CN111324880A - Fingerprint and electrocardio characteristic double-authentication identity recognition system and method - Google Patents
Fingerprint and electrocardio characteristic double-authentication identity recognition system and method Download PDFInfo
- Publication number
- CN111324880A CN111324880A CN202010157848.0A CN202010157848A CN111324880A CN 111324880 A CN111324880 A CN 111324880A CN 202010157848 A CN202010157848 A CN 202010157848A CN 111324880 A CN111324880 A CN 111324880A
- Authority
- CN
- China
- Prior art keywords
- fingerprint
- electrocardio
- human body
- electrocardio detection
- fingertip
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000001514 detection method Methods 0.000 claims abstract description 61
- 239000002184 metal Substances 0.000 claims abstract description 12
- 238000001208 nuclear magnetic resonance pulse sequence Methods 0.000 claims abstract description 11
- 230000001360 synchronised effect Effects 0.000 claims abstract description 7
- 238000000718 qrs complex Methods 0.000 claims description 17
- 238000005070 sampling Methods 0.000 claims description 9
- 230000010354 integration Effects 0.000 claims description 8
- 230000003321 amplification Effects 0.000 claims description 3
- 230000002238 attenuated effect Effects 0.000 claims description 3
- 230000004069 differentiation Effects 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 208000010201 Exanthema Diseases 0.000 description 2
- 201000005884 exanthem Diseases 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 206010037844 rash Diseases 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
- A61B5/349—Detecting specific parameters of the electrocardiograph cycle
- A61B5/366—Detecting abnormal QRS complex, e.g. widening
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Cardiology (AREA)
- Physics & Mathematics (AREA)
- Molecular Biology (AREA)
- Public Health (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Biophysics (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Pathology (AREA)
- Veterinary Medicine (AREA)
- Computer Security & Cryptography (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Hardware Design (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention provides a fingerprint and electrocardio characteristic double-authentication identity recognition system and a method, wherein the system comprises a computer host, human body electrocardio detection equipment, a fingerprint recognition module and a fingertip electrocardio detection module; the fingerprint identification module is used for collecting fingerprint information and comprises a finger touch pressure identification area and a metal electrode surrounding the finger touch pressure identification area, and the metal electrode is connected with the fingertip electrocardio detection module; the method comprises the following steps: firstly, a patient registers information on a computer host and inputs a fingerprint; then after the body electrocardio detection equipment is installed on the patient, pressing a finger into a finger touch pressure identification area of a fingerprint identification module, and acquiring the fingerprint information of the patient by the fingerprint identification module and comparing the fingerprint information with the registered fingerprint information; if the registered user is determined, acquiring finger electrocardiosignals and body electrocardiosignals, and if the QRS wave group pulse sequences of the finger electrocardiosignals and the body electrocardiosignals are synchronous, carrying out identity authentication and then carrying out electrocardio detection by human body electrocardio detection equipment.
Description
Technical Field
The invention relates to a fingerprint and electrocardio characteristic double-authentication identity recognition system and method.
Background
When clinical electrocardio collection in a hospital is carried out, the record is generally carried out by adopting a name of a person. However, because the number of patients to be examined is large, the operator cannot be familiar with each patient, and thus, the operator is prone to mistakes. In addition, some patients participate in the test instead of self-presenting in order to avoid the test or to obtain the desired test results. These undoubtedly affect the accuracy of the electrocardiographic measurements and the judgment of rash treatment by the subsequent physician.
Disclosure of Invention
In order to overcome the problems in the prior art, the invention provides a fingerprint and electrocardio characteristic double-authentication identity recognition system and method, which can realize effective patient identity characteristic authentication and avoid electrocardio acquisition errors, and has the following specific technical contents:
a fingerprint and electrocardio-characteristic double-authentication identity recognition system comprises: the human body electrocardio detection device, the fingerprint identification module and the fingertip electrocardio detection module are connected with the computer host;
the fingerprint identification module is used for collecting fingerprint information and comprises a finger touch pressure identification area and a metal electrode surrounding the finger touch pressure identification area, and the metal electrode is connected with the fingertip electrocardio detection module;
the fingertip electrocardio detection module is used for acquiring the electrocardio information from a fingertip while collecting the fingerprint information;
the computer host compares the collected fingerprint signal with the registered fingerprint information, compares the electrocardiosignals respectively collected by the fingertip electrocardio detection module and the human body electrocardio detection equipment after determining that the user is registered, and immediately performs the electrocardio detection through identity authentication if the QRS wave group pulse sequences of the fingertip electrocardio detection module and the human body electrocardio detection equipment are synchronous.
A fingerprint and electrocardio characteristic double-authentication identity recognition method comprises the following operation steps:
firstly, a patient registers information on a computer host and inputs a fingerprint;
then, after the human body electrocardio detection equipment is installed on the patient, the patient firstly carries out identity recognition; the step of identity recognition comprises the following steps: the patient presses a finger into a finger touch pressure identification area of the fingerprint identification module, the fingerprint identification module acquires the fingerprint information of the patient, and the fingerprint information is transmitted to the computer host and is compared with the registered fingerprint information; if the user is determined to be the registered user, carrying out the next authentication;
a metal electrode is arranged on the finger touch pressure identification area and is used for collecting finger electrocardiosignals through a fingertip; meanwhile, the human body electrocardiosignal detection equipment carries out electrocardio detection through the human body electrocardiosignal and the QRS wave group pulse sequence of the human body electrocardiosignal detection equipment are synchronous, and then the human body electrocardiosignal detection equipment carries out electrocardio detection through identity authentication.
In one or more embodiments of the present invention, the QRS complex pulse sequence is detected by:
firstly, differentiating the electrocardiosignals after amplification and filtration, then taking absolute values and time domain average to the signals, differentiating the signals to obtain the slope information of QRS complex waves, and selecting a differential transfer function used by a three-point center difference algorithm to accelerate the algorithm operation speed as follows:
H(z)＝0.5(1-z-2)；
after differentiation, the QRS complex wave is further enhanced, and the P wave and the T wave are further attenuated;
next, taking absolute values of the signal data;
then, moving window integration is carried out on the signal, and the difference equation used by the moving window integration is as follows:
y(nT)＝1；
N[x(nT-(n-1)T)+x(nT-(n-2)T)+…+x(nT)](5)；
where N is the number of sampling points of the width of the moving window;
and (4) retaining QRS complex wave information by the final moving window integration, and detecting the QRS wave by using a threshold value method on the basis.
In one or more embodiments of the present invention, a 100ms window is selected, and the window width sampling point N is 20 calculated according to a sampling frequency of 200 times/s.
The invention has the beneficial effects that: through the dual authentication of the fingerprint and the electrocardiosignal, the identity of the patient can be ensured, the error of electrocardio acquisition is avoided, particularly, the situation that other people replace the patient to participate in detection can be prevented, and the accuracy of the electrocardio detection and the rash treatment judgment of a subsequent doctor are ensured. Meanwhile, according to the characteristics of low power consumption and small memory space of the system, QRS complex wave identification is carried out by analyzing slope, amplitude and width identification, and optimization is carried out aiming at the specific characteristics of the single chip microcomputer, so that the processing speed of the algorithm is increased, the result that the square is easy to overflow is avoided, the influence on the final complex wave detection effect is extremely small, and the accuracy of the detection result is ensured.
Drawings
Fig. 1 is a schematic diagram of a fingerprint and electrocardiographic feature double-authentication identity recognition system of the present invention.
Fig. 2 is a block diagram of the process of detecting QRS complex pulse sequence according to the present invention.
FIG. 3 is a waveform diagram of the detection process of the present invention at each phase of the electrocardiograph signal.
Detailed Description
The application scheme is further described below with reference to the accompanying drawings:
referring to fig. 1, a fingerprint and electrocardiogram feature double-authentication identity recognition system includes: the human body electrocardio detection device comprises a computer host 1, and a human body electrocardio detection device 2, a fingerprint identification module 3 and a fingertip electrocardio detection module 4 which are connected with the computer host 1; the fingerprint identification module 3 is used for collecting fingerprint information, and includes a finger touch and pressure identification area 31 and a metal electrode (metal frame) 32 surrounding the finger touch and pressure identification area 31, and the metal electrode 32 is connected with the fingertip electrocardiogram detection module 3; the fingertip electrocardio detection module 3 is used for acquiring the electrocardio information from a fingertip while collecting fingerprint information; the computer host 1 compares the collected fingerprint signals with the registered fingerprint information, compares the electrocardiosignals respectively collected by the fingertip electrocardio detection module 4 and the human body electrocardio detection device 2 after determining that the user is a registered user, and immediately performs the electrocardio detection through identity authentication if the QRS wave group pulse sequences of the two are synchronous.
Referring to fig. 2 and 3, a fingerprint and electrocardiogram feature double-authentication identity recognition method comprises the following operation steps:
firstly, a patient registers information on a computer host 1 and inputs a fingerprint;
then, after the human body electrocardio detection device 2 is installed on the patient, the patient firstly carries out identity recognition; the step of identity recognition comprises the following steps: the patient presses the finger into the finger touch pressure identification area 31 of the fingerprint identification module 3, the fingerprint identification module 3 acquires the fingerprint information of the patient, and the fingerprint information is transmitted to the computer host 1 and compared with the registered fingerprint information; if the user is determined to be the registered user, carrying out the next authentication;
a metal electrode 32 is arranged on the finger touch pressure identification area 31 and is used for collecting finger electrocardiosignals through fingertips; meanwhile, the human body electrocardiosignal detection device 2 carries out electrocardio detection through the human body electrocardiosignal, if the QRS wave group pulse sequences of the human body electrocardiosignal detection device and the human body electrocardiosignal detection device are synchronous, the identity authentication is carried out, and then the human body electrocardiosignal detection device 2 carries out electrocardio detection.
The QRS complex detection methods are various, such as a difference threshold method, a template matching method, a wavelet transform method, a neural network method, and the like. The invention provides a real-time QRS complex wave detection algorithm aiming at an 8-bit singlechip, which identifies QRS complex waves by analyzing slope, amplitude and width identification and optimizes the characteristics of the singlechip according to the characteristics of low power consumption and small memory space of a system.
The QRS complex pulse sequence detection method comprises the following steps:
firstly, differentiating the electrocardiosignals after amplification and filtration, then taking absolute values and time domain average to the signals, differentiating the signals to obtain the slope information of QRS complex waves, and selecting a differential transfer function used by a three-point center difference algorithm to accelerate the algorithm operation speed as follows:
H(z)＝0.5(1-z-2)；
after differentiation, the QRS complex wave is further enhanced, and the P wave and the T wave are further attenuated;
then, an absolute value is obtained from the signal data, in order to enable the data to be in a positive value before the next operation, the method adopted in the prior art is to take the square of the signal data, and through experiments, the invention adopts a mode of taking the absolute value, thereby not only accelerating the processing speed of the algorithm, but also avoiding the consequence that the square is easy to overflow, and having little influence on the final complex wave detection effect;
then, moving window integration is carried out on the signal, and the difference equation used by the moving window integration is as follows:
y(nT)＝1；
N[x(nT-(n-1)T)+x(nT-(n-2)T)+…+x(nT)](5)；
n is the number of sampling points of the width of the moving window, the width given in the traditional algorithm is 150ms, and experiments show that the data effect finally obtained by a slightly narrower window is better, so that the window width sampling point N is 20 by selecting the window of 100ms and calculating according to the sampling frequency of 200 times/s. Comparing the original electrocardiosignal with the processed waveforms at each stage, it can be seen that the final moving window integral retains most information of the QRS complex wave and filters other waveforms, and on the basis, the QRS wave can be conveniently detected by using a threshold value method.
The above preferred embodiments should be considered as examples of the embodiments of the present application, and technical deductions, substitutions, improvements and the like similar to, similar to or based on the embodiments of the present application should be considered as the protection scope of the present patent.
Claims (4)
1. A fingerprint and electrocardio characteristic double-authentication identity recognition system is characterized by comprising: the human body electrocardio detection device, the fingerprint identification module and the fingertip electrocardio detection module are connected with the computer host;
the fingerprint identification module is used for collecting fingerprint information and comprises a finger touch pressure identification area and a metal electrode surrounding the finger touch pressure identification area, and the metal electrode is connected with the fingertip electrocardio detection module;
the fingertip electrocardio detection module is used for acquiring the electrocardio information from a fingertip while collecting the fingerprint information;
the computer host compares the collected fingerprint signal with the registered fingerprint information, compares the electrocardiosignals respectively collected by the fingertip electrocardio detection module and the human body electrocardio detection equipment after determining that the user is registered, and immediately performs the electrocardio detection through identity authentication if the QRS wave group pulse sequences of the fingertip electrocardio detection module and the human body electrocardio detection equipment are synchronous.
2. A fingerprint and electrocardio characteristic double-authentication identity recognition method is characterized by comprising the following operation steps:
firstly, a patient registers information on a computer host and inputs a fingerprint;
then, after the human body electrocardio detection equipment is installed on the patient, the patient firstly carries out identity recognition; the step of identity recognition comprises the following steps: the patient presses a finger into a finger touch pressure identification area of the fingerprint identification module, the fingerprint identification module acquires the fingerprint information of the patient, and the fingerprint information is transmitted to the computer host and is compared with the registered fingerprint information; if the user is determined to be the registered user, carrying out the next authentication;
a metal electrode is arranged on the finger touch pressure identification area and is used for collecting finger electrocardiosignals through a fingertip; meanwhile, the human body electrocardiosignal detection equipment carries out electrocardio detection through the human body electrocardiosignal and the QRS wave group pulse sequence of the human body electrocardiosignal detection equipment are synchronous, and then the human body electrocardiosignal detection equipment carries out electrocardio detection through identity authentication.
3. The fingerprint and electrocardiogram feature double-authentication identity recognition method of claim 2, wherein the QRS complex pulse sequence detection comprises the following steps:
firstly, differentiating the electrocardiosignals after amplification and filtration, then taking absolute values and time domain average to the signals, differentiating the signals to obtain the slope information of QRS complex waves, and selecting a differential transfer function used by a three-point center difference algorithm to accelerate the algorithm operation speed as follows:
H(z)＝0.5(1-z-2)；
after differentiation, the QRS complex wave is further enhanced, and the P wave and the T wave are further attenuated;
next, taking absolute values of the signal data;
then, moving window integration is carried out on the signal, and the difference equation used by the moving window integration is as follows:
y(nT)＝1；
N[x(nT-(n-1)T)+x(nT-(n-2)T)+…+x(nT)](5)；
where N is the number of sampling points of the width of the moving window;
and (4) retaining QRS complex wave information by the final moving window integration, and detecting the QRS wave by using a threshold value method on the basis.
4. The fingerprint and electrocardiogram feature double-authentication identity recognition method according to claim 3, wherein: and selecting a 100ms window, and calculating according to the sampling frequency of 200 times/s, wherein the sampling point N of the window width is 20.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010157848.0A CN111324880A (en) | 2020-03-09 | 2020-03-09 | Fingerprint and electrocardio characteristic double-authentication identity recognition system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010157848.0A CN111324880A (en) | 2020-03-09 | 2020-03-09 | Fingerprint and electrocardio characteristic double-authentication identity recognition system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111324880A true CN111324880A (en) | 2020-06-23 |
Family
ID=71167323
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010157848.0A Pending CN111324880A (en) | 2020-03-09 | 2020-03-09 | Fingerprint and electrocardio characteristic double-authentication identity recognition system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111324880A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116028914A (en) * | 2023-03-27 | 2023-04-28 | 深圳市魔样科技有限公司 | Intelligent finger ring identity authentication method and system |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202342029U (en) * | 2011-08-19 | 2012-07-25 | 深圳市永盟智能信息系统有限公司 | Intelligent ECG (electrocardiograph) sampling system using fingerprint to identify people |
| CN103714281A (en) * | 2013-12-12 | 2014-04-09 | 深圳先进技术研究院 | Identity recognition method based on electrocardiosignals |
| CN104287717A (en) * | 2014-09-30 | 2015-01-21 | 杭州电子科技大学 | Double-electrode based finger electrocardio identity recognition system |
| CN104771178A (en) * | 2015-04-13 | 2015-07-15 | 深圳市飞马与星月科技研究有限公司 | Method and device of identity recognition |
| CN106264518A (en) * | 2016-08-04 | 2017-01-04 | 东莞市第三人民医院 | A kind of based on finger tip Electrocardiographic atrial fibrillation detection method and device |
| CN107092874A (en) * | 2017-04-10 | 2017-08-25 | 山东大学 | Personal identification method, apparatus and system based on electrocardio and fingerprint fusion feature |
| WO2018023884A1 (en) * | 2016-08-04 | 2018-02-08 | 深圳先进技术研究院 | Device and method for identity recognition |
| CN107788969A (en) * | 2017-09-29 | 2018-03-13 | 成都瑞迪康医疗科技有限公司 | The automatic testing method of QRS complex in a kind of electrocardiosignal |
| CN108932416A (en) * | 2018-05-12 | 2018-12-04 | 广东可穿戴数字技术有限公司 | A method of confirmed based on fingerprint and electrocardio dual identity |
| CN109009143A (en) * | 2018-07-12 | 2018-12-18 | 杭州电子科技大学 | A method of ecg information is predicted by body gait |
-
2020
- 2020-03-09 CN CN202010157848.0A patent/CN111324880A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202342029U (en) * | 2011-08-19 | 2012-07-25 | 深圳市永盟智能信息系统有限公司 | Intelligent ECG (electrocardiograph) sampling system using fingerprint to identify people |
| CN103714281A (en) * | 2013-12-12 | 2014-04-09 | 深圳先进技术研究院 | Identity recognition method based on electrocardiosignals |
| CN104287717A (en) * | 2014-09-30 | 2015-01-21 | 杭州电子科技大学 | Double-electrode based finger electrocardio identity recognition system |
| CN104771178A (en) * | 2015-04-13 | 2015-07-15 | 深圳市飞马与星月科技研究有限公司 | Method and device of identity recognition |
| CN106264518A (en) * | 2016-08-04 | 2017-01-04 | 东莞市第三人民医院 | A kind of based on finger tip Electrocardiographic atrial fibrillation detection method and device |
| WO2018023884A1 (en) * | 2016-08-04 | 2018-02-08 | 深圳先进技术研究院 | Device and method for identity recognition |
| CN107092874A (en) * | 2017-04-10 | 2017-08-25 | 山东大学 | Personal identification method, apparatus and system based on electrocardio and fingerprint fusion feature |
| CN107788969A (en) * | 2017-09-29 | 2018-03-13 | 成都瑞迪康医疗科技有限公司 | The automatic testing method of QRS complex in a kind of electrocardiosignal |
| CN108932416A (en) * | 2018-05-12 | 2018-12-04 | 广东可穿戴数字技术有限公司 | A method of confirmed based on fingerprint and electrocardio dual identity |
| CN109009143A (en) * | 2018-07-12 | 2018-12-18 | 杭州电子科技大学 | A method of ecg information is predicted by body gait |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116028914A (en) * | 2023-03-27 | 2023-04-28 | 深圳市魔样科技有限公司 | Intelligent finger ring identity authentication method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lourenço et al. | Unveiling the biometric potential of finger‐based ECG signals | |
| US4721114A (en) | Method of detecting P-waves in ECG recordings | |
| CN107041743B (en) | Real-time R wave detection method for electrocardiosignals | |
| CN104173043B (en) | Electrocardiogram (ECG) data analysis method suitable for mobile platform | |
| Karvounis et al. | An automated methodology for fetal heart rate extraction from the abdominal electrocardiogram | |
| Lai et al. | Convolutional neural network based detection of atrial fibrillation combing RR intervals and F-wave frequency spectrum | |
| CN111772628B (en) | Electrocardiosignal atrial fibrillation automatic detection system based on deep learning | |
| Kelwade et al. | Radial basis function neural network for prediction of cardiac arrhythmias based on heart rate time series | |
| Ramkumar et al. | A graphical user interface based heart rate monitoring process and detection of pqrst peaks from ecg signal | |
| CN109893118A (en) | A kind of electrocardiosignal classification diagnosis method based on deep learning | |
| CN111832537A (en) | Abnormal electrocardiosignal identification method and abnormal electrocardiosignal identification device | |
| Kuila et al. | Feature extraction of electrocardiogram signal using machine learning classification | |
| Pauk | 419. Different techniques for EMG signal processing. | |
| CN111324880A (en) | Fingerprint and electrocardio characteristic double-authentication identity recognition system and method | |
| CN116784860B (en) | Electrocardiosignal characteristic extraction system based on morphological heart beat template clustering | |
| Singh et al. | Dry contact fingertip ECG-based authentication system using time, frequency domain features and support vector machine | |
| Vuksanovic et al. | Analysis of human electrocardiogram for biometric recognition using analytic and ar modeling extracted parameters | |
| CN102302373A (en) | Method for detecting heart sound signal feature points based on multiplexing multi-resolution wavelet transformation | |
| CN110491504A (en) | A kind of acquisition methods of cardiechema signals medical guidelines data | |
| JP2002078695A (en) | Electrocardiogram measuring instrument | |
| CN114098754A (en) | Atrial fibrillation signal preprocessing method, detection system, equipment and storage medium | |
| Atanasov et al. | An approach of Feature extraction of ECG signal of CLAS database | |
| Jokić et al. | An efficient approach for heartbeat classification | |
| Liang et al. | Detection Algorithm of QRS Wave and R Peak based on Adaptive Energy Segmentation | |
| US20230086822A1 (en) | Biometric verification using characteristic electrophysiological features |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20211111 Address after: 528400 card g2014, west of floor 2, plant 4, Zhongshan port export processing zone, Torch Development Zone, Zhongshan City, Guangdong Province Applicant after: Zhongshan Lianxin Electronic Technology Co.,Ltd. Address before: 518000 802, building 10, Shenzhen Bay science and technology ecological park, No. 10, Gaoxin South ninth Road, high tech Zone community, Yuehai street, Nanshan District, Shenzhen, Guangdong Applicant before: Shenzhen Lianxin Electronic Technology Co.,Ltd. |
|
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200623 |