CN110974191A - Radial artery waveform data identification algorithm - Google Patents
Radial artery waveform data identification algorithm Download PDFInfo
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- CN110974191A CN110974191A CN201911311723.2A CN201911311723A CN110974191A CN 110974191 A CN110974191 A CN 110974191A CN 201911311723 A CN201911311723 A CN 201911311723A CN 110974191 A CN110974191 A CN 110974191A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7246—Details of waveform analysis using correlation, e.g. template matching or determination of similarity
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Abstract
The invention discloses a radial artery waveform data recognition algorithm, belonging to the technical field of data comparison, and comprising the following specific steps: s1: data acquisition, S2: data comparison, S3: data screening, S4: data show that in the pulse feeling process, the pulse amplitude and the pulse frequency of the pulse are compared through the pulse condition acquisition instrument to carry out pulse condition oscillogram comparison, so that a similar comparison sample is obtained, the preliminary understanding of the patient on the self disease condition can be improved, the reference to a doctor can be improved after the pulse condition comparison, the problem that the pulse feeling and the disease condition comparison need to consume a large amount of energy from the beginning is avoided, the labor degree is reduced, the pulse feeling acquisition instrument is mainly used for reference, the guidance to the patient is convenient, the plain worry caused by over anxiety is avoided, the function of pre-reference is also played for the doctor, the judgment range is reduced, the judgment speed is improved, the consumed energy is reduced, and the common disease condition is convenient to judge.
Description
Technical Field
The invention relates to the technical field of data comparison, in particular to a radial artery waveform data identification algorithm.
Background
The pulse condition refers to the condition of rapid, slow, strong, weak and deep pulse. The pulse condition is a term of traditional Chinese medicine, referring to the image and dynamics of pulse, and is one of the basis of syndrome differentiation in traditional Chinese medicine. The pulse condition elements refer to the basic components of the pulse condition, including the position, number, shape and potential. It refers to the image and dynamics of pulse, which is one of the bases of syndrome differentiation in TCM and is generally classified into four categories, floating, sinking, slow and rapid. With the development of science and technology, the attention of people on self health and the improvement of health care concept, medical automation equipment needs to be more stable and faster, meets the increasing self-management requirements of people on self health more accurately, effectively improves the diagnosis efficiency and improves the medical and patient relations, but most of the traditional Chinese medical inquiry methods carry out manual pulse counting on people through professional medical personnel, so that the medical personnel are relatively in shortage and busy sometimes, and in addition, the medical personnel diagnose for a long time and are easy to fatigue, so that an identification algorithm capable of improving automatic diagnosis is needed, the diagnosis working strength of doctors is reduced, and the diagnosis on patients is convenient to improve.
Disclosure of Invention
The invention aims to provide a radial artery waveform data identification algorithm to solve the problems that the diagnosis speed and the diagnosis accuracy are reduced after a doctor is fatigued along with the time of the existing manual pulse taking method for the pulse taking, which is proposed in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a radial artery waveform data recognition algorithm specifically comprises the following steps:
s1: data acquisition: a large number of sickbed samples or normal samples are collected through a pulse collector to obtain a pulse fluctuation diagram, the samples are numbered in sequence, manual pulse cutting and pulse marking are adopted, corresponding pulse conditions are marked on the numbers to form a contrast sample, and then a plurality of valley points, peak points and random contrast points are arranged on the waveform diagram and used for comparing with the pulse fluctuation diagram obtained by new measurement of a patient in use, and a large number of contrast samples are uploaded to a database and used for enlarging the use area.
S2: and (3) data comparison: the method comprises the steps of S1, marking a newly acquired pulse chart by taking a comparison sample comprising a plurality of comparison points as a comparison reference, then carrying out comparison training with the comparison sample, disordering the sequence of the comparison sample after a comparison result reaches a certain qualification rate, realizing speed training of comparison of similarity of the comparison points by multi-point reference setting, finally carrying out manual extraction on the comparison samples similar to a plurality of oscillograms, carrying out simulation training on the precision of the acquired pulse chart in the similar oscillograms, returning the numerical value of the similarity percentage according to the similarity degree, and guiding a user or a doctor.
S3: and (3) screening data: after the data comparison training in the step S2 is completed, the user converts the acquired pulse condition into a waveform diagram by the pulse condition collector, compares the waveform diagram with the comparison sample in the step S1 database, sorts the comparison sample from high to low according to the degree of similarity with the comparison sample, hides the comparison diagram lower than a certain similar value, does not directly display the comparison diagram, and displays the comparison sample label and the corresponding pulse condition according to the comparison sample label called in the step S1, thereby completing the data screening.
S4: and (3) displaying data: with the good contrast result of sequencing in step S3, through will measuring the pulse manifestation picture, contrast sample, similarity show to the pulse manifestation result in the display contrast sample, the user of being convenient for knows the pulse manifestation and carries out reference effect in advance to the doctor, is less than the contrast result of certain numerical value to similar proportion and adopts the mode of hiding and accomodating, is convenient for accomplish the data display and uses.
Preferably, the step S1 further includes post-data padding and repairing, in the long-time use process, according to different pulse oscillograms collected by the client or errors occurring in the comparison result and the actual diagnosis, the client uploads the result, so as to mark the pulse oscillogram to be supplemented, mark the pulse oscillogram with the errors to be verified, and repair the result after the conclusion is made by using methods such as comparison experiments and the like.
Preferably, the plurality of comparison points in step S2 may be multiple or full segments from trough to peak, and the comparison points are used to quickly calculate slope and other corresponding oscillogram values for comparison calculation.
Preferably, the content shown in step S3 further includes contrast similarity, which indicates the possible symptoms of the pulse condition and the corresponding symptoms of the disease condition, for reference.
Preferably, the image displayed in step S4 is a contrast sample with a similarity exceeding 60%.
Compared with the prior art, the invention has the beneficial effects that:
1) according to the pulse feeling and pulse frequency comparison method, in the pulse feeling process, the pulse amplitude and the pulse frequency of the pulse are compared through the pulse condition acquisition instrument to obtain a similar comparison sample, so that the preliminary understanding of a patient on the disease condition of the patient can be improved, the reference to a doctor can be improved after the pulse condition comparison, the problem that a great deal of energy is consumed when the pulse feeling and the disease condition comparison are started from the beginning is solved, and the labor degree is reduced;
2) the invention is mainly used for reference, is convenient for guiding patients, avoids the normal worry caused by over anxiety, plays a role of reference in advance for doctors, reduces the judgment range, improves the judgment speed, reduces the energy consumption and is convenient for judging common symptoms.
Drawings
FIG. 1 is a flow chart of the system of the present invention;
FIG. 2 is a logic diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "top/bottom", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplification of description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "disposed," "sleeved/connected," "connected," and the like are to be construed broadly, e.g., "connected," which may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Referring to fig. 1-2, the present invention provides a technical solution: a radial artery waveform data recognition algorithm is characterized in that: the radial artery waveform data identification algorithm comprises the following specific steps:
s1: data acquisition: collecting a large amount of sickbed samples or normal samples through a pulse collector to obtain a pulse wave diagram, numbering the samples in sequence, marking corresponding pulse conditions on the numbers by adopting artificial pulse cutting and pulse condition marking to form a comparison sample, arranging a plurality of valley points, peak points and random comparison points on the waveform diagram for comparing with the newly measured pulse wave diagram of a patient when in use, uploading a large amount of comparison samples to a database for enlarging the use area, and filling and repairing the later period of data, wherein in the long-time use process, the pulse wave diagram is marked to be supplemented through uploading results by a client according to different pulse wave diagrams collected by the client or errors occur on the comparison results and actual diagnosis, and the pulse wave diagram with errors is marked to be verified, and (5) repairing the result after a conclusion is obtained by adopting methods such as a comparison experiment and the like in time.
S2: and (3) data comparison: marking a newly acquired pulse fluctuation image by taking the comparison sample comprising a plurality of comparison points in the step S1 as a comparison reference, then carrying out comparison training with the comparison sample, disordering the sequence of the comparison sample after the comparison result reaches a certain qualification rate, carrying out speed training of similarity comparison of the comparison points by setting the reference of a plurality of points, wherein the plurality of comparison points can be a plurality of sections or a whole section from a trough to a crest, the comparison points are used for quickly calculating corresponding oscillogram values such as slope and the like for carrying out comparison calculation, finally carrying out manual extraction on the comparison samples similar to the plurality of oscillograms, carrying out simulation training on the accuracy of the acquired pulse pictures in the similar oscillograms, returning the values of the similarity percentages according to the similarity degree, and guiding the user or a doctor.
S3: and (3) screening data: after the data comparison training in the step S2 is completed, the user converts the acquired pulse condition into a waveform diagram by the pulse condition acquisition instrument, compares the waveform diagram with the comparison sample in the step S1 database, sorts the pulse condition from high to low according to the degree of similarity with the comparison sample, hides the comparison diagram lower than a certain similar value, does not directly display the comparison diagram, and displays the comparison sample according to the label marked by the comparison sample called in the step S1 and the corresponding pulse condition, wherein the display content also includes the comparison similarity, and the possible symptoms of the pulse condition and the corresponding symptoms can be reflected for reference use, thereby completing the data screening.
S4: and (3) displaying data: the sequenced comparison results in the step S3 are displayed by displaying the measured pulse condition diagram, the comparison sample and the similarity, and the pulse condition results in the comparison sample are displayed, so that a user can conveniently know the pulse condition and make a reference for a doctor in advance, a hidden storage mode is adopted for the comparison results with the similarity ratio lower than a certain value, the display image is the comparison sample with the similarity higher than 60%, and the data display and use are conveniently completed.
The working principle is as follows: in the using process of the invention, training and learning of calculation methods such as data acquisition, comparison, screening and the like can be adopted for calculation and use by adopting a Tiny model Tiny-DSOD target detection algorithm, when in use, a user acquires own pulse condition by a pulse condition acquisition instrument, uploads the pulse condition to a database or a mobile terminal, and obtains a sample with the contrast (similarity) of more than 60 percent by comparing and matching with the data in the database, and the sample is called and displayed, and displays the data such as a waveform diagram, a symptom embodiment, a pulse condition name and the like attached to the corresponding sample data, so that the sample is used for the user to preliminarily know and facilitate a doctor to reduce the judgment range, and the pulse condition diagram without the matching and screening exceeding a certain similarity is uploaded and timely repaired and supplemented, the number of the contrast samples in the database is filled, and the sample is updated along with the passage of time, further improving the overall pulse condition measuring accuracy and the sample coverage, and facilitating the use of advanced reference during self-examination and on-site diagnosis of doctors.
While there have been shown and described the fundamental principles and essential features of the invention and advantages thereof, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but is capable of other specific forms without departing from the spirit or essential characteristics thereof; the present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein, and any reference signs in the claims are not intended to be construed as limiting the claim concerned.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. A radial artery waveform data recognition algorithm is characterized in that: the radial artery waveform data identification algorithm comprises the following specific steps:
s1: data acquisition: a large number of sickbed samples or normal samples are collected through a pulse collector to obtain a pulse fluctuation diagram, the samples are numbered in sequence, manual pulse cutting and pulse marking are adopted, corresponding pulse conditions are marked on the numbers to form a contrast sample, and then a plurality of valley points, peak points and random contrast points are arranged on the waveform diagram and used for comparing with the pulse fluctuation diagram obtained by new measurement of a patient in use, and a large number of contrast samples are uploaded to a database and used for enlarging the use area.
S2: and (3) data comparison: marking a newly acquired pulse fluctuation image by taking the comparison sample comprising the plurality of comparison points in the step S1 as a comparison reference, then carrying out comparison training with the comparison sample, disordering the sequence of the comparison sample after the comparison result reaches a certain qualification rate, realizing speed training of comparison of the similarity of the comparison points by multi-point reference setting, finally carrying out manual extraction on the comparison samples similar to the plurality of oscillograms, carrying out simulation training on the precision of the acquired pulse image in the similar oscillograms, returning the numerical value of the similarity percentage according to the similarity degree, and guiding a user or a doctor for use.
S3: and (3) screening data: after the data comparison training in the step S2 is completed, the user converts the acquired pulse condition into a waveform diagram by the pulse condition collector, compares the waveform diagram with the comparison sample in the step S1 database, sorts the comparison sample from high to low according to the degree of similarity with the comparison sample, hides the comparison diagram lower than a certain similar value, does not directly display the comparison diagram, and displays the comparison sample label and the corresponding pulse condition according to the comparison sample label called in the step S1, thereby completing the data screening.
S4: and (3) displaying data: with the good contrast result of sequencing in step S3, through will measuring the pulse manifestation picture, contrast sample, similarity show to the pulse manifestation result in the display contrast sample, the user of being convenient for knows the pulse manifestation and carries out reference effect in advance to the doctor, is less than the contrast result of certain numerical value to similar proportion and adopts the mode of hiding and accomodating, is convenient for accomplish the data display and uses.
2. The radial artery waveform data recognition algorithm of claim 1, wherein: the step S1 further includes post-data padding and repairing, in the long-time use process, according to the results of the client-side acquisition of different pulse oscillograms or errors in comparison results and actual diagnosis, the results are uploaded by the client-side, so that the pulse oscillograms are marked to be supplemented, the pulse oscillograms with errors in diagnosis are marked to be verified, and the results are repaired after conclusions are made by means of comparison experiments and the like.
3. The radial artery waveform data recognition algorithm of claim 1, wherein: the plurality of contrast points in step S2 may be multiple or full segments from the trough to the peak, and the contrast points are used to quickly calculate corresponding oscillogram values such as slope for performing contrast calculation.
4. The radial artery waveform data recognition algorithm of claim 1, wherein: the presentation in step S3 further includes contrast similarity, which may be indicative of the condition and the corresponding condition, for reference.
5. The radial artery waveform data recognition algorithm of claim 1, wherein: the image shown in the step S4 is a contrast sample with a similarity exceeding 60%.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113223700A (en) * | 2021-04-14 | 2021-08-06 | 北京联世科技有限公司 | Traditional Chinese medicine pulse condition identification method and device based on pulse condition data |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1339287A (en) * | 2000-08-18 | 2002-03-13 | 张洪羿 | Pulse detection medical instrument |
| US20090204668A1 (en) * | 2008-02-12 | 2009-08-13 | Sydney Furan Huang | System and process for distant pulse diagnosis |
| CN204654887U (en) * | 2015-03-26 | 2015-09-23 | 苏州大学 | Based on the Chinese medicine pulse assistant diagnosis system in high in the clouds |
| CN105678229A (en) * | 2015-12-29 | 2016-06-15 | 中国科学院深圳先进技术研究院 | High spectral image retrieval method |
| CN105930679A (en) * | 2016-05-20 | 2016-09-07 | 陕西科技大学 | Intelligent diagnosis-treatment and management system and use method thereof |
| CN106580275A (en) * | 2016-12-12 | 2017-04-26 | 东莞市广信知识产权服务有限公司 | System for processing pulse signals at wrist |
| CN108158556A (en) * | 2016-12-07 | 2018-06-15 | 上海交迅智能科技有限公司 | Diagnosis by feeling the pulse based on internet artificial intelligence is diagnosed a disease method |
| CN108198619A (en) * | 2018-01-10 | 2018-06-22 | 河海大学常州校区 | Internet of Things pulse diagnosis system based on big data analysis |
| CN109313937A (en) * | 2017-09-11 | 2019-02-05 | 深圳市得道健康管理有限公司 | Chinese medicine pulse assistant diagnosis system and method based on cloud computing platform |
| CN110200599A (en) * | 2019-06-06 | 2019-09-06 | 深圳市精气生物技术有限公司 | A kind of pulse wave detecting method, terminal device and system |
| CN209433883U (en) * | 2018-08-08 | 2019-09-24 | 湖南川马医疗科技有限公司 | A kind of intelligent positioning diagnosis by feeling the pulse analyzer based on big data |
| CN110522436A (en) * | 2019-08-22 | 2019-12-03 | 天津森宇科技股份有限公司 | Pulse signal acquisition system |
-
2019
- 2019-12-18 CN CN201911311723.2A patent/CN110974191A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1339287A (en) * | 2000-08-18 | 2002-03-13 | 张洪羿 | Pulse detection medical instrument |
| US20090204668A1 (en) * | 2008-02-12 | 2009-08-13 | Sydney Furan Huang | System and process for distant pulse diagnosis |
| CN204654887U (en) * | 2015-03-26 | 2015-09-23 | 苏州大学 | Based on the Chinese medicine pulse assistant diagnosis system in high in the clouds |
| CN105678229A (en) * | 2015-12-29 | 2016-06-15 | 中国科学院深圳先进技术研究院 | High spectral image retrieval method |
| CN105930679A (en) * | 2016-05-20 | 2016-09-07 | 陕西科技大学 | Intelligent diagnosis-treatment and management system and use method thereof |
| CN108158556A (en) * | 2016-12-07 | 2018-06-15 | 上海交迅智能科技有限公司 | Diagnosis by feeling the pulse based on internet artificial intelligence is diagnosed a disease method |
| CN106580275A (en) * | 2016-12-12 | 2017-04-26 | 东莞市广信知识产权服务有限公司 | System for processing pulse signals at wrist |
| CN109313937A (en) * | 2017-09-11 | 2019-02-05 | 深圳市得道健康管理有限公司 | Chinese medicine pulse assistant diagnosis system and method based on cloud computing platform |
| CN108198619A (en) * | 2018-01-10 | 2018-06-22 | 河海大学常州校区 | Internet of Things pulse diagnosis system based on big data analysis |
| CN209433883U (en) * | 2018-08-08 | 2019-09-24 | 湖南川马医疗科技有限公司 | A kind of intelligent positioning diagnosis by feeling the pulse analyzer based on big data |
| CN110200599A (en) * | 2019-06-06 | 2019-09-06 | 深圳市精气生物技术有限公司 | A kind of pulse wave detecting method, terminal device and system |
| CN110522436A (en) * | 2019-08-22 | 2019-12-03 | 天津森宇科技股份有限公司 | Pulse signal acquisition system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113223700A (en) * | 2021-04-14 | 2021-08-06 | 北京联世科技有限公司 | Traditional Chinese medicine pulse condition identification method and device based on pulse condition data |
| CN113223700B (en) * | 2021-04-14 | 2022-02-08 | 北京联世科技有限公司 | Traditional Chinese medicine pulse condition identification method and device based on pulse condition data |
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Application publication date: 20200410 |