CN112016405B - Electrocardiogram identity authentication method based on wearable equipment - Google Patents
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Abstract
The invention discloses an electrocardiogram identity authentication method based on wearable equipment. Aiming at the problem of electrocardiogram noise reduction on wearable equipment, the invention designs the mixed three-layer noise reduction to effectively remove noise signals, the mixed three-layer noise reduction combines the advantages of morphological filtering and frequency domain filtering, the implementation method is simple and convenient, and the signal quality of the electrocardiogram obtained after filtering can be obviously ensured. The user characteristic template constructed by the invention reserves the respective hard index and soft index characteristics of the reference characteristic and the non-reference characteristic of the electrocardiogram, and ensures the authentication accuracy of the system.
Description
Technical Field
The invention belongs to the field of biological authentication, and particularly relates to an electrocardiogram identity authentication method based on wearable equipment.
Background
In recent years, wearable devices (such as smart watches) are widely applied in the fields of mobile payment, mobile communication, health information monitoring, smart home interaction and the like. Currently, there are smart watches with electrocardiogram measurement function, such as Apple Watch and health Watch of China rice technology.
In the past, research data in electrocardiogram research are acquired by an electrocardiograph with high anti-interference performance and high precision, and the electrocardiogram data have high signal-to-noise ratio and do not have high-energy low-frequency noise and high-frequency noise. Therefore, the prior electrocardiogram identification and authentication research can remove the noise in the electrocardiogram by only one-time band-pass filtering. However, such frequency domain filtering methods are not suitable for removing noise in electrocardiograms acquired by wearable devices. The electrocardiogram signal noise ratio that wearable equipment gathered is lower, and the key low frequency region of electrocardiogram has been covered to the noise frequency channel of high energy, and frequency domain filtering can't extract the frequency channel that represents the electrocardiogram signal from the frequency channel of signal and noise aliasing effectively. On the other hand, when constructing the user feature template, the conventional research usually ignores the characteristics of the electrocardiogram features and directly constructs the feature template, and few researches differentiate the two types of features, and furthermore, the proportion of the features in the user feature template is not adjusted, so that the constructed feature template cannot sufficiently represent the electrocardiogram information of the user.
Disclosure of Invention
The invention aims to provide an electrocardiogram identity authentication method based on wearable equipment, aiming at the defects of the prior art.
The purpose of the invention is realized by the following technical scheme: an electrocardiogram identity authentication method based on wearable equipment comprises the following steps:
the method comprises the following steps: the wearable equipment with the electrocardiogram measuring function acquires an electrocardiogram of a user;
step two: preprocessing the electrocardiogram collected in the first step by applying three layers of mixed noise reduction;
step three: carrying out feature point detection on the electrocardiogram signals preprocessed in the step three, and positioning the position of the feature point;
step four: extracting reference features and non-reference features in the electrocardiogram based on the feature points positioned in the step three, and combining the reference features and the non-reference features to construct a feature template to form a user database;
step five: constructing a support vector machine based on the user database obtained in the fourth step;
step six: and obtaining an electrocardiogram feature template of the user to be authenticated according to the first step, the second step and the third step, inputting the class of support vector machines constructed in the fifth step, and authenticating the identity.
Further, in the first step, the wearable device includes a smart band, a smart watch, and a health band.
Further, in the second step, the three layers of mixed noise reduction are sequentially a first non-local mean noise reduction, a frequency domain filtering method and a second non-local mean noise reduction.
Further, in the first non-local mean denoising, the region length L of the target point and the field pointΔSetting the size of a QRS complex wave area, wherein the size N (p) of a search area is not less than half of the length of an electrocardiogram signal, and a smoothing parameter lambda is not more than half of the standard deviation of signal noise; in the second non-local mean noise reduction, LΔThe size of the QRS complex wave area is N (p), the size of the search area is not less than half of the length of the electrocardiogram signal, and the smoothing parameter lambda is 30% -50% of the standard deviation of signal noise.
Further, the frequency domain filtering method includes a wavelet denoising method and an elliptic filter.
Further, the third step is specifically: firstly, detecting the position of QRS complex wave in electrocardiogram signals by a band-pass filter, a derivative filter and a square sum integrator by adopting a Pan-Tompkins algorithm, roughly positioning the position of the QRS complex wave, and then accurately positioning the positions of characteristic points P, Q, R, S and a T point by a local self-adaptive threshold method.
Further, the fourth step includes the following substeps:
(4.1) calculating a time interval and an amplitude difference between the electrocardiogram feature points P, Q, R, S and the point T as reference features;
(4.2) intercepting electrocardiogram segments of one period from the electrocardiogram signals by taking the characteristic point R as the center, and calculating the mean value of all the intercepted electrocardiogram segments to obtain an average electrocardiogram segment;
(4.3) extracting non-reference features from the average electrocardiogram fragment obtained in the step (4.2), and extracting feature information of the electrocardiogram in a wavelet domain as the non-reference features by adopting wavelet decomposition;
(4.4) performing feature selection on the reference features obtained in the step (4.1) by adopting a Fisher algorithm to obtain electrocardiogram reference features with the highest discrimination, and performing dimension reduction on the non-reference features by adopting a multi-dimensional scaling method; debugging the identity authentication recognition effect by adjusting the reference feature number and the non-reference feature number in the feature template; the feature templates of all users constitute a user database.
Further, the step (4.3) is specifically: and decomposing the average electrocardiogram fragment by using the wavelet base db5, wherein the number of decomposition layers is 4, and the approximation coefficient of the 4 th layer and the detail coefficients of the 3 rd and 4 th layers are taken as non-reference characteristics.
Further, in the fifth step, the kernel function of the support vector machine of one type is set as a gaussian kernel function, and the anomaly ratio is 0.006.
The beneficial effects of the invention are: aiming at the problems of electrocardiogram noise reduction and the defects of characteristic template construction in the prior art, the invention provides a new noise reduction mode and a characteristic template construction method; the problem of making an uproar falls in the electrocardiogram on the wearable equipment is solved, the attitude of heart characteristic in order to improve the authentication rate of accuracy of protection electrocardiogram. The proportion of the reference features to the non-reference features is comprehensively adjusted to obtain the electrocardiogram feature template with strong representativeness, the implementation method is simple, the means is flexible, and the high accuracy and the low false recognition rate of the authentication system can be ensured.
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FIG. 1 is a schematic view of a use scenario of the present invention;
FIG. 2 is a flow chart of a wearable device based electrocardiogram identity authentication method;
FIG. 3 is a flow chart of hybrid three-layer noise reduction;
fig. 4 is a flow chart of feature extraction.
Detailed Description
In order to further explain the technical means adopted by the invention and the obtained effects, the invention is explained in detail below according to the attached drawings.
The wearable equipment such as the smart watch with the electrocardiogram measuring function can acquire the electrocardiogram of a user at any time for real-time authentication, and can also be used as auxiliary authentication of the smart equipment to realize the functions of opening an entrance guard, unlocking a mobile phone, performing payment authentication and the like, wherein a specific situation diagram is shown in figure 1.
As shown in fig. 2, fig. 2 is a flowchart of an electrocardiogram identity authentication method based on a wearable device, including the following steps:
the method comprises the following steps: the user wears wearable equipment such as intelligent bracelet that has heart electrograph measurement function, intelligent wrist-watch and healthy wrist strap, for example the user can wear the existing intelligent wrist-watch that has heart electrograph measurement function in the market in wrist department, and wearable equipment gathers user's original heart electrograph data.
In this step, the user contacts the collecting electrode on the wearable device, the wearable device is internally provided with an electrocardiogram collecting chip, the electrocardiogram signal can be captured through weak potential change of the skin on the electrode, and the collected original electrocardiogram signal is uploaded to the terminal or the server.
Step two: and (3) denoising the original electrocardiogram signal by using the mixed three-layer denoising to obtain an effective electrocardiogram signal.
This step is one of the core steps of the present invention, as shown in fig. 3, fig. 3 is a flow chart of hybrid three-layer noise reduction, which is divided into the following sub-steps:
1) the acquired electrocardiogram signals of the users contain aliasing low-frequency and high-frequency noises with high energy, three layers of noise reduction are mixed to reduce noise for three times, namely, the noise reduction is carried out on the original electrocardiogram signals for the first time, and the noise reduction is carried out on the original electrocardiogram signals by using non-local mean noise reduction firstly; non-local mean noise reduction takes advantage of the non-correlation of the noise signal with the electrocardiogram signal, as well as the strong correlation of the electrocardiogram signal, to extract the effective electrocardiogram signal from the aliased signal.
The low-frequency noise and the high-frequency noise of the first non-local mean noise reduction filtering part can not excessively smooth signals, can also save complete detail characteristics, and still contain more noise. The parameters of the first non-local mean noise reduction are respectively set as: λ ═ 0.5 × 0.08, LΔ10 and n (p) 3000. Lambda is a smoothing parameter, the degree of the smoothed signal is controlled, the larger the lambda is, the more gradual the change of the gaussian function is, the higher the noise reduction level is, but excessive smoothing is easily caused, the smaller the lambda is, more details can be reserved, but excessive noise data can be remained, and the smoothing effect is not obvious enough; the value of lambda is related to signal noise, and the smoothing parameter lambda is positively related to the noise standard deviation sigma, namely, lambda is k sigma; when the standard deviation of the signal noise is too large, a larger lambda is needed to smooth the noise, and if the standard deviation of the signal noise is too small, a smaller lambda is needed to smooth the noise. The first time the non-local mean noise reduction parameter should be set to be moderate to prevent over-smoothing, so it is necessary to control the lambda parameter not to exceedHalf of the signal noise standard deviation sigma ensures that the first time of noise reduction does not lose detail features. Region length L of target point and field pointΔThe size of the similar area of the electrocardiogram, namely the size of the QRS complex wave area is set. N (p) determines the size of the search area, and N (p) is set to be not less than half of the length of the electrocardiogram signal so as to search more similar morphological areas.
2) And the second noise reduction adopts a frequency domain filtering method.
The high-frequency noise of the electrocardiogram can be filtered by a frequency domain filtering method, and the key low-frequency information of the electrocardiogram is protected; firstly, detail information of the electrocardiogram is filtered by a wavelet denoising method, and then high-frequency noise is further filtered by an elliptic low-pass filter; the frequency domain filtered electrocardiogram signal still contains low-frequency noise, but the detail features are completely preserved.
3) And the third noise reduction is carried out again by adopting non-local mean noise reduction to obtain a clean and effective electrocardiogram signal.
And denoising and removing low-frequency noise for the second non-local mean value, completely storing the detail characteristics of the electrocardiogram, and setting the parameters of denoising for the second non-local mean value as: λ 0.4 × 0.03, LΔ10 and n (p) 3000, LΔThe size of the similar area of the electrocardiogram, i.e. the size of the QRS complex, is still set, and n (p) is still set to be not less than half the length of the electrocardiogram signal, in order to find more similar morphological areas. Because the electrocardiogram contains less noise after two times of denoising, the lambda parameter does not need to be too large, and the lambda parameter is 30% -50% of the standard deviation sigma of the signal noise.
This can remove aliasing electrocardiogram noise and effectively preserve the morphological features of the electrocardiogram. The electrocardiogram signals after noise reduction for three times are well filtered, and the details of the electrocardiogram are completely stored.
Step three: and positioning each characteristic point in the electrocardiogram after noise reduction.
In this step, the Pan-Tompkins algorithm is combined with the local adaptive thresholding method to locate the ECG feature points. Firstly, the Pan-Tompkins algorithm detects the position of a QRS complex wave through a band-pass filter, a derivative filter, a square sum integrator, roughly positions the position of the QRS complex wave, and then accurately positions the position of a characteristic point through a local self-adaptive threshold method.
Step four: the method comprises the following steps of extracting reference features and non-reference features of the electrocardiogram based on feature points positioned in the third step, and constructing a feature template corresponding to the input electrocardiogram by combining the reference features and the non-reference features; as shown in fig. 4, fig. 4 is a flowchart of feature extraction, and the feature template construction method is divided into the following sub-steps:
1) extracting reference features by taking the feature points of the electrocardiogram as reference points, and extracting non-reference features based on average electrocardiogram segments;
after the feature point detection of the previous step, the feature points in each electrocardiogram segment are located, and the time interval and amplitude difference between the electrocardiogram feature points P, Q, R, S and the T point are calculated as the reference features. In the invention, 9 time interval features and 10 amplitude features (RQ amplitude features are removed) are extracted in total, and the details are shown in Table 1.
Table 1: 9 time interval features and 10 amplitude features of electrocardiogram
Before extracting non-reference features, the average electrocardiogram segment must be calculated. From a series of electrocardiogram signals, according to the position of the characteristic point R, positioning one electrocardiogram position, then taking the characteristic point R as the center to intercept one period of electrocardiogram segments, and calculating the average value of all the intercepted electrocardiograms to obtain the average electrocardiogram segment.
Extracting non-reference features on the average electrocardiogram segment, and extracting feature information of the electrocardiogram in a wavelet domain by adopting wavelet decomposition, wherein the method specifically comprises the following steps: the average electrocardiogram fragment is decomposed by wavelet base db5, the number of decomposition layers is 4, and the 4 th layer approximation coefficient and the 3 rd and 4 th layer detail coefficients are used as non-reference features.
2) And selecting the reference features, reducing the dimension of the non-reference features, and adjusting the number of the reference features and the number of the non-reference features to construct a feature template.
Selecting the features of the reference features by using a Fisher algorithm to select the features with the most distinguishing degree; and reducing the dimension of the high-dimensional non-reference features by adopting a multi-dimensional scaling method to extract core representative non-reference features, reducing feature redundancy and preventing high-dimensional disasters. According to electrocardiogram data provided by a user, a user characteristic template containing different reference characteristics and non-reference characteristic combination collocation is constructed, the reference characteristic number and the non-reference characteristic number in the characteristic template are adjusted to enable the user characteristic template to obtain comprehensive optimal performance when testing the recall rate and the false recognition rate of the user, the most representative user characteristic template is obtained, and a user database is established. Respectively selecting and reducing the dimensions of the reference features and the non-reference features, so that the characteristics of respective soft and hard indexes can be reserved, and a complete user feature template can be constructed; the user feature template constructed based on the reference features and the non-reference features not only retains the detail features of the electrocardiogram, but also stores the overall morphological features of the electrocardiogram.
Step five: and constructing a type of support vector machine for identity authentication according to the user database obtained in the step four.
In the step, a hyperplane is constructed by adopting a support vector machine of the same type based on a user database to distinguish a user from a non-user, if the input features are within the hyperplane, the user is indicated, otherwise, the non-user is indicated; the kernel function of the support vector machine is set to be a gaussian kernel function, and the anomaly ratio is 0.006.
Step six: when identity authentication is carried out, obtaining an electrocardiogram feature template of a user to be subjected to identity authentication according to the first step, the second step and the third step, inputting the type of support vector machine constructed in the fifth step, and carrying out identity authentication; the method is simple in calculation, rapid in operation and suitable for being transplanted on each platform.
Claims (7)
1. An electrocardiogram identity authentication method based on wearable equipment is characterized by comprising the following steps:
the method comprises the following steps: the wearable device with the electrocardiogram measurement function acquires an electrocardiogram of a user;
step two: preprocessing the electrocardiogram collected in the first step by applying three layers of mixed noise reduction; the mixed three-layer noise reduction sequentially comprises a first non-local mean noise reduction, a frequency domain filtering method and a second non-local mean noise reduction; in the first non-local mean noise reduction, the region length L of the target point and the field pointΔSetting the size of a QRS complex wave area, wherein the size N (p) of a search area is not less than half of the length of an electrocardiogram signal, and a smoothing parameter lambda is not more than half of the standard deviation of signal noise; in the second non-local mean noise reduction, LΔThe size of the QRS complex wave area is N (p), the size of the search area is not less than half of the length of the electrocardiogram signal, and the smoothing parameter lambda is 30% -50% of the standard deviation of signal noise;
step three: detecting characteristic points of the electrocardiogram signals preprocessed in the step two, and positioning the positions of the characteristic points;
step four: extracting reference features and non-reference features in the electrocardiogram based on the feature points positioned in the step three, and combining the reference features and the non-reference features to construct a feature template to form a user database; wherein the reference features are the time interval and the amplitude difference between the electrocardiogram feature point P, Q, R, S and the point T; intercepting electrocardiogram segments of a period by taking a characteristic point R as a center from electrocardiogram signals, calculating the mean value of all the intercepted electrocardiogram segments to obtain an average electrocardiogram segment, and extracting the characteristic information of the electrocardiogram in a wavelet domain as non-reference characteristics by adopting wavelet decomposition on the average electrocardiogram segment;
step five: constructing a support vector machine based on the user database obtained in the step four;
step six: and obtaining an electrocardiogram feature template of the user to be authenticated according to the first step, the second step and the third step, inputting the electrocardiogram feature template into the support vector machine constructed in the fifth step, and authenticating the identity.
2. The wearable device-based electrocardiogram identity authentication method according to claim 1, wherein in the first step, the wearable device comprises a smart band, a smart watch and a health band.
3. The wearable device-based electrocardiogram identity authentication method according to claim 1, wherein the frequency domain filtering method comprises a wavelet denoising method and an elliptic filter.
4. The wearable device-based electrocardiogram identity authentication method according to claim 1, wherein the third step is specifically: firstly, detecting the position of QRS complex wave in electrocardiogram signals by a band-pass filter, a derivative filter and a square sum integrator by adopting a Pan-Tompkins algorithm, roughly positioning the position of the QRS complex wave, and then accurately positioning the positions of characteristic points P, Q, R, S and a T point by a local self-adaptive threshold method.
5. The wearable device-based electrocardiogram identity authentication method according to claim 1, wherein in the fourth step, the feature template is constructed by combining the reference feature and the non-reference feature to form a user database, which comprises: performing feature selection on the reference features by adopting a Fisher algorithm to obtain electrocardiogram reference features with the highest discrimination, and performing dimensionality reduction on the non-reference features by adopting a multi-dimensional scaling method; debugging the identity authentication recognition effect by adjusting the reference feature number and the non-reference feature number in the feature template; the feature templates of all users constitute a user database.
6. The wearable device-based electrocardiogram identity authentication method according to claim 1, wherein in the fourth step, the wavelet base db5 is used to decompose the average electrocardiogram segment, the number of decomposition layers is 4, and the 4 th layer approximation coefficient and the 3 rd and 4 th layer detail coefficients are used as non-reference features.
7. The wearable device-based electrocardiogram identity authentication method according to claim 1, wherein in step five, the kernel function of one type of support vector machine is set as a gaussian kernel function, and the anomaly ratio is 0.006.
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