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Identification method and identification system using identification method

A technology for identification and identification, applied in the field of identification, can solve the problems of low frequency, mainly between 10Hz-15Hz, unobservable, large slope, etc.

Inactive Publication Date: 2010-07-14
SCI RES TRAINING CENT FOR CHINESE ASTRONAUTS
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  • Application Information

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Problems solved by technology

The waveforms in the electrocardiogram are the comprehensive effects of many cardiomyocyte action potentials on the body surface. The P wave reflects the depolarization process of the atrial muscle, and its frequency is low, mainly between 10Hz-15Hz; the QRS wave reflects the depolarization process of the ventricular muscle. During the polar process, its waveform is steeper and the slope is larger, mainly between 10Hz-40Hz; T wave reflects the repolarization process of ventricular muscle, and its frequency is mainly between 10Hz-15Hz; The polar process is covered by the QRS wave and cannot be observed

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  • Identification method and identification system using identification method
  • Identification method and identification system using identification method
  • Identification method and identification system using identification method

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Embodiment approach 1

[0047] The identification method according to this embodiment includes processes such as ECG signal input, ECG signal preprocessing, feature extraction, identification, and result output. The flow chart of the identification method according to this embodiment is as follows figure 2 shown. These processes are described separately below.

[0048] 1. ECG signal acquisition

[0049] The present invention utilizes the Medilog AR12 (holter) of Oxford Instrument Company to collect electrocardiogram, the sampling frequency is 1024Hz, and the quantization is 16bit. Of course, it is also possible to use other instruments, or adopt different sampling frequencies and bit rates, as long as the present invention can be realized. Each person collects two ECG signals, each for 2 minutes, and the time interval between the two ECG signals is more than one day, so as to ensure the robustness of the ECG identification method under heart rate variability. 30 segments of ECG data from differe...

Embodiment approach 2

[0070] The identification method according to this embodiment includes, in addition to the above-mentioned identification method based on ECG signals, a fingerprint identification method, that is, combined identification is performed through ECG signals and fingerprint features. In this embodiment, it includes the ECG identification process and fingerprint identification process carried out sequentially, including the following steps: (a) signal acquisition, wherein the ECG signal and fingerprint image of the human body are collected synchronously; (b) ECG signal preprocessing, The collected ECG signal is filtered; (c) ECG feature extraction, wherein the ECG feature is extracted, and the ECG feature vector is established; (d) ECG identity recognition, wherein the ECG feature vector of the person to be identified is pre-stored in the ECG feature template library Compared with the ECG feature vectors in ECG, when the ECG identification is successful, the next step of fingerprint ...

Embodiment approach 3

[0074] In this embodiment, ECG signals and fingerprint features are also used for combined identification. The difference between this embodiment and the second embodiment is that in this embodiment, instead of performing identity recognition and fingerprint recognition based on ECG signals separately, the extracted fingerprint features and ECG features are fused together, and the established The feature vector of is used as the identification feature vector. Specifically, the method includes the following steps: (a) signal acquisition, wherein the ECG signal and the fingerprint image of the human body are synchronously collected; (b) ECG signal preprocessing, wherein the collected ECG signal is filtered; (c) ECG feature extraction, wherein the ECG feature is extracted; (d) fingerprint image preprocessing, wherein the fingerprint image is preprocessed; (e) fingerprint feature extraction, wherein the fingerprint feature is extracted; (f) fingerprint feature and ECG feature fusi...

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Abstract

The invention provides an identification method, which performs identification by using an electrocardio (ECG) signal. The method comprises the following steps of: (a) ECG signal collection; (b) ECG signal pre-treatment, wherein the collected ECG signal is filtered; (c) characteristic extraction, wherein the characteristics of the ECG signal are extracted to build an identification characteristic vectors and the built identification characteristic vectors comprise analysis characteristics, presentative characteristics, transformation-domain characteristics and fusion characteristics; (d) identification process, wherein the identification characteristic vectors of a person to be identified is compared with the identification characteristic vectors which are pre-stored in an ECG characteristic template base; and (e) result output. The invention also provides an identification system using the identification method.

Description

technical field [0001] The present invention relates to an identification method, more specifically to a biometric identification method using electrocardiographic signals for identification. The invention also relates to an identification system applying the identification method. Background technique [0002] With the development of computer network and electronic technology, a new identity verification method has emerged to replace traditional passwords and passwords - biometric identification technology. Biometric Identification Technology (Biometric Identification Technology, BIT) refers to a technology that uses human biological characteristics or behavioral characteristics for identity authentication. [1] . Biological characteristics are unique (different from others), physiological characteristics or behavior patterns that can be measured or automatically identified and verified, and are divided into physiological characteristics and behavioral characteristics. Th...

Claims

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Application Information

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IPC IPC(8): A61B5/117A61B5/0402A61B5/1171A61B5/1172
Inventor 杨向林严洪任兆瑞宋晋忠轩永姚宇华梁仲刚李延军杨辉
Owner SCI RES TRAINING CENT FOR CHINESE ASTRONAUTS
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