CN111582138A - Electrocardio identity recognition method and system based on frequency domain cepstrum coefficient characteristics - Google Patents
Electrocardio identity recognition method and system based on frequency domain cepstrum coefficient characteristics Download PDFInfo
- Publication number
- CN111582138A CN111582138A CN202010365770.1A CN202010365770A CN111582138A CN 111582138 A CN111582138 A CN 111582138A CN 202010365770 A CN202010365770 A CN 202010365770A CN 111582138 A CN111582138 A CN 111582138A
- Authority
- CN
- China
- Prior art keywords
- identified
- features
- electrocardiosignals
- frame
- electrocardiosignal
- 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 54
- 238000007781 pre-processing Methods 0.000 claims abstract description 13
- 238000001228 spectrum Methods 0.000 claims description 21
- 230000015654 memory Effects 0.000 claims description 18
- 238000012545 processing Methods 0.000 claims description 18
- 238000004590 computer program Methods 0.000 claims description 11
- 238000005070 sampling Methods 0.000 claims description 9
- 239000013598 vector Substances 0.000 claims description 8
- 238000000605 extraction Methods 0.000 claims description 7
- 238000009432 framing Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 230000011218 segmentation Effects 0.000 claims description 5
- 230000009466 transformation Effects 0.000 claims description 5
- 238000004422 calculation algorithm Methods 0.000 claims description 4
- 238000009499 grossing Methods 0.000 claims description 4
- 238000012952 Resampling Methods 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 abstract description 3
- 230000000747 cardiac effect Effects 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000012850 discrimination method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000003183 myoelectrical effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 238000011426 transformation method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
-
- 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
-
- 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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
- G06F2218/10—Feature extraction by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Animal Behavior & Ethology (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- General Health & Medical Sciences (AREA)
- Surgery (AREA)
- Molecular Biology (AREA)
- Medical Informatics (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Artificial Intelligence (AREA)
- Theoretical Computer Science (AREA)
- Cardiology (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physiology (AREA)
- Psychiatry (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The invention discloses an electrocardio identity recognition method and system based on frequency domain cepstrum coefficient characteristics, which comprises the following steps: acquiring an electrocardiosignal to be identified; preprocessing the electrocardiosignals to be identified; extracting the features of the preprocessed electrocardiosignals to be recognized, and extracting the features to be recognized; and comparing the features to be identified with the features in the electrocardio feature template library, and outputting an identity identification result. According to the method, the electrocardiosignal is analyzed from the angle of the frequency domain, compared with time domain analysis, all components of the signal can be found more intuitively, noise interference is avoided, the extracted cepstrum coefficient can be used as the characteristic of identity recognition to well distinguish the difference between different individuals, and the method has good recognition performance.
Description
Technical Field
The disclosure relates to the technical field of biological feature recognition, in particular to an electrocardio identity recognition method and system based on frequency domain cepstrum coefficient features.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Electrocardiosignals are a biological feature with high safety, which has a relatively obvious difference among different individuals and can be used for identification in addition to being used as a basis for disease diagnosis. In addition, the electrocardiosignals are generated in the human body and are difficult to steal or forge, so the identification based on the electrocardiosignals (called electrocardio identification for short) has good development prospect. Common methods for extracting the electrocardiosignal features include a filter bank method, a threshold discrimination method, wavelet transformation and the like.
However, in the course of implementing the present disclosure, the inventors found that the following technical problems exist in the prior art:
although the filter bank method has real-time performance, the overlapping of noise and signal frequency spectrum cannot be processed; the threshold discrimination method excessively depends on the preprocessing of the signal, and if the signal is seriously interfered by noise, the signal is easy to be detected by mistake or missed; the wavelet transformation method has good anti-interference capability, but lacks adaptivity.
Disclosure of Invention
In order to overcome the defects of the prior art, the present disclosure provides a method and a system for identifying an electrocardiogram based on frequency domain cepstrum coefficient characteristics;
in a first aspect, the present disclosure provides a method for identifying an electrocardiogram based on frequency domain cepstrum coefficient characteristics;
the electrocardio identity recognition method based on the frequency domain cepstrum coefficient characteristics comprises the following steps:
acquiring an electrocardiosignal to be identified;
preprocessing the electrocardiosignals to be identified;
extracting the features of the preprocessed electrocardiosignals to be recognized, and extracting the features to be recognized;
and comparing the features to be identified with the features in the electrocardio feature template library, and outputting an identity identification result.
In a second aspect, the present disclosure provides a system for cardiac electrical identity recognition based on frequency domain cepstrum coefficient features;
electrocardio identity recognition system based on frequency domain cepstrum coefficient characteristics includes:
an acquisition module configured to: acquiring an electrocardiosignal to be identified;
a pre-processing module configured to: preprocessing the electrocardiosignals to be identified;
a feature extraction module configured to: extracting the features of the preprocessed electrocardiosignals to be recognized, and extracting the features to be recognized;
an output module configured to: and comparing the features to be identified with the features in the electrocardio feature template library, and outputting an identity identification result.
In a third aspect, the present disclosure also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first aspect.
In a fourth aspect, the present disclosure also provides a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
In a fifth aspect, the present disclosure also provides a computer program (product) comprising a computer program for implementing the method of any one of the preceding first aspects when run on one or more processors.
Compared with the prior art, the beneficial effect of this disclosure is:
according to the method, the electrocardiosignal is analyzed from the angle of the frequency domain, compared with time domain analysis, all components of the signal can be found more intuitively, noise interference is avoided, the extracted cepstrum coefficient is used as the characteristic of identity recognition, the difference between different individuals can be well distinguished, and the method has good recognition performance;
according to the QRS wave band identification method and device, the QRS wave band which is least prone to interference is selected for processing, the whole heartbeat is not needed, the processing capacity of data is greatly reduced, the whole extraction algorithm does not involve complex operation, the time complexity is small, and therefore the high efficiency of the identification process is guaranteed.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a flow chart of a method of the first embodiment;
fig. 2 is a schematic diagram of the distribution of the filter bank for frequency conversion of Mel to linear frequencies according to the first embodiment.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
The embodiment provides an electrocardio identity recognition method based on frequency domain cepstrum coefficient characteristics;
as shown in fig. 1, the method for identifying an electrocardiogram based on frequency domain cepstrum coefficient characteristics includes:
s101: acquiring an electrocardiosignal to be identified;
s102: preprocessing the electrocardiosignals to be identified;
s103: extracting the features of the preprocessed electrocardiosignals to be recognized, and extracting the features to be recognized;
s104: and comparing the features to be identified with the features in the electrocardio feature template library, and outputting an identity identification result.
As one or more embodiments, in S101, obtaining an electrocardiographic signal to be identified; the method comprises the following specific steps: the electrodes are used for collecting or wearable equipment is used for collecting.
It should be understood that, for the specific collection mode, a person skilled in the art may select the collection mode according to the actual situation, and the application is not limited in any way.
As one or more embodiments, in S102, preprocessing an electrocardiographic signal to be recognized; the method comprises the following specific steps:
denoising the electrocardiosignals to be identified;
resampling the denoised electrocardiosignals to be identified;
and carrying out heartbeat cycle segmentation processing on the resampled electrocardiosignals to be identified to obtain one or more set wave bands of the heartbeat cycle.
Furthermore, the denoising processing is performed on the electrocardiosignals to be identified, and myoelectric interference or baseline drift noise is removed by adopting a fourth-order low-pass Butterworth filter with the cut-off frequency of 40 Hz.
Further, the electrocardiosignals to be identified after the denoising processing are resampled, so that the sampling rate reaches 250 Hz. To solve the problem of different signal sampling rates.
Further, the cardiac cycle segmentation processing is performed on the cardiac signal to be identified after the resampling processing to obtain one or more set wave bands of the cardiac cycle, the cardiac cycle is segmented based on the R peak of the cardiac signal, a plurality of R peaks in the cardiac signal are detected, and a QRS wave band is obtained with the second R peak as a reference.
Specifically, the heart beat period is divided based on the R peak of the electrocardiosignal, a plurality of R peaks in the electrocardiosignal are detected, and a QRS wave band is obtained by taking the second R peak as a reference; specifically, the position of an R peak is detected by using a Pan-Tompkins algorithm, and signals of 15 sampling points on the left and right sides are selected based on the R peak under the sampling rate of 250Hz to obtain a QRS wave band in a heartbeat period.
It will be appreciated that the segmentation of the heart cycle based on the R peak of the cardiac signal is due to: the R peak is the position where the waveform changes most obviously in a heartbeat cycle, is not easy to be interfered by noise, and is easy to detect an accurate position.
As one or more embodiments, in S103, performing feature extraction on the preprocessed electrocardiographic signals to be recognized, and extracting features to be recognized; the method comprises the following specific steps:
performing framing processing on the set wave bands of one or more heartbeat periods;
smoothing each frame by using a Hamming window;
performing fast Fourier transform on each frame of signals subjected to windowing to obtain a frequency spectrum of each frame;
processing the frequency spectrum of each frame to obtain a power spectrum of each frame signal;
processing the power spectrum of each frame of signal through a filter bank to obtain logarithmic energy output by each filter bank;
performing DCT (discrete cosine transformation) on the obtained logarithmic energy to obtain a cepstrum coefficient of each frame of signal;
and splicing the cepstrum coefficients of all the frame signals to obtain the feature vector of the set waveband in the current heartbeat period.
It should be understood that the feature extraction is to perform time-frequency conversion on the obtained QRS band, perform frequency domain analysis, perform framing on the QRS band, extract cepstrum coefficients of each frame, and connect the cepstrum coefficients of each frame to obtain a final 72-dimensional feature vector.
It should be understood that the framing processing is performed because the electrocardiographic signals are non-stationary signals, the fourier transform cannot be directly performed, the electrocardiographic signals can be framed, each frame of signals can be regarded as a short-time stationary signal, and the stationary signals can be better subjected to the spectral analysis only by performing the fourier transform.
Illustratively, framing is performed on a set band of one or more heartbeat cycles; the length of each frame signal is 14 sampling points, and a frame stack of 12 sampling points exists between two adjacent frames, so that one QRS wave band can be divided into 9 frames.
It should be understood that the smoothing process using the hamming window for each frame is to smooth the discontinuous changes at the truncation to reduce the spectral leakage.
Illustratively, the smoothing process is performed on each frame by using a hamming window, and the method specifically comprises the following steps:
if one of the frame signals after the framing is S (n), the windowed signal is S' (n) ═ S (n) × w (n), and the expression of the hamming window is as follows:
where N is the length of the signal per frame.
It should be understood that, performing fast fourier transform on each frame of signal after windowing to obtain a frequency spectrum of each frame; processing the frequency spectrum of each frame to obtain a power spectrum of each frame signal; the reason is that:
for discrete signals in a computer, the frequency spectrum of the signals can be acquired very efficiently by using Fast Fourier Transform (FFT), the number of transform points needs to be set before transformation, the number of the transform points needs to be an integer power of 2, the higher the number of the transform points is, the higher the resolution of the frequency spectrum is, and the method is more beneficial to distinguishing different frequency components in the frequency spectrum.
Experiments show that the FFT of 512 points is respectively carried out on each frame signal S' (n) after windowing, so that the frequency spectrum of each frame can be obtained more quickly under the condition of keeping high resolution, and the frequency spectrum of each frame can be obtainedThe power spectrum | X of the signal is obtained by the square of the frequency spectrum modulusa(k)|2。
Further, the specific step of obtaining the logarithmic energy output by each filter bank by processing the power spectrum of each frame signal through the filter bank includes:
the power spectrum | X of each frame signala(k)|2Obtaining the logarithmic energy output by each filter through a filter bank H (k) and taking a logarithm calculation, wherein the calculation formula is as follows:
where M is the number of filters, Hm(k) Is the mth triangular filter. The number of filters is 40, so that 40 logarithmic energies can be obtained per frame of signal.
Further, performing DCT on the obtained logarithmic energy to obtain a cepstrum coefficient of each frame of signal; the method comprises the following specific steps:
performing DCT transformation on the obtained logarithmic energy S (m), wherein the formula is as follows:
obtaining an L-order coefficient, taking L as 8, obtaining an 8-order cepstrum coefficient from each frame of signal, obtaining 9 frames of each QRS wave band in total, and obtaining a 72-dimensional feature vector after splicing.
A Mel filter bank h (k) is constructed consisting of 40 triangular filters, each with a response of 1 at the centre frequency, evenly distributed over the Mel frequency.
The linear frequency to Mel frequency mapping formula is:
the filter bank distribution for the Mel frequency conversion to linear frequency according to this formula is shown in figure 2.
The specific calculation method of the filter bank h (k) is as follows:
1. determining the lowest linear frequency (generally 0Hz), the highest linear frequency (generally sampling rate) and the number of Mel filters of the electrocardiosignals;
2. calculating Mel frequencies corresponding to the lowest and highest linear frequencies;
3. calculating the distance between the center frequencies of two adjacent Mel filters: (highest Mel frequency-lowest Mel frequency)/(number of filters + 1);
4. converting each central Mel frequency into a linear frequency;
5. the index of the frequency corresponding to the mid-point of the FFT is calculated.
As one or more embodiments, in S104, comparing the features to be recognized with the features in the electrocardiographic feature template library, and outputting an identity recognition result; the method comprises the following specific steps:
and calculating the cosine distance of the included angle between the features to be identified and the features in the electrocardio feature template library, wherein the identity of the person to be tested of the features to be identified is the identity of the person corresponding to the features in the electrocardio feature template library, the cosine distance of the included angle of which is less than a set threshold value.
It should be understood that, for the electrocardiosignals to be tested, 72-dimensional feature vectors are extracted, the cosine distance of the included angle of the feature vectors corresponding to each individual in the template library is calculated, and finally, identity recognition is carried out.
In this embodiment, identity recognition is performed based on the cosine distance of the included angle, the cosine distance of the included angle between the feature vector of the electrocardiographic signal to be tested and the feature vector corresponding to each individual in the feature template library is respectively calculated, and the signal to be tested is classified as the corresponding individual with the smallest cosine distance of the included angle.
Example two
The embodiment provides an electrocardio identity recognition system based on frequency domain cepstrum coefficient characteristics;
electrocardio identity recognition system based on frequency domain cepstrum coefficient characteristics includes:
an acquisition module configured to: acquiring an electrocardiosignal to be identified;
a pre-processing module configured to: preprocessing the electrocardiosignals to be identified;
a feature extraction module configured to: extracting the features of the preprocessed electrocardiosignals to be recognized, and extracting the features to be recognized;
an output module configured to: and comparing the features to be identified with the features in the electrocardio feature template library, and outputting an identity identification result.
It should be noted here that the acquiring module, the preprocessing module, the feature extracting module and the outputting module correspond to steps S101 to S104 in the first embodiment, and the modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In the foregoing embodiments, the descriptions of the embodiments have different emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules is merely a logical functional division, and in actual implementation, there may be other divisions, for example, multiple modules may be combined or integrated into another system, or some features may be omitted, or not executed.
EXAMPLE III
The present embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, a processor is connected with the memory, the one or more computer programs are stored in the memory, and when the electronic device runs, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first embodiment.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Example four
The present embodiments also provide a computer-readable storage medium for storing computer instructions, which when executed by a processor, perform the method of the first embodiment.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. The electrocardio identity recognition method based on the frequency domain cepstrum coefficient characteristics is characterized by comprising the following steps:
acquiring an electrocardiosignal to be identified;
preprocessing the electrocardiosignals to be identified;
extracting the features of the preprocessed electrocardiosignals to be recognized, and extracting the features to be recognized;
and comparing the features to be identified with the features in the electrocardio feature template library, and outputting an identity identification result.
2. The method according to claim 1, characterized by obtaining the electrocardiosignals to be identified; the method comprises the following specific steps: the electrodes are used for collecting or wearable equipment is used for collecting.
3. The method according to claim 1, characterized in that the electrocardiosignals to be identified are preprocessed; the method comprises the following specific steps:
denoising the electrocardiosignals to be identified;
resampling the denoised electrocardiosignals to be identified;
and carrying out heartbeat cycle segmentation processing on the resampled electrocardiosignals to be identified to obtain one or more set wave bands of the heartbeat cycle.
4. The method as claimed in claim 3, wherein the denoising of the electrocardiographic signal to be recognized is performed by using a fourth-order low-pass Butterworth filter with a cut-off frequency of 40Hz to remove electromyographic interference or baseline wander noise.
5. The method as claimed in claim 3, wherein the heart beat cycle segmentation process is performed on the re-sampled electrocardiosignal to be identified to obtain one or more set wave bands of the heart beat cycle, the heart beat cycle is segmented based on the R peak of the electrocardiosignal to detect a plurality of R peaks in the electrocardiosignal, and a QRS wave band is obtained with the second R peak as a reference;
alternatively, the first and second electrodes may be,
dividing the heartbeat cycle based on the R peak of the electrocardiosignal, detecting a plurality of R peaks in the electrocardiosignal, and obtaining a QRS wave band by taking the second R peak as a reference; specifically, the position of an R peak is detected by using a Pan-Tompkins algorithm, and signals of 15 sampling points on the left and right sides are selected based on the R peak under the sampling rate of 250Hz to obtain a QRS wave band in a heartbeat period.
6. The method as claimed in claim 1, wherein the preprocessed electrocardiosignals to be identified are subjected to feature extraction to extract features to be identified; the method comprises the following specific steps:
performing framing processing on the set wave bands of one or more heartbeat periods;
smoothing each frame by using a Hamming window;
performing fast Fourier transform on each frame of signals subjected to windowing to obtain a frequency spectrum of each frame;
processing the frequency spectrum of each frame to obtain a power spectrum of each frame signal;
processing the power spectrum of each frame of signal through a filter bank to obtain logarithmic energy output by each filter bank;
performing DCT (discrete cosine transformation) on the obtained logarithmic energy to obtain a cepstrum coefficient of each frame of signal;
and splicing the cepstrum coefficients of all the frame signals to obtain the feature vector of the set waveband in the current heartbeat period.
7. The method as claimed in claim 1, wherein the features to be identified are compared with the features in the electrocardiogram feature template library to output an identity identification result; the method comprises the following specific steps:
and calculating the cosine distance of the included angle between the features to be identified and the features in the electrocardio feature template library, wherein the identity of the person to be tested of the features to be identified is the identity of the person corresponding to the features in the electrocardio feature template library, the cosine distance of the included angle of which is less than a set threshold value.
8. An electrocardio identity recognition system based on frequency domain cepstrum coefficient characteristics is characterized by comprising:
an acquisition module configured to: acquiring an electrocardiosignal to be identified;
a pre-processing module configured to: preprocessing the electrocardiosignals to be identified;
a feature extraction module configured to: extracting the features of the preprocessed electrocardiosignals to be recognized, and extracting the features to be recognized;
an output module configured to: and comparing the features to be identified with the features in the electrocardio feature template library, and outputting an identity identification result.
9. An electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs being stored in the memory, the processor executing the one or more computer programs stored in the memory when the electronic device is running, to cause the electronic device to perform the method of any of the preceding claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010365770.1A CN111582138A (en) | 2020-04-30 | 2020-04-30 | Electrocardio identity recognition method and system based on frequency domain cepstrum coefficient characteristics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010365770.1A CN111582138A (en) | 2020-04-30 | 2020-04-30 | Electrocardio identity recognition method and system based on frequency domain cepstrum coefficient characteristics |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111582138A true CN111582138A (en) | 2020-08-25 |
Family
ID=72112015
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010365770.1A Pending CN111582138A (en) | 2020-04-30 | 2020-04-30 | Electrocardio identity recognition method and system based on frequency domain cepstrum coefficient characteristics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111582138A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112401902A (en) * | 2020-12-04 | 2021-02-26 | 山东大学 | Electrocardio identity recognition method and system based on neural network time-frequency analysis combination |
| CN112472105A (en) * | 2020-12-21 | 2021-03-12 | 山东大学 | Electrocardio identity recognition method and system based on bounded discriminant analysis of kernel |
| CN112818315A (en) * | 2021-02-26 | 2021-05-18 | 山东大学 | Electrocardiosignal identity recognition method and system fusing multi-feature information |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104102915A (en) * | 2014-07-01 | 2014-10-15 | 清华大学深圳研究生院 | Multiple-template matching identity recognition method based on ECG (Electrocardiogram) under electrocardiogram abnormality state |
| 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 |
| CN109330585A (en) * | 2018-11-16 | 2019-02-15 | 山东大学 | A kind of electrocardiosignal identification, evaluation method and device based on NMF |
| CN109431517A (en) * | 2018-11-13 | 2019-03-08 | 四川长虹电器股份有限公司 | A kind of personal identification method based on heart sound |
| CN109620213A (en) * | 2019-02-25 | 2019-04-16 | 山东大学 | A kind of electrocardiogram recognition method and device based on multiple dimensioned Differential Characteristics |
| CN109961017A (en) * | 2019-02-26 | 2019-07-02 | 杭州电子科技大学 | A kind of cardiechema signals classification method based on convolution loop neural network |
| CN110192864A (en) * | 2019-06-12 | 2019-09-03 | 北京交通大学 | A kind of cross-domain electrocardiogram biometric identity recognition methods |
| CN110786850A (en) * | 2019-11-11 | 2020-02-14 | 菏泽学院 | Electrocardiosignal identity recognition method and system based on multi-feature sparse representation |
-
2020
- 2020-04-30 CN CN202010365770.1A patent/CN111582138A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104102915A (en) * | 2014-07-01 | 2014-10-15 | 清华大学深圳研究生院 | Multiple-template matching identity recognition method based on ECG (Electrocardiogram) under electrocardiogram abnormality state |
| 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 |
| CN109431517A (en) * | 2018-11-13 | 2019-03-08 | 四川长虹电器股份有限公司 | A kind of personal identification method based on heart sound |
| CN109330585A (en) * | 2018-11-16 | 2019-02-15 | 山东大学 | A kind of electrocardiosignal identification, evaluation method and device based on NMF |
| CN109620213A (en) * | 2019-02-25 | 2019-04-16 | 山东大学 | A kind of electrocardiogram recognition method and device based on multiple dimensioned Differential Characteristics |
| CN109961017A (en) * | 2019-02-26 | 2019-07-02 | 杭州电子科技大学 | A kind of cardiechema signals classification method based on convolution loop neural network |
| CN110192864A (en) * | 2019-06-12 | 2019-09-03 | 北京交通大学 | A kind of cross-domain electrocardiogram biometric identity recognition methods |
| CN110786850A (en) * | 2019-11-11 | 2020-02-14 | 菏泽学院 | Electrocardiosignal identity recognition method and system based on multi-feature sparse representation |
Non-Patent Citations (1)
| Title |
|---|
| 梁盛德 等: "基于卷积神经网络和心电QRS波群的身份识别", 《电子测量与仪器学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112401902A (en) * | 2020-12-04 | 2021-02-26 | 山东大学 | Electrocardio identity recognition method and system based on neural network time-frequency analysis combination |
| CN112401902B (en) * | 2020-12-04 | 2022-02-08 | 山东大学 | Electrocardio identity recognition method and system based on neural network time-frequency analysis combination |
| CN112472105A (en) * | 2020-12-21 | 2021-03-12 | 山东大学 | Electrocardio identity recognition method and system based on bounded discriminant analysis of kernel |
| CN112472105B (en) * | 2020-12-21 | 2021-08-17 | 山东大学 | Electrocardio identity recognition method and system based on bounded discriminant analysis of kernel |
| CN112818315A (en) * | 2021-02-26 | 2021-05-18 | 山东大学 | Electrocardiosignal identity recognition method and system fusing multi-feature information |
| CN112818315B (en) * | 2021-02-26 | 2022-03-15 | 山东大学 | Electrocardiosignal identity recognition method and system fusing multi-feature information |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CA2976568C (en) | Methods and systems for disease analysis based on tranformations of diagnostic signals | |
| US20220395212A1 (en) | Methods and systems for tracking physiological parameters of mother and fetus during pregnancy | |
| CN109171712B (en) | Atrial fibrillation identification method, atrial fibrillation identification device, atrial fibrillation identification equipment and computer readable storage medium | |
| Abibullaev et al. | A new QRS detection method using wavelets and artificial neural networks | |
| CN109907752B (en) | Electrocardiogram diagnosis and monitoring system for removing motion artifact interference and electrocardio characteristic detection | |
| CN111582138A (en) | Electrocardio identity recognition method and system based on frequency domain cepstrum coefficient characteristics | |
| CN103110417B (en) | Automatic electrocardiogram recognition system | |
| Kao et al. | Automatic phonocardiograph signal analysis for detecting heart valve disorders | |
| CN112401902B (en) | Electrocardio identity recognition method and system based on neural network time-frequency analysis combination | |
| CN109948396B (en) | Heart beat classification method, heart beat classification device and electronic equipment | |
| Zou et al. | An ultra-low power QRS complex detection algorithm based on down-sampling wavelet transform | |
| CN105147252A (en) | Heart disease recognition and assessment method | |
| CN115120248A (en) | Histogram-based adaptive threshold R peak detection and heart rhythm classification method and device | |
| Uchaipichat et al. | Development of QRS detection using short-time fourier transform based technique | |
| US11660062B2 (en) | Method and system for recognizing crackles | |
| Illanes-Manriquez | An automatic multi-lead electrocardiogram segmentation algorithm based on abrupt change detection | |
| CN115204297A (en) | Electrocardiogram classification method and system based on multi-channel convolutional neural network | |
| WO2018160890A1 (en) | Efficient fetal-maternal ecg signal separation from two maternal abdominal leads via diffusion-based channel selection | |
| CN114010220A (en) | Heart sound signal processing method, computer device and storage medium | |
| Khandait et al. | ECG signal processing using classifier to analyses cardiovascular disease | |
| Mukhopadhyay et al. | Robust Identification of the QRS-Complexes in Electrocardiogram Signals Using Ramanujan Filter Bank-Based Periodicity Estimation Technique | |
| Yusuf et al. | Feature Extraction of ECG Signals using Discrete Wavelet Transform and MFCC | |
| Mohanty et al. | R-peak detection using efficient technique for tachycardia detection | |
| CN115177267B (en) | Heart beat artifact identification method and system | |
| Patil et al. | Cardiac Arrhythmia Detection through ECG Signals |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200825 |
|
| RJ01 | Rejection of invention patent application after publication |