CN103714281A - Identity recognition method based on electrocardiosignals - Google Patents
Identity recognition method based on electrocardiosignals Download PDFInfo
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- CN103714281A CN103714281A CN201310686789.6A CN201310686789A CN103714281A CN 103714281 A CN103714281 A CN 103714281A CN 201310686789 A CN201310686789 A CN 201310686789A CN 103714281 A CN103714281 A CN 103714281A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/117—Identification of persons
Abstract
The invention relates to the field of biological recognition, and provides an identity recognition method based on electrocardiosignals. The method comprises the following steps: S1: acquiring ECG (electrocardio) information data; S2: preprocessing information data; S3: carrying out feature extraction on the information data processed in the step S2; S4: dividing the information data processed in the S3 into a training set and a test set; S5: obtaining a training template library by the training set; and S6: comparing the similarities of the information data of the testing set and the information data of the template library, so as to determine the owner of the ECG information data. According to the method, the electrocardiosignals can be processed to realize the identity recognition, and the reliability of the recognition is strong, the antijamming capability is strong, and the recognition accuracy is high.
Description
Technical field
The present invention relates to field of biological recognition, particularly relate to a kind of personal identification method based on electrocardiosignal.
Background technology
Along with the raising of social informatization degree, information security is more and more subject to people and payes attention to.And the identity verification mode of the forms such as original user and password is just being subject to various invasions, how to obtain the identification of safety and precise more, become the emphasis of current technical research.Biological identification technology makes this problem effectively be solved.Biological identification technology is to utilize computing machine and the intrinsic physiological property of human body to carry out the technology of discriminate individuals feature.
Identity recognizing technology based on biological characteristic is rapidly developed with advantages such as its high security, uniqueness, stability and validity, and current biological identification technology mainly contains fingerprint recognition, hand identification, iris recognition, face recognition, voice recognition, Gait Recognition, DNA identification etc.These biological characteristics have its unique advantage in some field, but also have certain limitation or defect at aspects such as accuracy rate, antifalsification and applicabilities.
Everyone heart organ form, frequency etc. are different separately, and ecg wave form is difficult for losing, and are difficult to plagiarize, and can represent uniquely my feature.Thereby electrocardio also can be applied to field of biological recognition as fingerprint, people's face etc.
At present, mainly contain the personal identification method based on resolving feature and transform characteristics.At the beginning of 21 century, Lena Biel proposes a kind of new personal identification method the earliest, adopts each a plurality of unique point of leading of 12 electrocardiosignals that lead as personal feature, after principal component analysis (PCA) is processed, carries out identification.Gahi etc. select 9 optimum features and obtain 100% recognition correct rate in 16 test samples from 24 amplitudes and interval feature.In addition, also occurred the personal identification method based on fusion feature, but because its data processing amount is larger, recognition speed is slower.
Prior art gathers the equipment operating complexity of electrocardiosignal, and data volume and noise are more, and signal-data processing difficulty is larger, distinguishes accuracy not high.Part Methods identification rate relies on the accuracy rate of waveform character point extraction, and when some ripple is small cannot measure time, its relevant proper vector cannot obtain.Prior art is not generally considered the impact that changes in heart rate is brought waveform, and directly using one or average period waveform generation transform characteristics as recognition feature, thereby affect the accuracy rate of identification.
Summary of the invention
The present invention adopts a kind of personal identification method based on electrocardiosignal, thereby makes the reliability of electrocardiosignal identification strong, and antijamming capability is strong, and recognition accuracy is high.
The present invention adopts following scheme:
A personal identification method based on electrocardiosignal, comprising:
S1: gather ecg information data;
S2: to described information data pre-service;
S3: the information data after S2 processes is carried out to feature extraction;
S4: the information data after S3 processes is divided into training set and test set;
S5: obtain training set template base by training set;
S6: test set information data and template base information data are carried out to similarity comparison, determine ecg information data owner.
Preferably, gather ecg information data, use electrocardiogram acquisition equipment to gather the data of different object different time sections.
Preferably, described information data pre-service is comprised described information data is removed to noise and baseline wander.
Preferably, use median filter and Wavelet Transformation Algorithm to remove noise and baseline wander.
The method of preferably, the information data after S2 processes being carried out to feature extraction comprises:
S501: the QRS that adopts So and Chan QRS detection algorithm to detect electrocardiosignal involves R wave-wave peak;
S502: the crest of take is found out all monocycle signal as boundary, and each monocycle signal is as a proper vector;
S503: to each proper vector normalized, carry out cubic spline interpolation processing on transverse axis on the longitudinal axis;
S504: a plurality of normalized monocycle electrocardiosignals are carried out to cluster analysis;
S505: choose signal data in maximum classification as the set of eigenvectors of this sample objects.
Preferably, described information data after S3 processes is divided into training set and test set is mean allocation.
Preferably, the described method that test set information data and template base information data are carried out to similarity comparison adopts Euclidean distance comparison algorithm, comprising: the Euclidean distance comparison algorithm of average and the Euclidean distance algorithm of matrix;
The Euclidean distance comparison algorithm of average is: establish two n+1 dimensional vectors, X=[x
0, x
1, x
2..., x
n], Y=[y
0, y
1, y
2..., y
n], their Euclidean distance is:
Utilize Euclidean distance as the similarity measurement of two characteristic curvees.In training set, have N electrocardiosignal, the k group monocycle vector that each electrocardiosignal obtains after the processing of three steps, gets the average value vector of k group monocycle vector as template
the training set data storehouse that forms altogether N vectorial template.In test set, each electrocardiosignal obtains k group monocycle vector after pre-service and feature extraction, asks respectively k group vector to training set data N vectorial template
euclidean distance, the Euclidean distance of corresponding each vectorial template.After vote in majority, select its minimum value as final value, such electrocardiosignal and training set template relatively after, obtain N value.If test set data have M electrocardiosignal, just can obtain the matrix of a M*N, the row n at the minimum value place that wherein m is capable, maximum for n template signal similarity in m electrocardiosignal in test set and training set, be ecg information data owner;
The Euclidean distance algorithm of matrix is: the Euclidean distance algorithm of matrix is on the Euclidean distance algorithm basis of average, the N of a training set electrocardiosignal is got to its k group monocycle vector data, then compare with test set k group monocycle vector, get minimum value as the distance measure of two electrocardiosignals, finally obtain the distance matrix of M*N, equally, the row n at the minimum value place that wherein m is capable, maximum for n signal similar degree in m electrocardiosignal in test set and training set, be ecg information data owner.
Compared with prior art, the present invention adopts a kind of personal identification method based on electrocardiosignal, realizes the processing of the electrocardiosignal collecting and identification, and identification certainty is strong, and antijamming capability is strong, and recognition accuracy is high.
Accompanying drawing explanation
Fig. 1 is a kind of personal identification method process flow diagram based on electrocardiosignal of the embodiment of the present invention;
Fig. 2 is the database thermal map of the embodiment of the present invention 1 indication;
Fig. 3 is the database scale-of-two thermal map of the embodiment of the present invention 1 indication;
Fig. 4 is that the sampling duration of the embodiment of the present invention 2 indications is the database scale-of-two thermal map of 5 seconds;
Fig. 5 is that the sampling duration of the embodiment of the present invention 2 indications is the database scale-of-two thermal map of 10 seconds;
Fig. 6 is that the sampling duration of the embodiment of the present invention 2 indications is the database scale-of-two thermal map of 20 seconds;
Fig. 7 is that the sampling duration of the embodiment of the present invention 2 indications is the database scale-of-two thermal map of 40 seconds.
Embodiment
Referring to shown in Fig. 1, is a kind of personal identification method process flow diagram based on electrocardiosignal of the present embodiment.
The method comprises the steps: a kind of personal identification method based on electrocardiosignal, comprising:
S1: gather ecg information data.
Gather ecg information data, use common electrocardiogram acquisition equipment to gather the data of different object different time sections.
S2: to information data pre-service.
Use median filter and Wavelet Transformation Algorithm to remove noise and baseline wander to information data.
S3: the information data after S2 processes is carried out to feature extraction.
The QRS that adopts So and Chan QRS detection algorithm to detect electrocardiosignal involves R wave-wave peak, and the crest of take is found out all monocycle signal as boundary, and each monocycle signal is as a proper vector; At the longitudinal axis (voltage axis), go up each proper vector normalization [0,1] between, on transverse axis (time shaft), carry out cubic spline interpolation processing, interpolation is spaced apart X=[0:0.01:1], a plurality of normalized monocycle electrocardiosignals are carried out to cluster analysis, choose signal data in maximum classification as the set of eigenvectors of this sample objects.
S4: the information data after S3 processes is divided into training set and test set.
Information data is equally assigned into training set and test set.
S5: obtain training set template base by training set.
S6: test set information data and template base information data are carried out to similarity comparison, determine ecg information data owner.
The method of test set information data and template base information data being carried out to similarity comparison adopts Euclidean distance comparison algorithm, comprising: the Euclidean distance comparison algorithm of average and the Euclidean distance algorithm of matrix.
The Euclidean distance comparison algorithm of average is: establish two n+1 dimensional vectors, X=[x
0, x
1, x
2..., x
n], Y=[y
0, y
1, y
2..., y
n], their Euclidean distance is:
Utilize Euclidean distance as the similarity measurement of two characteristic curvees.In training set, have N electrocardiosignal, the k group monocycle vector that each electrocardiosignal obtains after the processing of three steps, gets the average value vector of k group monocycle vector as template
the training set data storehouse that forms altogether N vectorial template.In test set, each electrocardiosignal obtains k group monocycle vector after pre-service and feature extraction, asks respectively k group vector to training set data N vectorial template
euclidean distance, the Euclidean distance of corresponding each vectorial template.After vote in majority, select its minimum value as final value, such electrocardiosignal and training set template relatively after, obtain N value.If test set data have M electrocardiosignal, just can obtain the matrix of a M*N, the row n at the minimum value place that wherein m is capable, maximum for n template signal similarity in m electrocardiosignal in test set and training set, be ecg information data owner.
The Euclidean distance algorithm of matrix is: the Euclidean distance algorithm of matrix is on the Euclidean distance algorithm basis of average, the N of a training set electrocardiosignal is got to its k group monocycle vector data, then compare with test set k group monocycle vector, get minimum value as the distance measure of two electrocardiosignals, finally obtain the distance matrix of M*N, equally, the row n at the minimum value place that wherein m is capable, maximum for n signal similar degree in m electrocardiosignal in test set and training set, be ecg information data owner.
embodiment 1:
In the present embodiment, adopt disclosed QT database as case, as table 1:
Table 1
| MIT-BIH | MIT-BIH | MIT-BIH | MIT-BIH | ESC | MIT-BIH | Sudden |
| Arrhyt. | ST?DB | Sup.Vent. | Long?Term | STT | NSR?DB | Death |
| 15 | 6 | 13 | 4 | 33 | 10 | 24 |
In QT database data acquisition totally 105 data of 7 disparate databases, wherein Healthy People data are 14,91 of other heart patient's data.Its data sampling frequency is 250HZ.
The present embodiment adopts above-mentioned a kind of personal identification method based on electrocardiosignal to carry out identification, in data after the processing of said method, its result represents as Fig. 3 as scale-of-two thermal map in Fig. 2 and database with database thermal map, and horizontal ordinate is training set, and ordinate is test set.The accuracy rate of its electrocardio identification is 92.38%.
embodiment 2:
In the present embodiment, adopt disclosed PTB database as case, as table 2:
Table 2
| Diagnostic-type | Quantity |
| Myocardial infarction (Myocardial infarction) | 148 |
| (Cardiomyopathy/Heart failure) in |
18 |
| Chamber bundle-branch block (Bundle branch block) | 15 |
| Dysrhythmia (Dysrhythmia) | 14 |
| Myocardial hypertrophy (Myocardial hypertrophy) | 7 |
| Valvulopathy (Valvular heart disease) | 6 |
| Myocarditis (Myocarditis) | 4 |
| Other (Miscellaneous) | 4 |
| Normal healthy controls group (Healthy controls) | 52 |
Totally 268 data in QT database, wherein Healthy People data are 52,216 of other heart patient's data.Its data sampling frequency is 1000HZ.
The present embodiment adopts above-mentioned a kind of personal identification method based on electrocardiosignal to carry out identification, and the difference sampling duration for PTB database comprises 5 seconds, 10 seconds, 20 seconds, 40 seconds, and electrocardio identity has been carried out analyzing identification.Result as in data after the processing of said method, its result represents with scale-of-two thermal map in database, as Fig. 4, sampling duration is the database scale-of-two thermal map of 5 seconds; As Fig. 5, sampling duration is the database scale-of-two thermal map of 10 seconds; As Fig. 6, sampling duration is the database scale-of-two thermal map of 20 seconds; As Fig. 7, sampling duration is the database scale-of-two thermal map of 40 seconds, and horizontal ordinate is training set, and ordinate is test set.The accuracy rate of its electrocardio identification is: sampling duration is the accuracy rate 94.23% of 5 seconds; Sampling duration is the accuracy rate 96.15% of 10 seconds; Sampling duration is the accuracy rate 98.07% of 20 seconds; Sampling duration is the accuracy rate 100% of 40 seconds.
The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, all any modifications of doing within the spirit and principles in the present invention, be equal to and replace and improvement etc., within all should being included in protection scope of the present invention.
Claims (7)
1. the personal identification method based on electrocardiosignal, is characterized in that, comprising:
S1: gather ecg information data;
S2: to described information data pre-service;
S3: the information data after S2 processes is carried out to feature extraction;
S4: the information data after S3 processes is divided into training set and test set;
S5: obtain training set template base by training set;
S6: test set information data and template base information data are carried out to similarity comparison, determine ecg information data owner.
2. method according to claim 1, is characterized in that, gathers ecg information data, uses electrocardiogram acquisition equipment to gather the data of different object different time sections.
3. method according to claim 1, is characterized in that, described information data pre-service is comprised described information data is removed to noise and baseline wander.
4. method according to claim 3, is characterized in that, uses median filter and Wavelet Transformation Algorithm to remove noise and baseline wander.
5. method according to claim 1, is characterized in that, wherein, the method for the information data after S2 processes being carried out to feature extraction comprises:
S501: the QRS that adopts So and Chan QRS detection algorithm to detect electrocardiosignal involves R wave-wave peak;
S502: the crest of take is found out all monocycle signal as boundary, and each monocycle signal is as a proper vector;
S503: to each proper vector normalized, carry out cubic spline interpolation processing on transverse axis on the longitudinal axis;
S504: a plurality of normalized monocycle electrocardiosignals are carried out to cluster analysis;
S505: choose signal data in maximum classification as the set of eigenvectors of this sample objects.
6. method according to claim 1, is characterized in that, described information data after S3 processes is divided into training set and test set is mean allocation.
7. method according to claim 1, it is characterized in that, the described method that test set information data and template base information data are carried out to similarity comparison adopts the Euclidean distance comparison algorithm of matrix, comprising: the Euclidean distance comparison algorithm of average and the Euclidean distance algorithm of matrix;
The Euclidean distance comparison algorithm of average is: establish two n+1 dimensional vectors, X=[x
0, x
1, x
2..., x
n], Y=[y
0, y
1, y
2..., y
n], their Euclidean distance is:
Utilize Euclidean distance as the similarity measurement of two characteristic curvees.In training set, have N electrocardiosignal, the k group monocycle vector that each electrocardiosignal obtains after the processing of three steps, gets the average value vector of k group monocycle vector as template
the training set data storehouse that forms altogether N vectorial template.In test set, each electrocardiosignal obtains k group monocycle vector after pre-service and feature extraction, asks respectively k group vector to training set data N vectorial template
euclidean distance, the Euclidean distance of corresponding each vectorial template.After vote in majority, select its minimum value as final value, such electrocardiosignal and training set template relatively after, obtain N value.If test set data have M electrocardiosignal, just can obtain the matrix of a M*N, the row n at the minimum value place that wherein m is capable, maximum for n template signal similarity in m electrocardiosignal in test set and training set, be ecg information data owner;
The Euclidean distance algorithm of matrix is: the Euclidean distance algorithm of matrix is on the Euclidean distance algorithm basis of average, the N of a training set electrocardiosignal is got to its k group monocycle vector data, then compare with test set k group monocycle vector, get minimum value as the distance measure of two electrocardiosignals, finally obtain the distance matrix of M*N, equally, the row n at the minimum value place that wherein m is capable, maximum for n signal similar degree in m electrocardiosignal in test set and training set, be ecg information data owner.
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