation are performed by:
the electrocardiosignal determines the position of R wave of heart beat by wavelet transformation, or determines the rough position of R wave of heart beat by the minimum value of the second order difference signal of electrocardiosignal, then determines the point where the first order difference signal at the rough position of R wave is closest to zero, and positions the position of R wave crest (R) according to the point;
taking one position within the range of 160-180 milliseconds from the left side of each R wave peak (R) as a P wave starting point (Ps); taking one position within the range of 80-100 milliseconds from the left side of each R wave peak (R) as a P wave terminal point (Pe); taking a position within 80-100 milliseconds from the right side of each R wave crest as a J wave starting point (J); taking the maximum value in a section of region on the right side of each R wave peak as a T wave peak (Tp), wherein the section of region starts from a J wave starting point (J) and is cut off at 2/3 current RR intervals; the position of the first-order differential signal on the right side of the T wave peak (Tp) from negative to positive for the first time is taken as a T wave end point (Te).
In a preferred embodiment, in the feature extraction step, the segmented waveform correction is performed by:
carrying out segmented resampling on the heart beat signals, wherein each P wave band is subjected to up-sampling, and the duration of the P wave band is prolonged after the up-sampling, so that the durations of the P wave bands are unified into 460-500 milliseconds; the duration of each QRS wave band is kept unchanged; and for each T wave band, respectively carrying out down sampling on the J-Tp section and the Tp-Tp section, so that the respective time length of the two small sections after resampling of each T wave band is unified to 10-20 milliseconds.
In a preferred embodiment, in the feature extraction step, the number of axes that keep the contribution ratio at or above a set threshold is extracted as the coefficient feature, and the set threshold is preferably 99%.
The features and advantages of embodiments of the present invention are further described below in conjunction with the following figures.
The electrocardio identity authentication device of the specific embodiment comprises an electrocardio signal acquisition module and an electrocardio signal processing and identity authentication module; the electrocardiosignal acquisition module can adopt sensors arranged outside the two sides of the mobile phone frame to acquire electrocardiosignals for the electrocardiosignal processing and identity verification module to use; the electrocardiosignal processing and identity authentication module processes the collected electrocardiosignals and carries out identity authentication. Specifically, the following aspects are included:
1. electrocardiosignal acquisition
The electrocardio data can be collected by strip-shaped sensors on two sides of the mobile phone frame. The sensor at the position can meet the habits of different users and fully improve the user experience. The two hands are respectively pressed on the sensors at the two ends, and the potential difference of the left limb and the right limb is utilized for collection.
2. Electrocardiosignal processing
a. Pretreatment of
The method mainly comprises the step of filtering the original electrocardiosignals to eliminate common interference.
b. Feature extraction
As shown in fig. 5, first, each reference point in the electrocardiographic signal is detected to extract a quasi-periodic heart beat as an original electrocardiographic feature. The ecg signal is a quasi-periodic signal, but not specific to components in the entire heart cycle, where the P-wave, QRS complex, and T-wave in each heart cycle contain most of the ecg-specific information. The embodiment of the invention cuts out wave bands in each heart cycle from continuous electrocardiosignals to be used as original electrocardio characteristics. For this purpose, the reference point of the heart beat is located. In addition, in the subsequent waveform correction step, the P-wave and the T-wave need to be further processed. Therefore, it is necessary to locate the critical locations of these waveforms, and these points are collectively referred to as fiducials. The reference points for each heartbeat detection of the embodiment of the invention comprise: the P wave starting point (Ps) and P wave end point (Pe), the R wave peak (R), the J wave starting point (J), the T wave peak (Tp) and the T wave end point (Te) total 6 types of reference points.
Among them, the electrocardiographic signal is relatively mild overall, and the R wave is the sharpest part. The R wave is located at the minimum position of the second-order difference of the signals, and the first-order difference is 0. The embodiment of the invention determines the rough position of the R wave by using the minimum value of the second-order differential signal of the original signal. After the rough position of the R wave is positioned, according to the characteristic that the amplitude of the R wave is at the position of the maximum value, the first derivative is 0, and under the discrete condition, namely the one of the first-order difference signals which is closest to zero, the accurate position of the R wave peak is positioned according to the first-order derivative.
Further, one position, preferably 170 milliseconds, within the range of 160-180 milliseconds at the left side of each R wave is taken as the P wave starting point Ps; taking one position in the range of 80-100 milliseconds at the left side of each R wave, preferably 90 milliseconds as a P wave terminal point Pe; taking one position in the range of 80-100 milliseconds at the right side of each R wave peak, preferably 90 milliseconds as a J wave starting point (J); taking the maximum value in a section of region on the right side of each R wave peak (R) as a T wave peak (Tp), wherein the section of region is cut off from the starting point of the J wave to 2/3 current RR intervals (namely the time length between two adjacent R wave peaks); the position of the first-order differential signal on the right side of the T wave peak (Tp) from negative to positive for the first time is taken as a T wave end point (Te).
Because the heart rate changes and the heart beats in each quasi-period are different, the embodiment of the invention provides a method for correcting the segmented waveform to eliminate the influence of heart rate variation, the basic method for correcting is to perform segmented resampling on the original heart beat signal, specifically, the P wave band is up-sampled, the time length of the P wave band is prolonged after the up-sampling, and the time length is unified to 460 plus 500 milliseconds, preferably 480 milliseconds; for QRS bands remain unchanged, e.g. 180 milliseconds long; for the T-band, the J-Tp segment and the Tp-Tp segment are down-sampled respectively, so that the time lengths of the two small segments after the re-sampling are unified to 10-20 milliseconds, preferably 15 milliseconds. Finally, the total length of the corrected heart beat is substantially the same, for example 690 milliseconds. Since the heart rate of a person is different at different times and with different exercises, the heart rate difference should not be a measure for the identity of the person. The invention takes QRS wave band as reference to generate a signal which is convenient to detect, and the lengths of heart cycles are consistent, thereby eliminating the difference caused by heart rate variation.
Finally, using PCA to reduce dimension and extracting coefficient characteristics as final electrocardio characteristics; PCA principal component analysis can concentrate signal energy to direct current and low frequency parts, and the invention performs feature extraction and feature dimensionality reduction on heart beats subjected to PCA smoothing. Preferably, the number of axes in which the retention contribution ratio is equal to or greater than a set threshold is extracted as the coefficient feature, and the set threshold is preferably 99%. Experimental tests show that the coincidence degree of the coefficient vectors of the heart beats is higher, which indicates that the intra-class distance between the coefficient vectors is small; energy is distributed mainly in the first 80 dimensions.
c. Authentication
And judging whether to accept the identity claim of the test sample based on a template matching method. The specific method of template matching may be implemented by the prior art well known to those skilled in the art, and will not be described herein.
Preferably, a pre-generated electrocardiogram template is used to perform multiple matching judgment on waveforms of multiple heartbeats of the user, and the electrocardiogram template matching with the electrocardiogram signal of the user is considered to be successful only if the authentication accuracy is greater than 80%.
As shown in fig. 6, in another embodiment, the difference from the embodiment shown in fig. 4 is that, after a series of heartbeats of a user are acquired, R-wave positions of the heartbeats are detected, a plurality of heartbeats are divided according to RR intervals, then reference point features of the heartbeats are extracted, n different electrocardiographic templates which are generated in advance are used for template matching of the heartbeats, and finally, an electrocardiographic identity authentication result of the user is determined based on matching results of the n electrocardiographic templates. In a preferred embodiment, a method of voting for the matching result may be adopted to obtain the final authentication result. And voting by adopting the highest entropy in the process of voting the matching result for final identity recognition, counting entropy values of various categories in the primary identity recognition, and taking a category number corresponding to the maximum entropy value as a final recognition result according to the counted entropy values. The entropy value such as frequency can be used when the highest entropy voting is carried out by the highest entropy voting module. And counting the occurrence frequency of each category corresponding to each electrocardiogram template in the preliminary classification, and calculating the frequency of each category in the preliminary classification. Based on the statistical entropy, a maximum entropy, such as a maximum frequency, is found. The class number corresponding to the maximum entropy value is the final recognition result of the system.
In some embodiments, the user needs to be registered for the first time of use, which generates a user template. The authentication link of each time is that the newly acquired electrocardio is matched with the electrocardio of the user template.
In another aspect of the present invention, a fast electrocardiographic identification system is provided in the apparatus for authenticating electrocardiographic identity based on mobile phone, which is proposed in the applicant's patent application (201610698195.0) ' a method and system for fast electrocardiographic identification '.
In another aspect of the present invention, a method for authenticating an electrocardiogram based on a mobile phone uses a method for fast identifying an electrocardiogram proposed in the present applicant's patent application (201610698195.0) ' a method for fast identifying an electrocardiogram and a system thereof ' on a mobile phone.
Applicants' invention patent application (201610698195.0) "a method and system for rapid cardiac identification" is incorporated herein by reference in its entirety.
The foregoing is a more detailed description of the invention in connection with specific/preferred embodiments and is not intended to limit the practice of the invention to those descriptions. It will be apparent to those skilled in the art that various substitutions and modifications can be made to the described embodiments without departing from the spirit of the invention, and these substitutions and modifications should be considered to fall within the scope of the invention.
Claims (5)
1. An electrocardio identity authentication device based on a mobile phone is characterized by comprising an electrocardiosignal acquisition module arranged on the front surface, the back surface and/or the peripheral side of the mobile phone and an electrocardiosignal processing and identity authentication module arranged in the mobile phone, the electrocardiosignal acquisition module comprises a sensor for acquiring electrocardiosignals, the electrocardiosignal processing and identity authentication module comprises a preprocessing module, a feature extraction module and an authentication module, wherein the preprocessing module is used for filtering the electrocardiosignals acquired by the sensor to eliminate interference, the feature extraction module is used for detecting each reference point in the electrocardiosignal to extract quasi-periodic heart beat signals as original electrocardio features, after the heart beat is corrected by sectional waveform, PCA is used for dimensionality reduction and coefficient characteristics are extracted as final electrocardio characteristics, the authentication module uses a template matching based method to judge whether the identity authentication of the test sample is successful;
the heart beat detection and segmentation module is used for detecting each reference point in an electrocardiosignal and segmenting a waveform based on the reference point, the segmentation waveform correction module is used for carrying out segmentation waveform correction to eliminate heart beat difference caused by heart beat variation, and the PCA dimension reduction and feature extraction module carries out dimension reduction by utilizing principal component analysis and extracts coefficient features as final electrocardio features;
wherein the fiducial point detection and segmentation module performs fiducial point detection and waveform segmentation by:
the electrocardiosignal determines the position of R wave of heart beat by wavelet transformation, or determines the rough position of R wave of heart beat by the minimum value of the second order difference signal of electrocardiosignal, then determines the point where the first order difference signal at the rough position of R wave is closest to zero, and positions the position of R wave crest (R) according to the point;
taking one position within the range of 160-180 milliseconds from the left side of each R wave peak (R) as a P wave starting point (Ps); taking one position within the range of 80-100 milliseconds from the left side of each R wave peak (R) as a P wave terminal point (Pe); taking a position within 80-100 milliseconds from the right side of each R wave crest as a J wave starting point (J); taking the maximum value in a section of region on the right side of each R wave peak (R) as a T wave peak (Tp), wherein the section of region starts from a J wave starting point (J) and is cut off at 2/3 current RR intervals; taking the position of the first-order differential signal on the right side of the T wave peak (Tp) from negative to positive for the first time as a T wave terminal (Te);
the segmented waveform correction module carries out segmented waveform correction in the following way:
carrying out segmented resampling on the heart beat signals, wherein each P wave band is subjected to up-sampling, and the duration of the P wave band is prolonged after the up-sampling, so that the durations of the P wave bands are unified into 460-500 milliseconds; the duration of each QRS wave band is kept unchanged; and for each T wave band, respectively carrying out down sampling on the J-Tp section and the Tp-Tp section, so that the respective time length of the two small sections after resampling of each T wave band is unified to 10-20 milliseconds.
2. The device for authenticating the electrocardio-identity based on the mobile phone according to claim 1, wherein the electrocardiosignal acquisition module comprises an electrode, the electrode comprises a contact surface layer which is packaged into a whole by a flexible material and a connecting terminal which is electrically connected with the contact surface layer, the contact surface layer is used for contacting with the skin of a finger to acquire electrocardiosignals, the connecting terminal transmits the electrocardiosignals to an internal circuit of the mobile phone, the flexible material is food-grade or medical-grade liquid silica gel or solid silica gel which is fully encapsulated and sealed, and the contact surface layer is food-grade or medical-grade stainless steel, copper sheet, nickel strip, iron or manganese steel.
3. The device for authenticating the electrocardiographic identity based on the mobile phone according to claim 1, wherein the electrocardiographic signal acquisition module further comprises a touch sensor correspondingly disposed according to the distribution position of each electrode, the touch sensor is configured to detect whether a finger touches the electrode, and send a signal to a processor in the mobile phone when detecting that the finger touches the electrode, and the processor controls the electrocardiographic signal acquisition module to acquire the signal after detecting the signal.
4. The device for cardiac electric identity authentication based on mobile phone according to claim 1, wherein the PCA dimension reduction and feature extraction module extracts axis numbers keeping the contribution rate above a set threshold as coefficient features, the set threshold being 99%.
5. An electrocardio identity authentication method based on a mobile phone, which is characterized in that the electrocardio identity authentication device of any one of claims 1 to 4 is used, the method comprises a preprocessing step, a feature extraction step and an authentication step, wherein the preprocessing step comprises filtering electrocardiosignals acquired by a sensor to eliminate interference, the feature extraction step comprises detecting each reference point in the electrocardiosignals to extract quasi-periodic heartbeat signals as original electrocardiofeatures, and after the heart beats are subjected to segmented waveform correction, PCA is utilized to reduce dimension and extract coefficient features as final electrocardiofeatures, and the authentication step comprises judging whether identity authentication of a test sample is successful by using a method based on template matching.
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| CN101773394A (en) * | 2010-01-06 | 2010-07-14 | 中国航天员科研训练中心 | Identification method and identification system using identification method |
| CN102866843A (en) * | 2011-06-29 | 2013-01-09 | Lg电子株式会社 | Mobile terminal with at least a body sensor |
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