CN112587148B - Template generation method and device comprising fuzzification similarity measurement method - Google Patents
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Abstract
The template selection method and device comprise a fuzzification similarity measurement method, wherein ventricular premature beat vectors are constructed into fuzzy symbolic type vectors through data preprocessing and index vectors, the similar fuzzy symbolic type vectors are classified according to Euclidean distances, a plurality of templates containing all known electrocardio types are respectively formed, and the templates can be used for judging whether electrocardiosignals are ventricular premature beat, can be accurately selected, and can cover various shapes of ventricular premature beat.
Description
Technical Field
The application belongs to the technical field of electrocardiogram processing, and particularly relates to a template selection method and device comprising a fuzzification similarity measurement method.
Background
All ectopic heartbeats are identified from the 24-hour dynamic electrocardiogram, and a large amount of manpower and material resources are consumed; if the electrocardiogram doctor carries out manual analysis, the increase of detectors brings heavy pressure to the doctor; and doctors may ignore or misjudge certain electrocardiographic features due to fatigue, computer-assisted analysis of electrocardiographic signals becomes particularly important. Ventricular premature beats are one of the common types of ectopic heartbeats. Aiming at the automatic diagnosis of ventricular premature beat, many achievements with guiding significance have appeared at home and abroad, but the activation points of ventricular premature beat can come from different parts, so that the waveform of ventricular premature beat is complex. Finding templates of all waveform types is therefore particularly important for ventricular premature beat selection, and the templates directly result in accuracy of ventricular premature beat identification.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in order to solve the defects in the prior art, a template generation method and a template generation device comprising the fuzzification similarity measurement method are provided.
The technical scheme adopted by the invention for solving the technical problem is as follows:
a template generation method comprising a fuzzification similarity measurement method comprises the following steps:
s1: collecting data, namely collecting clinical resting electrocardiosignal data containing ventricular premature beat, wherein the occurrence position of the ventricular premature beat is known;
s2: data preprocessing, namely intercepting ventricular premature beat vectors from electrocardiosignals to obtain ventricular premature beat vectors, wherein the length of the vectors is equal to the number M of corresponding sampling points in preset time, and the R wave peak position is located at the position 2/3 of the ventricular premature beat vectors from front to back;
s3: constructing an index vector: randomly selecting a plurality of integer values from 1 to M to form an index vector indx (i);
s4: fuzzification treatment: screening all ventricular premature beat vectors by using the numerical value of the index vector indx (i) to obtain screening vectors p (j) with the same quantity as the ventricular premature beat vectors, wherein each quantity in the screening vectors p (j) has the voltage value of the electrocardiosignal of a sampling point corresponding to the integer value represented by i in the index vector indx (i), and comparing the magnitudes of p (j) and p (j + 1) from j =1 respectively, wherein j is an odd number between 1 and i, and when the p (j) is large, the magnitude is marked as 1; when p (j + 1) is large; is marked as-1; if p (j) = p (j + 1), recording as 0, and arranging the comparison results in sequence to form a fuzzy symbol type vector;
s5: similarity comparison, namely calculating the Euclidean distance between any two fuzzy symbolic type vectors aiming at the fuzzy symbolic type vectors obtained by all ventricular premature beat vectors;
s6: template generation, selecting fuzzy symbolic type vectors to generate and obtain a plurality of templates, wherein all the templates comprise all the fuzzy symbolic type vectors, the first template is a first fuzzy symbolic type vector, and the obtaining conditions of the first fuzzy symbolic type vector are as follows: the fuzzy symbolic type vectors with Euclidean distance smaller than a preset value with the first fuzzy symbolic type vector account for the most of all fuzzy symbolic type vectors, and the account ratio is larger than 5%; the Nth template is an Nth fuzzy symbolic type vector, and the obtaining condition of the Nth fuzzy symbolic type vector is as follows: after fuzzy symbolic vectors with Euclidean distances to the former (N-1) th template smaller than a preset value are eliminated, fuzzy symbolic vectors with Euclidean distances to the Nth fuzzy symbolic vector smaller than the preset value have the most proportion in the residual fuzzy symbolic vectors, and the proportion is larger than 5%; n is a natural number more than or equal to 2.
Preferably, in the template generating method including the fuzzification similarity measuring method of the present invention, the preprocessing employs a fir filter with upper and lower cutoff frequencies of 0.1hz and 100hz for filtering, and if the sampling frequency of the electrocardiographic signal is not 500Hz, the signal is resampled to 500Hz by using a nearest neighbor interpolation method.
Preferably, the template generation method comprising the fuzzification similarity measurement method of the present invention randomly selects an integer value from 1 to M that constitutes the index vector indx (i) by 10% M-15% M, i being 10% M-15% M.
Preferably, in the template generating method including the fuzzification similarity measuring method, the preset value is 5.
Preferably, the template generation method including the fuzzification similarity measurement method of the invention includes that the length of the clinical resting electrocardiosignal data including ventricular premature beat is at least 10s, and the preset time is 0.9s.
The invention also provides a template generation device comprising the fuzzification similarity measurement method, which comprises the following steps:
a data collection module: the ventricular premature beat collecting device is used for collecting clinical rest electrocardiosignal data containing ventricular premature beat, and the occurrence position of the ventricular premature beat is known;
a data preprocessing module: the ventricular premature beat vector is obtained by intercepting the electrocardiosignals, the length of the vector is equal to the number M of corresponding sampling points in preset time, and the position of the R wave peak is positioned at the position 2/3 of the ventricular premature beat vector from front to back;
and an index vector constructing module: randomly selecting a plurality of integer values from 1 to M to form an index vector indx (i);
the fuzzification processing module: the ventricular premature beat vector screening method comprises the steps that all ventricular premature beat vectors are screened respectively according to the numerical value of an index vector indx (i) to obtain screening vectors p with the same quantity as the ventricular premature beat vectors, each quantity in the screening vectors p has the voltage value of an electrocardiosignal of a sampling point corresponding to an integer value represented by i in the index vector indx (i), the voltage values of the electrocardiosignal of the sampling points are compared with the voltage value of p from j =1, wherein j is an odd number between 1 and i, and when the p is large, the p is marked as 1; when p (j + 1) is large; is marked as-1; if p (j) = p (j + 1), recording as 0, and arranging the comparison results in sequence to form a fuzzy symbol type vector;
a similarity comparison module: the Euclidean distance calculating device is used for calculating Euclidean distances of any two fuzzy symbolic type vectors aiming at the fuzzy symbolic type vectors obtained by all ventricular premature beat vectors;
a template generation module: the method is used for selecting fuzzy symbolic type vectors, generating and obtaining a plurality of templates, wherein all the templates comprise all the fuzzy symbolic type vectors, the first template is a first fuzzy symbolic type vector, and the obtaining conditions of the first fuzzy symbolic type vector are as follows: the fuzzy symbolic type vector with the Euclidean distance from the first fuzzy symbolic type vector smaller than a preset value accounts for the most of all fuzzy symbolic type vectors, and the occupation ratio is larger than 5%; the Nth template is an Nth fuzzy symbolic type vector, and the obtaining condition of the Nth fuzzy symbolic type vector is as follows: after fuzzy symbolic vectors with Euclidean distances to the former (N-1) th template smaller than a preset value are eliminated, fuzzy symbolic vectors with Euclidean distances to the Nth fuzzy symbolic vector smaller than the preset value have the most proportion in the residual fuzzy symbolic vectors, and the proportion is larger than 5%; n is a natural number more than or equal to 2.
Preferably, in the template generating apparatus including the blurring similarity measurement method according to the present invention, the preprocessing is performed by filtering with a fir filter having an upper and lower cutoff frequency of 0.1Hz and 100hz, and if the sampling frequency of the ecg signal is not 500Hz, the signal is resampled to 500Hz by using the nearest neighbor interpolation method.
Preferably, the template generation apparatus comprising the fuzzification similarity measurement method of the present invention randomly selects an integer value composition index vector indx (i) of 10-M-15-M, i being 10-M-15-M, from 1 to M.
Preferably, the preset value of the template generation device including the fuzzification similarity measurement method is 5.
Preferably, the template generating device including the fuzzification similarity measuring method of the invention comprises that the length of the clinical resting electrocardiosignal data of ventricular premature beat is at least 10s, and the preset time is 0.9s.
The invention has the beneficial effects that:
according to the template selection method and device comprising the fuzzification similarity measurement method, ventricular premature beat vectors are constructed into fuzzy symbolic type vectors through data preprocessing and index vectors, the similar fuzzy symbolic type vectors are classified according to Euclidean distances, a plurality of templates comprising all known electrocardio types are respectively formed, the templates can be used for judging whether electrocardiosignals are ventricular premature beats or not, the template selection method and device have the advantages of being accurate in selection and capable of covering various ventricular premature beat shapes.
Drawings
The technical solution of the present application is further explained below with reference to the drawings and the embodiments.
FIG. 1 is a flow chart of a template generation method including a fuzzified similarity measurement method according to an embodiment of the present application;
Detailed Description
It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict.
The technical solutions of the present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Example 1
The embodiment provides a template generation method including a fuzzification similarity measurement method, as shown in fig. 1, including the following steps:
s1: collecting data, namely collecting clinical resting 10s electrocardiosignal data containing ventricular premature beat, wherein the occurrence position of the ventricular premature beat is known;
s2: data preprocessing, namely intercepting ventricular premature beat vectors (consisting of voltage values of all sampling points) from the electrocardiosignals to obtain the ventricular premature beat vectors, wherein the length of the vectors is equal to the number M of the corresponding sampling points within 0.9s, and the R peak position is positioned at the 2/3 position from front to back in the ventricular premature beat vectors;
the preprocessing can also adopt a fir filter with upper and lower cut-off frequencies of 0.1Hz and 100Hz for filtering, if the sampling frequency of the electrocardiosignal is not 500Hz, the signal is also required to be resampled to be 500Hz by adopting a nearest neighbor interpolation method, and because 500 sampling points are arranged in 1s, the number of the corresponding sampling points in 0.9s is 450, the truncation is carried out according to 150 points from R wave front 299 points to R wave back, and the R wave peak position is all at 300 points. And then, carrying out mean value filtering and normalization on each electrocardiosignal.
S3: constructing an index vector: randomly selecting from 1 to M an integer value of 10-15% by weight M constituting an index vector indx (i), i being 10-15% by weight M;
s4: fuzzification treatment:
screening all ventricular premature beat vectors by using the numerical value of the index vector indx (i) to obtain screening vectors p with the same quantity as the ventricular premature beat vectors, wherein each screening vector p has the voltage value of the electrocardiosignal of a sampling point corresponding to the integer value represented by i in the index vector indx (i), such as indx (i) =1,3,7,9 … …, then the screening vector p is the voltage value/mV of the electrocardiosignal corresponding to the 1 st, 3 rd, 7 th and 9 th … … sampling points, starting from j =1, and comparing the sizes of p and p respectively, wherein j is an odd number between 1 and i, and when the p is large, the p is marked as 1; when p (j + 1) is large; is marked as-1; if p (j) = p (j + 1), recording as 0, and arranging the comparison results in sequence to form a fuzzy symbol type vector;
s5: similarity comparison, namely calculating the Euclidean distance between any two fuzzy symbolic type vectors aiming at the fuzzy symbolic type vectors obtained by all ventricular premature beat vectors; the Euclidean distance represents the similarity between corresponding ventricular premature beats, and the closer the distance is, the closer the shape is;
s6: template generation, selecting fuzzy symbolic type vector to generate and obtain several templates, in which all templates contain all fuzzy symbolic type vectors
The first template is a first fuzzy symbolic type vector, and the obtaining condition of the first fuzzy symbolic type vector is as follows: the fuzzy symbolic type vector with Euclidean distance less than 5 to the first fuzzy symbolic type vector accounts for the most of all fuzzy symbolic type vectors, and the proportion is more than 5%;
the Nth template is an Nth fuzzy symbolic type vector, and the obtaining condition of the Nth fuzzy symbolic type vector is as follows: after fuzzy symbolic vectors with Euclidean distance less than 5 with the former (N-1) th template are eliminated, fuzzy symbolic vectors with Euclidean distance less than 5 with the Nth fuzzy symbolic vector have the most proportion in the residual fuzzy symbolic vectors, and the proportion is more than 5%; n is a natural number not less than 2.
Several templates for identifying ventricular premature beats are generated by the above method.
The use method of the template comprises the following steps:
constructing a fuzzy symbolic vector for a new electrocardiosignal which is unknown to be ventricular premature beat or not according to the method of the steps S2-S4, sequentially carrying out Euclidean distance calculation on the fuzzy symbolic vector and a first template and an N template, wherein the Euclidean distance between the fuzzy symbolic vector and any template is greater than 5, the Euclidean distance is considered to be ventricular premature beat, and the Euclidean distance between the fuzzy symbolic vector and all templates is greater than 5; the new heartbeat is deemed not to belong to the ventricular premature beat.
Example 2
The present embodiment provides a template generating apparatus including a fuzzification similarity measuring method, including:
a data collection module: the ventricular premature beat collecting device is used for collecting clinical rest 10s electrocardiosignal data containing ventricular premature beat, and the occurrence position of the ventricular premature beat is known;
a data preprocessing module: the device is used for intercepting ventricular premature beat vectors from electrocardiosignals, the length of the vectors is equal to the number M of corresponding sampling points within 0.9s, and the position of an R wave peak is located at the position 2/3 of the ventricular premature beat vectors from front to back;
the preprocessing can also adopt a fir filter with upper and lower cut-off frequencies of 0.1Hz and 100Hz for filtering, if the sampling frequency of the electrocardiosignal is not 500Hz, the signal is also required to be resampled to be 500Hz by adopting a nearest neighbor interpolation method, and because 500 sampling points are arranged in 1s, the number of the corresponding sampling points in 0.9s is 450, the truncation is carried out according to 150 points from R wave front 299 points to R wave back, and the R wave peak position is all at 300 points. And then, carrying out mean value filtering and normalization on each electrocardiosignal.
And an index vector constructing module: randomly selecting an integer value from 1 to M that constitutes an index vector indx (i) that is 10-15% by weight M;
the fuzzification processing module: the method is used for screening all ventricular premature beat vectors by using the numerical value of an index vector indx (i) to obtain screening vectors p with the same quantity as the ventricular premature beat vectors, each quantity in the screening vectors p has the voltage value of the electrocardiosignal of a sampling point corresponding to an integer value represented by i in the index vector indx (i), for example, indx (i) =1,3,7,9 … …, if the screening vector p is the voltage value/mV of the electrocardiosignal corresponding to the 1 st, the 3 rd, the 7 th and the 9 th … … sampling points, starting from j =1, and comparing the magnitudes of p (j) and p (j + 1) respectively, wherein j is an odd number between 1 and i, and when the p (j) is large, the magnitude is 1; when p (j + 1) is large; is marked as-1; if p (j) = p (j + 1), recording as 0, and arranging the comparison results in sequence to form a fuzzy symbol type vector;
a similarity comparison module: the Euclidean distance calculating device is used for calculating Euclidean distances of any two fuzzy symbolic type vectors aiming at the fuzzy symbolic type vectors obtained by all ventricular premature beat vectors; the Euclidean distance represents the similarity between corresponding ventricular premature beats, and the closer the distance is, the closer the shape is;
a template generation module: generating and obtaining a plurality of templates for selecting the fuzzy symbolic type vectors, wherein all the templates contain all the fuzzy symbolic type vectors, and the fuzzy symbolic type vectors are obtained
The first template is a first fuzzy symbolic type vector, and the obtaining condition of the first fuzzy symbolic type vector is as follows: the fuzzy symbolic type vector with Euclidean distance less than 5 to the first fuzzy symbolic type vector accounts for the most of all fuzzy symbolic type vectors, and the proportion is more than 5%;
the Nth template is an Nth fuzzy symbolic type vector, and the obtaining condition of the Nth fuzzy symbolic type vector is as follows: after fuzzy symbolic vectors with Euclidean distance less than 5 with the former (N-1) th template are eliminated, fuzzy symbolic vectors with Euclidean distance less than 5 with the Nth fuzzy symbolic vector have the most proportion in the residual fuzzy symbolic vectors, and the proportion is more than 5%; n is a natural number more than or equal to 2.
Several templates for identifying ventricular premature beats are generated by the above method.
In light of the foregoing description of the preferred embodiments according to the present application, it is to be understood that various changes and modifications may be made by those skilled in the art without departing from the scope of the invention as defined by the appended claims. The technical scope of the present application is not limited to the content of the specification, and must be determined according to the scope of the claims.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied in the medium.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus, and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Claims (8)
1. A template generation method comprising a fuzzification similarity measurement method is characterized by comprising the following steps:
s1: collecting data, namely collecting clinical resting electrocardiosignal data containing ventricular premature beat, wherein the occurrence position of the ventricular premature beat is known;
s2: data preprocessing, namely intercepting ventricular premature beat vectors from electrocardiosignals to obtain ventricular premature beat vectors, wherein the length of the vectors corresponds to the number M of sampling points in preset time, and the R wave peak position is located at the 2/3 position from front to back in the ventricular premature beat vectors;
s3: constructing an index vector: randomly selecting from 1 to M an integer value constituting the index vector indx (i) by 10% M-15% by weight, i being 10% by weight M-15% by weight M;
s4: fuzzification treatment: screening all ventricular premature beat vectors by using the numerical value of the index vector indx (i) to obtain screening vectors p (j) with the same quantity as the ventricular premature beat vectors, wherein each quantity in the screening vectors p (j) sequentially corresponds to the voltage value of the electrocardiosignal of a sampling point corresponding to the integer value represented by i in the index vector indx (i), and comparing the magnitudes of p (j) and p (j + 1) from j =1, wherein j is an odd number between 1 and i, and when the p (j) is large, the comparison result is recorded as 1; when p (j + 1) is large; recording the comparison result as-1; if p (j) = p (j + 1), recording the comparison result as 0, and arranging the comparison results in sequence to form a fuzzy symbol type vector;
s5: similarity comparison, namely calculating the Euclidean distance between any two fuzzy symbolic type vectors aiming at the fuzzy symbolic type vectors obtained by all ventricular premature beat vectors;
s6: template generation, selecting fuzzy symbolic type vectors to generate a plurality of templates, wherein all the templates comprise all the fuzzy symbolic type vectors, the first template is a first fuzzy symbolic type vector, and the obtaining conditions of the first fuzzy symbolic type vector are as follows: the fuzzy symbolic type vector with the Euclidean distance from the first fuzzy symbolic type vector smaller than a preset value accounts for the most of all fuzzy symbolic type vectors, and the occupation ratio is larger than 5%; the Nth template is an Nth fuzzy symbolic type vector, and the obtaining condition of the Nth fuzzy symbolic type vector is as follows: after fuzzy symbolic vectors with Euclidean distances to the former (N-1) th template smaller than a preset value are eliminated, fuzzy symbolic vectors with Euclidean distances to the Nth fuzzy symbolic vector smaller than the preset value have the most proportion in the residual fuzzy symbolic vectors, and the proportion is larger than 5%; n is a natural number more than or equal to 2.
2. The template generating method comprising the blurring similarity measuring method according to claim 1, wherein the preprocessing is performed by filtering with a fir filter having upper and lower cutoff frequencies of 0.1Hz,100hz, and if the sampling frequency of the electrocardiographic signal is not 500Hz, the signal is resampled to 500Hz by the nearest neighbor interpolation method.
3. The template generation method including the fuzzification similarity measurement method according to claim 1 or 2, wherein the preset value is 5.
4. The template generation method comprising the fuzzification similarity measurement method according to claim 1 or 2, wherein the length of the clinical resting electrocardiosignal data comprising the ventricular premature beat is at least 10s, and the preset time is 0.9s.
5. A template generation apparatus including a fuzzification similarity measurement method, comprising:
a data collection module: the ventricular premature beat collecting device is used for collecting clinical rest electrocardiosignal data containing ventricular premature beat, and the occurrence position of the ventricular premature beat is known;
a data preprocessing module: the ventricular premature beat vector is obtained by intercepting the electrocardiosignals, the length of the vector corresponds to the number M of sampling points in preset time, and the position of the R wave peak is positioned at the 2/3 position from front to back in the ventricular premature beat vector;
and an index vector constructing module: randomly selecting an integer value from 1 to M that constitutes an index vector indx (i) that is 10% by M-15% by M, i being 10% by M-15% by M;
the fuzzification processing module: the ventricular premature beat vector screening method comprises the steps that screening vectors p (j) with the same quantity as ventricular premature beat vectors are obtained by screening all ventricular premature beat vectors according to the numerical values of index vectors indx (i), each quantity in the screening vectors p (j) has the voltage value of an electrocardiosignal of a sampling point corresponding to an integer value represented by i in the index vector indx (i), the sizes of p (j) and p (j + 1) are compared from j =1, wherein j is an odd number between 1 and i, and when the p (j) is large, the comparison result is recorded as 1; when p (j + 1) is large; recording the comparison result as-1; if p (j) = p (j + 1), the comparison result is 0, and the comparison results are sequentially arranged to form a fuzzy symbol type vector;
a similarity comparison module: the Euclidean distance calculating device is used for calculating Euclidean distances of any two fuzzy symbolic type vectors aiming at the fuzzy symbolic type vectors obtained by all ventricular premature beat vectors;
a template generation module: the method is used for selecting fuzzy symbolic type vectors to generate a plurality of templates, wherein all templates contain all fuzzy symbolic type vectors, the first template is a first fuzzy symbolic type vector, and the obtaining conditions of the first fuzzy symbolic type vector are as follows: the fuzzy symbolic type vector with the Euclidean distance from the first fuzzy symbolic type vector smaller than a preset value accounts for the most of all fuzzy symbolic type vectors, and the occupation ratio is larger than 5%; the Nth template is an Nth fuzzy symbolic type vector, and the obtaining condition of the Nth fuzzy symbolic type vector is as follows: after eliminating the fuzzy symbolic type vectors with the Euclidean distance from the previous N-1 th template being smaller than a preset value, the fuzzy symbolic type vectors with the Euclidean distance from the Nth fuzzy symbolic type vector being smaller than the preset value have the most proportion in the residual fuzzy symbolic type vectors, and the proportion is larger than 5%; n is a natural number not less than 2.
6. The template generating apparatus including the blurring similarity measuring method according to claim 5, wherein the preprocessing is performed by filtering with a fir filter having upper and lower cutoff frequencies of 0.1Hz,100hz, and if the sampling frequency of the electrocardiographic signal is not 500Hz, the signal is resampled to 500Hz by using a nearest neighbor interpolation method.
7. The template generating apparatus including the blurring similarity measuring method according to claim 5 or 6, wherein the preset value is 5.
8. The template generating apparatus comprising the fuzzification similarity measurement method according to claim 5 or 6, wherein the length of the clinical resting electrocardiosignal data comprising the ventricular premature beat is at least 10s, and the preset time is 0.9s.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103714281A (en) * | 2013-12-12 | 2014-04-09 | 深圳先进技术研究院 | Identity recognition method based on electrocardiosignals |
| CN108720831A (en) * | 2018-05-12 | 2018-11-02 | 鲁东大学 | A kind of automatic arrhythmia analysis method based on lead deep neural network |
| CN109745034A (en) * | 2019-01-04 | 2019-05-14 | 东南大学 | Atrial fibrillation real time scan method and device based on range normalization fuzzy mearue entropy |
| WO2019100562A1 (en) * | 2017-11-27 | 2019-05-31 | 乐普（北京）医疗器械股份有限公司 | Automatic recognition and classification method for electrocardiogram heartbeat based on artificial intelligence |
| CN109875546A (en) * | 2019-01-24 | 2019-06-14 | 西安交通大学 | A kind of depth model classification results method for visualizing towards ECG data |
| EP3719679A1 (en) * | 2019-04-03 | 2020-10-07 | Fondation de L'institut de Recherche Idiap | A method for protecting biometric templates, and a system and method for verifying a speaker´s identity |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| IL147502A0 (en) * | 2002-01-07 | 2002-08-14 | Widemed Ltd | Self-adaptive system, for the analysis of biomedical signals of a patient |
| US20040024755A1 (en) * | 2002-08-05 | 2004-02-05 | Rickard John Terrell | System and method for indexing non-textual data |
| US7191164B2 (en) * | 2003-08-19 | 2007-03-13 | Intel Corporation | Searching for object images with reduced computation |
| WO2007098275A2 (en) * | 2006-02-27 | 2007-08-30 | Cardiosoft L.L.P. | Multi-channel system for beat to beat qt interval variability |
| EP1995685A3 (en) * | 2007-05-21 | 2012-08-01 | Biotronik CRM Patent AG | Medical device for monitoring biological signal |
| CN101785670A (en) * | 2009-01-22 | 2010-07-28 | 陈跃军 | Intelligent blurry electrocardiogram on-line analyzer system |
| CN102160787B (en) * | 2011-05-31 | 2013-06-26 | 广东工业大学 | Time-frequency-transformation-based blind extraction method of fetal electrocardiography |
| US9918649B2 (en) * | 2013-05-14 | 2018-03-20 | Boston Scientific Scimed Inc. | Representation and identification of activity patterns during electro-physiology mapping using vector fields |
| CN104537243B (en) * | 2014-12-30 | 2018-03-27 | 哈尔滨理工大学 | A kind of electrocardio sorting technique based on fuzzy reasoning combination weighted similarity measures for non-treatment purpose |
| US10188867B2 (en) * | 2015-01-23 | 2019-01-29 | Medtronic, Inc. | Method and apparatus for beat acquisition during template generation in a medical device having dual sensing vectors |
-
2020
- 2020-12-01 CN CN202011386131.XA patent/CN112587148B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103714281A (en) * | 2013-12-12 | 2014-04-09 | 深圳先进技术研究院 | Identity recognition method based on electrocardiosignals |
| WO2019100562A1 (en) * | 2017-11-27 | 2019-05-31 | 乐普（北京）医疗器械股份有限公司 | Automatic recognition and classification method for electrocardiogram heartbeat based on artificial intelligence |
| CN108720831A (en) * | 2018-05-12 | 2018-11-02 | 鲁东大学 | A kind of automatic arrhythmia analysis method based on lead deep neural network |
| CN109745034A (en) * | 2019-01-04 | 2019-05-14 | 东南大学 | Atrial fibrillation real time scan method and device based on range normalization fuzzy mearue entropy |
| CN109875546A (en) * | 2019-01-24 | 2019-06-14 | 西安交通大学 | A kind of depth model classification results method for visualizing towards ECG data |
| EP3719679A1 (en) * | 2019-04-03 | 2020-10-07 | Fondation de L'institut de Recherche Idiap | A method for protecting biometric templates, and a system and method for verifying a speaker´s identity |
Non-Patent Citations (4)
| Title |
|---|
| 室性早搏的多模板匹配自适应识别算法;张集墨等;《计算机工程与设计》;20111231(第08期);第2885-2888页 * |
| 谢胜龙等.基于一维卷积神经网络的房颤智能诊断方法研究.《计量学报》.2020, * |
| 邓伟辉.时间序列的多粒度智能分析方法研究.《中国优秀硕士学位论文全文数据库（信息科技辑）》.2018, * |
| 郑晓娜等.基于心电信号的身份识别方法研究进展.《北京生物医学工程》.2011,(第02期), * |
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