CN105310687A - Dynamic electrocardiogram real-time monitoring method based on mobile internet - Google Patents
Dynamic electrocardiogram real-time monitoring method based on mobile internet Download PDFInfo
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Abstract
The invention discloses a dynamic electrocardiogram real-time monitoring method based on the mobile internet. The method comprises the steps that electrocardiogram data are obtained through electrocardiogram detection equipment; the electrocardiogram data obtained through the electrocardiogram detection equipment are uploaded to a computer so as to be remotely transmitted to a server, a relevant medical mechanism or doctor can read the electrocardiogram data relevant to a detected person from the server through a client computer so as to judge the physical state of the detected person, and therefore the foundation is provided for remote medical treatment; meanwhile, the diagnosis and treat opinions of the doctor can be remotely transmitted to intelligent equipment through the mobile internet. In addition, in order to prevent electrocardiogram data errors, the electrocardiogram data are transmitted to the intelligent equipment before being transmitted to the server, comparison is carried out by the intelligent equipment to the electrocardiogram data, and only the electrocardiogram data without errors can be transmitted to the server. The defect that the obtained electrocardiogram data are inaccurate can be effectively overcome; a user can obtain the diagnosis and treat opinions in real time, so that medical treatment is arranged conveniently, and misdiagnosis is avoided.
Description
Technical field
The present invention relates to a kind of method by development of Mobile Internet technology real-time dynamic monitoring patient body situation, especially by the method for development of Mobile Internet technology dynamic monitoring patient electrocardiogram (ECG) data, particularly a kind of dynamic electrocardiogram method for real-time monitoring based on mobile Internet.
Background technology
Along with the development of Internet technology, especially technology of Internet of things is more and more extensive in the application of medical field.Various armarium not only can miniaturization for domestic, and can to wear in real time, by the health of development of Mobile Internet technology real-time dynamic monitoring patient.But the limited precision of the simple small medical equipment of function, there is inaccurate defect in the data of collection.And existing armarium complex structure, complex operation step, needs professional to operate, and is difficult to carry out life-time service in community medicine, endowment and even remote diagnosis for individual consumer.Especially complicated equipment, numerous lines, can cause the pressure on outpatients mental state and intense strain, may affect patient, and the data that diagnosis is obtained and truth have certain gap, may affect the correct diagnosis to the state of an illness.
To be common in the heart disease of middle-aged and elderly people, in order to prevent to diagnose early in advance, general all needs adopts the electrocardiogram acquisition equipment of specialty to detect electrocardiogram (ECG) data, the namely so-called thought-read electrograph of common people's visual understanding, its most basic operation accurately installs electrocardioelectrode with it at detected object.
What Fig. 1 showed is the three electrode position schematic diagrams led in ECG detecting that prior art is commonly used, namely as figure, three ECG detecting that lead comprise seven electrodes, wherein, first positive pole led is expressed as CH1+ (the electrode sequence number in standard 12 lead specification is expressed as V1), and negative pole is expressed as CH1-(the electrode sequence number in standard 12 lead specification is expressed as V2); Second positive pole led is expressed as CH2+ (the electrode sequence number in standard 12 lead specification is expressed as V3), and negative pole is expressed as CH2-(the electrode sequence number in standard 12 lead specification is expressed as V4); 3rd positive pole led is expressed as CH3+ (the electrode sequence number in standard 12 lead specification is expressed as V5), and negative pole is expressed as CH3-(the electrode sequence number in standard 12 lead specification is expressed as V6); 7th electrode RL is ground electrode (the electrode sequence number in standard 12 lead specification is expressed as V7).
The accurate location of V1-V7 electrode is respectively: V1 electrode is the 5th intercostal space in left anterior axillary line; V2 electrode is at right clavicle and breastbone intersection; V3 electrode is in right border of sternum the 4th intercostal space; V4 electrode is at left clavicle and breastbone intersection; V5 electrode is the 5th rib midline position in left side; V6 electrode, on presternum, is positioned under CH1-electrode and CH2-electrode; V7 electrode is in arcus costarum lower edge position, right side.
In existing universal standard specification, the color for the electrode wires of each electrode also has clear and definite regulation.According to the standard of AHA (American Heart Association), the electrode wires color of V1-V7 electrode is respectively: red, and white is brown, black, orange, blue, green.According to the standard of IEC (International Electrotechnical Commission), the electrode wires color of V1-V7 electrode is respectively: green, red, and white is yellow, orange, blue, black.
As can be seen from the electrode position of Fig. 1 display, the color of each electrode, position are different, need the positioning of electrode of suitable Professional knowledge ability proper operation complexity, because circuit is more, location is complicated, non-professional detection doctor cannot be competent at, and therefore, ordinary individual has been difficult to the ECG detecting of specialty.Although occurred that some aim at the ECG detecting equipment of individual's design in the market, but complex structure, operation also bothers very much, the more important thing is once electrode position places mistake, the electrocardiogram (ECG) data obtained is exactly inaccurate, and unpredictable serious consequence will be brought in the diagnosis and treatment basis in this, as heart disease.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of dynamic electrocardiogram method for real-time monitoring based on mobile Internet, to reduce or to avoid problem noted earlier.
For solving the problems of the technologies described above, the present invention proposes a kind of dynamic electrocardiogram method for real-time monitoring based on mobile Internet, described method comprises the steps: ECG detecting equipment to be worn on detected object, and obtaining one section of duration every a predetermined time is the electrocardiogram (ECG) data of H; It is that the electrocardiogram (ECG) data of S sends to a smart machine by bluetooth that described ECG detecting equipment intercepts one section of duration from described electrocardiogram (ECG) data, and described smart machine is compared to described electrocardiogram (ECG) data, and wherein duration S is less than duration H; If described smart machine finds mistake by comparison, send the prompting that reports an error, then again operate described ECG detecting equipment until do not find mistake by comparison; If described smart machine does not find mistake by comparison, then send instruction with the described electrocardiogram (ECG) data controlling described ECG detecting equipment and obtained all be transferred to one with described ECG detecting equipment connection upload computer, upload computer by described ECG Data Transmission Based to server by described; The client computer be connected with described server downloads described electrocardiogram (ECG) data from described server, the diagnosis and treatment suggestion of doctor is transferred to described server by described client computer by described client computer, and described diagnosis and treatment suggestion is passed to described smart machine by mobile Internet by described server.
Preferably, described ECG detecting equipment is three to lead ECG detecting equipment, and the described three ECG detecting equipment that lead comprise CH1+, and CH1-, CH2+, CH2-, CH3+, CH3-, RL be totally seven electrodes, and wherein said electrode CH1+ and CH1-forms the first both positive and negative polarity led; Described electrode CH2+ and CH2-forms the second both positive and negative polarity led; Described electrode CH3+ and CH3-forms the 3rd both positive and negative polarity led; Electrode RL is ground electrode.
Preferably, described smart machine is compared to described electrocardiogram (ECG) data, and to monitor three electrode positions led in ECG detecting process whether wrong, described comparison comprises the steps:
Step one: simplify electrode position and judge, get rid of the position wrong of the described ground electrode RL away from all the other six electrodes, get rid of the position wrong of described negative electrode CH1-, CH2-and CH3-of three close positions;
Step 2: raw data acquisition, according to correct electrode position connected mode, for different tested objects, adopting described ECG detecting equipment to gather the correct electrocardiogram (ECG) data of multiple seasonal effect in time series is stored in a raw data base, each time series comprises many described correct electrocardiogram (ECG) datas that one group of interval same time gathers, each article of described correct electrocardiogram (ECG) data comprises and described first to lead, second to lead and the 3rd electrocardio test voltage CV1 led, CV2 and CV3;
Step 3: assuming that the electrocardio test voltage CV1 in each described correct electrocardiogram (ECG) data, CV2 and CV3, can be undertaken rebuilding acquisition by following formula 1, described formula 1 is:
CV1＝b11*1+b12*CV2+b13*CV3
CV2＝b21*1+b22*CV1+b23*CV3
CV3＝b31*1+b32*CV1+b33*CV2
Described correct electrocardiogram (ECG) data in described raw data base is substituted into described formula 1, calculates the matrix numerical value of the coefficient bk obtained in described formula 1:
Step 4: the matrix numerical value of the described coefficient bk calculating acquisition in step 3 is substituted into described formula 1, by the described electrocardio test voltage CV1 of described for each in described raw data base correct electrocardiogram (ECG) data, CV2 and CV3 substitutes into described formula 1 equally, namely may correspond to acquisition one group virtual electrocardio voltage DV1, DV2 and DV3;
Calculate the electrocardio test voltage CV1 of correct electrocardiogram (ECG) data described in each seasonal effect in time series one group, the described virtual electrocardio voltage DV1 that CV2 and CV3 is corresponding with it, the correlation coefficient f1 between DV2 and DV3, f2 and f3;
Define a linear function formula 2:
Z＝T0+T1*f1+T2*f2+T3*f3
Described correlation coefficient f1, f2 and the f3 substitution formula 2 each group calculating obtained all can obtain a corresponding function Z, and each described function Z is substituted into a decision-making formula 3:
Described decision function g (Z) corresponding to correct electrocardiogram (ECG) data equals 1 and determined, solution formula 3, substituting into formula 2, calculating the T coefficient matrix numerical value obtained in formula 2 by formula 3 by calculating each described function Z obtained:
T＝[T0T1T2T3]
Step 5: adopt ECG detecting equipment official testing electrocardiogram (ECG) data same in step 2, the described simplification electrode position determining step of same employing step one, obtain multiple seasonal effect in time series many articles of official testing electrocardiogram (ECG) datas described first to lead, second to lead and the 3rd formal electrocardio test voltage CV1, CV2 and CV3 led;
The described formal electrocardio test voltage CV1 of many official testing electrocardiogram (ECG) data described in multiple seasonal effect in time series that test is obtained, the matrix numerical value calculating the described coefficient bk of acquisition in CV2 and CV3 and step 3 substitutes into described formula 1, each time series correspondence obtains one group of virtual formal voltage DV1, DV2 and DV3;
Calculate the virtual formal voltage DV1 that described in each seasonal effect in time series one group, formal electrocardio test voltage CV1, CV2 and CV3 are corresponding with it, the correlation coefficient f1 between DV2 and DV3, f2 and f3; The described correlation coefficient f1 obtained will be calculated, f2 and f3 and step 4 calculate the described T coefficient matrix numerical value obtained and substitute into the numerical value that formula 2 obtains linear function Z, the numerical value of described function Z is substituted into decision-making formula 3, calculates the numerical value obtaining described decision function g (Z);
If the numerical value calculating the described decision function g (Z) obtained is more than or equal to the demarcation numerical value t of a setting, then judge that electrode position does not have wrong;
If the numerical value calculating the described decision function g (Z) obtained is less than described demarcation numerical value t, then judges electrode position wrong and send the prompting that reports an error, repeating step 5, until judge that electrode position does not have wrong.
Preferably, described step 2 comprises data base further and increases step: by the described electrocardio test voltage CV1 in described for each in described raw data base correct electrocardiogram (ECG) data, CV2 and CV3 carries out permutation and combination, forms five new wrong electrocardiogram (ECG) datas and is stored in described raw data base.
Preferably, the step gathering correct electrocardiogram (ECG) data described in described step 2 is: each tested object follow-on test 24 hours, in these 24 hours, within 10 seconds, obtains a described correct electrocardiogram (ECG) data every test in 1 hour.
Preferably, described for each in described raw data base wrong electrocardiogram (ECG) data is substituted in step 4, described decision function g (Z) corresponding to wrong electrocardiogram (ECG) data equals 0 and determined, solution formula 3, substituting into formula 2 by formula 3 by calculating each described function Z obtained, calculating the T coefficient matrix numerical value obtained in formula 2.
Preferably, utilize the described correct electrocardiogram (ECG) data in described raw data base and described wrong electrocardiogram (ECG) data to adopt gradient descent method to calculate and obtain described T coefficient matrix numerical value.
Preferably, in described step 4, the demarcation numerical value t of described setting is more than or equal to 0.5.
Preferably, in described step 3, the matrix numerical value of the coefficient bk in described formula 1 is:
Preferably, in described step 4, the T coefficient matrix numerical value in described formula 2 is:
T＝[-3.41157.75074.1454-4.5733]。
Dynamic electrocardiogram method for real-time monitoring based on mobile Internet of the present invention, can by the dynamic electrocardiogram (ECG) data of Network Capture detected object, simultaneously for avoiding electrocardiogram (ECG) data mistake, provide a kind of extra surveillance and control measure, before being transferred to server, first electrocardiogram (ECG) data is sent to smart machine, by smart machine, electrocardiogram (ECG) data is compared process, only have and do not find that the electrocardiogram (ECG) data of mistake just can be transferred to server, this surveillance and control measure avoids the inaccurate defect of electrocardiogram (ECG) data that layman's operated from a distance obtains, diagnosis and treatment data are avoided to be forbidden brought serious consequence.The diagnosis and treatment suggestion of doctor also can pass to smart machine by mobile Internet is long-range simultaneously, and user can also obtain diagnosis and treatment suggestion in real time, so that arrange to seek medical advice, can not worry mistaken diagnosis.
Accompanying drawing explanation
The following drawings is only intended to schematically illustrate the present invention and explain, not delimit the scope of the invention.Wherein,
What Fig. 1 showed is the three electrode position schematic diagrams led in ECG detecting that prior art is commonly used;
Fig. 2 display be the schematic flow sheet of the dynamic electrocardiogram method for real-time monitoring based on mobile Internet according to a specific embodiment of the present invention;
Fig. 3 display be the curve synoptic diagram of the decision function that decision model according to the present invention is drawn.
Detailed description of the invention
In order to there be understanding clearly to technical characteristic of the present invention, object and effect, now contrast accompanying drawing and the specific embodiment of the present invention is described.Wherein, identical parts adopt identical label.
Just as described in the background section, based on community medicine, the needs of endowment, ordinary consumer also has the demand of remote diagnosis, but due to existing equipment too complex, the detection data obtained by remote mode are difficult to ensure accurately, even if to lead ECG detecting process for three in aforementioned exemplary, all need the exact connect ion problem relating to seven electrodes, ordinary people is difficult to the correct detection accurately completing electrocardiogram (ECG) data, therefore the invention provides a kind of dynamic electrocardiogram method for real-time monitoring based on mobile Internet, in order to before long-range reception electrocardiogram (ECG) data, special means are adopted only to upload correct electrocardiogram (ECG) data to server, simultaneously by the smart machine of mobile Internet by the diagnosis and treatment suggestion mobile phone passing to user of doctor and so on.
Specifically, as shown in Figure 2, its display be the schematic flow sheet of the dynamic electrocardiogram method for real-time monitoring based on mobile Internet according to a specific embodiment of the present invention, described method comprises the steps:
Be worn on by ECG detecting equipment 100 on detected object 400, obtaining one section of duration every a predetermined time is the electrocardiogram (ECG) data of H.Such as, for the old people colony that heart disease risk is higher, usually detected object 400 continuous wear ECG detecting equipment 100 is needed, the dynamic electrocardiogram (ECG) data of Real-time Obtaining detected object 400, namely ECG detecting equipment 100 is in real-time holding state, but if continue to obtain electrocardiogram (ECG) data, then data volume is too large, also unnecessary, it is such as comparatively suitable every 6 hours acquisition durations electrocardiogram (ECG) data of 60 minutes to it is therefore preferable that.
Certainly, whether this electrocardiogram (ECG) data of 60 minutes is ignorant accurately and reliably, if the data of mistake are transferred to far-end server, wastes even harmful.Therefore next step is, it is that the electrocardiogram (ECG) data of S sends to a smart machine 300 by bluetooth that ECG detecting equipment 100 intercepts one section of duration from described electrocardiogram (ECG) data, and smart machine 300 is compared to described electrocardiogram (ECG) data, and wherein duration S is less than duration H.That is the object of this step is compared to the electrocardiogram (ECG) data that ECG detecting equipment 100 obtains, to judge whether these electrocardiogram (ECG) datas exist mistake.Certainly, based on the consideration of cost and reliability, ECG detecting equipment 100 only possesses function and the data transmission interface of ECG detecting, and the smart machine 300 work of comparing being given the smart mobile phone or panel computer and so on of general performance surplus is relatively preferred schemes.Simultaneously, whether mistake does not need whole data to comparison electrocardio yet, therefore preferably intercepting the shorter one section of electrocardiogram (ECG) data (such as the electrocardiogram (ECG) data of 1 minute) of duration sends to smart machine 300 to compare work, also the considerable transmission time can be saved, reduce the performance requirement of ECG detecting equipment 100 and smart machine 300 simultaneously, can user cost be reduced.
Afterwards, if smart machine 300 finds mistake by comparison, send the prompting that reports an error, then again operate ECG detecting equipment 100 until do not find mistake by comparison.
If smart machine 300 does not find mistake by comparison, then send instruction with the described electrocardiogram (ECG) data controlling ECG detecting equipment 100 and obtained all be transferred to one be connected with ECG detecting equipment 100 upload computer 700, by uploading computer 700 by described ECG Data Transmission Based to server 200.Why this step needs to use is uploaded computer 700, equally also be based on cost consideration, the function that complex hardware should not be provided to require to ECG detecting equipment 100, to reduce the complexity of ECG detecting equipment 100, improving its reliability, the work of large-capacity data transmission being given common computer that namely general family have as uploading computer 700 simultaneously.Such as, when smart machine 300 does not find mistake, get final product the instruction that video data is on its screen normal, can upload, electro-detection equipment 100 can be uploaded computer 700 by USB data line connection according to instruction by user, electrocardiogram (ECG) data in electro-detection equipment 100 is all transferred to uploads computer 700 by uploading the special-purpose software that computer 700 is installed, and then be transferred to server 200 by uploading computer 700.
After server 200 has transmitted electrocardiogram (ECG) data, the client computer 800 be connected with server 200 downloads described electrocardiogram (ECG) data from server 200, the diagnosis and treatment suggestion of doctor can be transferred to server 200 by client computer 800 by client computer 800 simultaneously, and described diagnosis and treatment suggestion is passed to smart machine 300 by mobile Internet 900 by server 200.
Namely, a key concept of the present invention is, the electrocardiogram (ECG) data that ECG detecting equipment 100 can be obtained is by uploading computer 700 remote transmission to server 200, relevant healthcare institution or doctor can read the electrocardiogram (ECG) data relevant to detected object 400 by client computer 800 from server 200, to judge the condition of detected object 400, in this, as the basis of tele-medicine; The diagnosis and treatment suggestion of doctor can pass to smart machine 300 by mobile Internet 900 is long-range simultaneously, and user can obtain diagnosis and treatment suggestion in real time, so that arrange to seek medical advice, can not worry mistaken diagnosis.
But, if the electrocardiogram (ECG) data being transferred to server 200 exists mistake, then likely cause mistaken diagnosis, quite serious consequence can be brought.For avoiding electrocardiogram (ECG) data mistake, the invention provides a kind of extra surveillance and control measure, namely before being transferred to server 200, first electrocardiogram (ECG) data is sent to the smart machine 300 of such as smart mobile phone, panel computer and so on, to be compared process by smart machine 300 pairs of electrocardiogram (ECG) datas, only have and do not find that the electrocardiogram (ECG) data of mistake just can be transferred to server 200, find that mistake just sends the prompting that reports an error, repeat to survey again, until do not find mistake, then according to not finding that the flow process of mistake processes equally.
In a specific embodiment, ECG detecting equipment 100 of the present invention can be three to lead ECG detecting equipment, certainly, before detailed description method of the present invention, still needing to be similar to background technology part re-defines to three each electrodes related in ECG detecting that lead like that, because background parts has carried out very detailed description with reference to Fig. 1, therefore only carry out simply defining as follows for the closely-related electrode of method of the present invention: three of the present embodiment ECG detecting equipment that leads comprises CH1+ herein, CH1-, CH2+, CH2-, CH3+, CH3-, RL is totally seven electrodes, wherein electrode CH1+ and CH1-forms the first both positive and negative polarity led, electrode CH2+ and CH2-forms the second both positive and negative polarity led, electrode CH3+ and CH3-forms the 3rd both positive and negative polarity led, electrode RL is ground electrode.
In another specific embodiment, in method of the present invention, smart machine 300 pairs of electrocardiogram (ECG) datas are compared, and specifically, are monitor three electrode positions led in ECG detecting process whether wrong.The process of concrete comparison comprises the steps:
Step one: first, judging three electrode positions led in ECG detecting process whether before wrong, considering that the complex array combined result of seven malposition of electrode is too large, therefore needing to carry out the judgement of simplifications electrode position.Namely, first the position wrong of the ground electrode RL away from all the other six electrodes is got rid of, visible with reference to Fig. 1,7th electrode RL is away from all the other six electrodes, be very easy to connect, the probability of wrong is very low, therefore the probability of this electrode RL position wrong is got rid of, and in subsequent step, same do eliminating process can obtain same effect.
Then, the negative electrode CH1-of three close positions is got rid of, the position wrong of CH2-and CH3-.After getting rid of the situation of the 7th electrode position wrong, still need consideration three to the situation of the location dislocation of totally six electrodes of leading, suppose that six electrode positions exchange arbitrarily, the scheme that 6 unequal to 720 kinds is possible can be caused, judge to get up very difficult.But, fortunately, as shown in Figure 1, three negative electrode CH1-, the position of CH2-and CH3-is close, and electric potential difference is very little, therefore can suppose that they are equipotential substantially, and if there is exchange three negative electrode positions, because their position is very near, the therefore small electric potential difference caused can be ignored completely.Therefore it is also rational, same for simplifying during electrode position judges the situation getting rid of the negative pole position wrong of three close positions, does to get rid of process equally and also can obtain same effect in subsequent step.
After carrying out simplification electrode position judgement process, can find out with reference to Fig. 1, due to three anelectrode CH1+, CH2+ and CH3+ is arranged in far-end, and thus electric potential difference is large for distance each other, therefore, the present invention is about the determination methods of electrode position wrong, situation when only needing consideration three positive electrode position to exchange, i.e. three anelectrode CH1+, the location status that CH2+ and CH3+ may possess is listed as follows:
Wherein, P1, P2, P3 represent three anelectrode CH1+, the sequence of positions at CH2+, CH3+ place, and such as, in location status 1, P1 position is the position at CH1+ place in Fig. 1, and P2 position is the position at CH2+ place in Fig. 1, and P3 position is the position at CH3+ place in Fig. 1.Wherein, always have six kinds of sequence of positions, suppose wherein only have location status 1 to be correct, i.e. electrode CH1+, CH2+, CH3+ respectively in P1 position, P2 position, P3 position.
Step 2: below it is envisaged that judge that in six kinds of possible location status, any is correct, remaining is all the position of mistake.
Therefore, first step 2 will store the data of a large amount of tram states, namely by raw data acquisition, in order to as the basis judging wrong.Namely, in a specific embodiment, according to correct electrode position connected mode, for different tested objects, adopting ECG detecting equipment 100 to gather the correct electrocardiogram (ECG) data of multiple seasonal effect in time series is stored in a raw data base, each time series comprises many correct electrocardiogram (ECG) datas that one group of interval same time gathers, and each article of correct electrocardiogram (ECG) data comprises and first to lead, second to lead and the 3rd electrocardio test voltage CV1 led, CV2 and CV3.
Concrete, the step gathering correct electrocardiogram (ECG) data is: each tested object follow-on test 24 hours, in these 24 hours, within 10 seconds, obtain a correct electrocardiogram (ECG) data every test in 1 hour, therefore each tested object follow-on test can obtain 24 correct electrocardiogram (ECG) datas for one day.Certainly, those skilled in the art can find out, even if such follow-on test a whole day, a tested object also can only obtain little data, in view of the surging situation of cost of labor, wanting to obtain abundant correct electrocardiogram (ECG) data is also a very expensive job.
Therefore, invention further provides the step that a kind of data base increases, in order to reduce the collection capacity of initial data, reduce cost of labor.Be specially:
By the electrocardio test voltage CV1 in the correct electrocardiogram (ECG) data of each in raw data base, CV2 and CV3 carries out permutation and combination, forms five new data and is stored in raw data base.
Such as, the original alignment order of a correct electrocardiogram (ECG) data in raw data base is, CV1, CV2 and CV3, therefore carry out permutation and combination according to according to optional position, can form six data altogether, wherein Article 1 data are original correct electrocardiogram (ECG) datas, all the other five data are the wrong electrocardiogram (ECG) datas of the positional fault newly increased, such as, shown in following table:
CV1，CV2，CV3
CV2，CV1，CV3
CV3，CV2，CV1
CV1，CV3，CV2
CV3，CV1，CV2
CV2，CV3，CV1
The new data that combinations thereof is formed be stored in equally in raw data base, therefore increasing step by data base can expand as original six times by the data volume of raw data base, greatly reduces the workload of image data, reduces cost.Certainly, it is correct electrocardiogram (ECG) data that raw data base needs which marks, and which is wrong electrocardiogram (ECG) data so that solve coefficient matrix numerical value time the correct electrocardiogram (ECG) data of later use carries out gradient descent method training, after will further describe this.
Step 3: data reconstruction.Assuming that the electrocardio test voltage CV1 in each correct electrocardiogram (ECG) data in raw data base, CV2 and CV3, can be undertaken rebuilding acquisition by following formula 1, described formula 1 is:
CV1＝b11*1+b12*CV2+b13*CV3
CV2＝b21*1+b22*CV1+b23*CV3
CV3＝b31*1+b32*CV1+b33*CV2
The above-mentioned hypothesis rebuild is a kind of protocol step proposed based on the invention thinking, have passed follow-up and true inspection, show that this supposition possesses practical value, also be difficult to complete analysis as its principle inventor illustrate, those skilled in the art carry out operation based on hypothesis of the present invention can obtain correct result.
Afterwards, the correct electrocardiogram (ECG) data in raw data base is substituted into described formula 1, calculates the matrix numerical value of the coefficient bk obtained in formula 1:
As can be seen from formula 1, three equations comprise nine coefficients altogether, these nine coefficients to be solved and calculate, at least need three official testing electrocardiogram (ECG) datas, each data has three formal electrocardio test voltages, substitute into formula 1 and can form nine equations, simple solution can obtain the matrix numerical value of coefficient bk.Certainly, in order to reduce the error that individual data brings, such as 100 in raw data base can be utilized, and even 1000 data (comprising the data after data base's increase), adopt the mode difference the Fitting Calculation of method of least square to obtain the matrix numerical value of coefficient bk, the matrix numerical value of the coefficient bk obtained like this will be relatively accurately a lot.
About the common practise that the method being obtained more exact computation results by least square fitting is art of mathematics, by textbook or internet checking, those skilled in the art can both know that matching of the present invention obtains the ultimate principle of the matrix numerical value of coefficient bk: the optimal function coupling being found data by the quadratic sum of minimum error, and the quadratic sum making error between the data of trying to achieve and real data is minimum.Due to a kind of method that method of least square is known fitted data, its substantially a point principle be not protection scope of the present invention, therefore this is no longer going to repeat them.
In a specific embodiment, same ECG detecting equipment 100 is adopted to gather, each tested object follow-on test 24 hours, in these 24 hours, within 10 seconds, a correct electrocardiogram (ECG) data is obtained every test in 1 hour, such as after tested object is more than 100 people, the matrix numerical value of the coefficient bk calculated in the formula 1 obtained by above-mentioned steps is:
Step 4: decision function builds.The matrix numerical value of the coefficient bk calculating acquisition in step 3 is substituted into described formula 1, simultaneously by the electrocardio test voltage CV1 of the correct electrocardiogram (ECG) data of each in raw data base, CV2 and CV3 substitutes into formula 1 equally, namely may correspond to acquisition one group virtual electrocardio voltage DV1, DV2 and DV3.
Certainly, increase in step aforesaid data base, each the wrong electrocardiogram (ECG) data be stored in raw data base obtained by rearranging combination also can substitute into formula 1 equally, also correspondingly can obtain one group of virtual electrocardio voltage, uses to be ready for use on subsequent flows journey.
Should be noted that, because the matrix numerical value calculating the coefficient bk of acquisition in step 3 is the result obtained after the mathematical method Optimal Fitting of many data separate method of least square and so on, therefore by the electrocardio test voltage CV1 of each correct electrocardiogram (ECG) data, small deviation can be there is in the result that CV2 and CV3 obtains after substituting in formula 1 and initial data, therefore the one group of numerical value calculated through formula 1 is not real numerical value, it should be the virtual numerical value close to actual value, therefore virtual electrocardio voltage DV1 is defined as in the present invention, DV2 and DV3, in order to make a distinction with actual value, certain those skilled in the art also can be defined by other title, it is only a title code name herein, a virtual word wherein should be interpreted as other implication any by those skilled in the art.
Subsequently, calculate the electrocardio test voltage CV1 of each seasonal effect in time series one group of correct electrocardiogram (ECG) data, the virtual electrocardio voltage DV1 that CV2 and CV3 is corresponding with it, the correlation coefficient f1 between DV2 and DV3, f2 and f3, in order to the difference between these two groups of data of comparison.That is, correlation coefficient f3 between correlation coefficient f1 between one group of DV1 and CV1 under the formula of art of mathematics known calculating correlation coefficient can be utilized to calculate each time series respectively, the correlation coefficient f2 between one group of DV2 and CV2 under each time series and one group of DV3 and CV3 under each time series.About the common practise that the computational methods solving correlation coefficient are art of mathematics, its ultimate principle is not the scope of protection of present invention, and those skilled in the art search mathematical textbooks and can be easy to obtain, and this is no longer going to repeat them.
Afterwards, a linear function formula 2 is defined:
Z＝T0+T1*f1+T2*f2+T3*f3
Each group is calculated the correlation coefficient f1 obtained, f2 and f3 substitution formula 2 all can obtain a corresponding function Z, certainly, due to the coefficient T 0, T1 in now formula 2, T2 and T3 or unknown, now function Z is not the numerical value determined, needs to be solved by following step to obtain coefficient T 0, T1, after the concrete numerical value of T2 and T3, formula 2 could use in follow-up official testing.
Above-mentioned formula 2 is a kind of Scenarios proposed based on the invention thinking, namely each group correlation coefficient f1 is supposed, linear relationship is met between f2 and f3, by follow-up and true inspection, show that this supposition possesses practical value, also be difficult to complete analysis as its principle inventor illustrate, those skilled in the art carry out operation based on hypothesis of the present invention can obtain correct result.
Then, each described function Z is substituted into a decision-making formula 3:
Now, if know the numerical value of decision function g (Z), then can substitute into the equation left side of formula 3, then the concrete numerical value of the function Z obtained on the right of equation just oppositely can be solved by formula 3, substituting into formula 2 by calculating each described function Z obtained, calculating the T coefficient matrix numerical value obtained in formula 2:
T＝[T0T1T2T3]
About decision-making formula 3, be that the one that inventor chooses from different decision scheme is applicable to decision model of the present invention, as shown in Figure 3, its display be the curve synoptic diagram of the decision function that decision model according to the present invention is drawn, wherein, using linear function Z as decision boundary function in Fig. 3, represent with transverse axis coordinate, ordinate of orthogonal axes is expressed as decision function g (Z).As can be seen from Fig. 3, when with the formula 3 judge the decision function of electrode position whether wrong as the present invention when, when decision function g (Z) is 1, electrode position should be right-on, when decision function g (Z) is 0, then electrode position should be full of prunes.
Therefore, the principle based on decision-making formula 3 is known, and by the correct electrocardiogram (ECG) data of each in raw data base after abovementioned steps converts, the corresponding determining function g (Z) obtained just should equal 1; Same, by the wrong electrocardiogram (ECG) data of each in raw data base after abovementioned steps converts, the corresponding determining function g (Z) obtained just should equal 0 (mistake electrocardiogram (ECG) data increases step by aforesaid data base and obtained).
Therefore, the feature that when utilizing decision-making formula 3 position correct, functional value equals 1, by the correct electrocardiogram (ECG) data solution formula 3 in raw data base, then can substitute into formula 2 by the concrete numerical value solving the function Z obtained, just can obtain T coefficient matrix numerical value.Same, the feature that when also can utilize decision-making formula 3 positional fault, functional value equals 0, by the wrong electrocardiogram (ECG) data solution formula 3 in raw data base, then the concrete numerical value solving the function Z obtained is substituted into formula 2, T coefficient matrix numerical value can be obtained equally.
Due in formula 2, correlation coefficient f1, f2 and f3 are known, coefficient T 0, T1, T2 and T3 are unknown, and now formula 2 is equal to a quaternary linear equation, minimum needs four groups of correlation coefficienies substitute into the T coefficient matrix numerical value that formula 2 just can obtain coefficient T 0, T1, T2 and T3.
Certainly, because the correct electrocardiogram (ECG) data in raw data base of the present invention can not be only limitted to four certainly, simultaneously, the wrong data of five haplotype data amounts can be obtained by data base's expansion step, therefore can be obtained T coefficient matrix numerical value of the present invention by the method that the matrix numerical value of the coefficient bk in solution formula 1 is similar.In a specific embodiment, the present invention preferably adopts gradient descent method to calculate the T coefficient matrix numerical value obtained in formula 2.
Be similar to aforesaid method of least square, gradient descent method is also a kind of known mathematic calculation, by textbook or internet checking, those skilled in the art can both know that the present invention calculates the ultimate principle obtaining T coefficient matrix numerical value by gradient descent method: utilize negative gradient direction to decide the new direction of search of each iteration, make each iteration that object function to be optimized can be made progressively to reduce, gradient descent method is to obtain the convergence of function parameter by continuous iteration, within the specific limits, the scale of input data determines the degree of convergence of its result of calculation, that is data volume in raw data base is larger, the T coefficient matrix numerical value obtained more close to theoretical value (when accuracy exceedes certain threshold value, as 99%, Shi Ze does not need to carry out more data again, because too much data can increase human cost, convergence concussion may be caused because input data itself exist error) simultaneously.Due to a kind of method that gradient descent method is known fitted data, its ultimate principle is not protection scope of the present invention, and therefore this is no longer going to repeat them.
In a specific embodiment, same ECG detecting equipment 100 is adopted to gather, each tested object follow-on test 24 hours, in these 24 hours, within 10 seconds, a correct electrocardiogram (ECG) data is obtained every test in 1 hour, such as after tested object is more than 100 people, by above-mentioned steps, the T coefficient matrix numerical value calculated in the formula 2 obtained is:
T＝[-3.41157.75074.1454-4.5733]
Step 5: formal detect and judge electrode position whether wrong.When operator formally start to detect, it can adopt ECG detecting equipment 100 official testing electrocardiogram (ECG) data same in step 2, the simplification electrode position determining step of same employing step one, obtain multiple seasonal effect in time series many articles of official testing electrocardiogram (ECG) datas first to lead, second to lead and the 3rd formal electrocardio test voltage CV1, CV2 and CV3 led.When namely formally starting to detect, need to keep initial condition constant, namely suppose the 7th electrode, the position of whole three negative poles does not have wrong.
Then, by the formal electrocardio test voltage CV1 of multiple seasonal effect in time series many official testing electrocardiogram (ECG) datas that test obtains, the matrix numerical value calculating the coefficient bk of acquisition in CV2 and CV3 and step 3 substitutes into described formula 1, each time series correspondence obtains one group of virtual formal voltage DV1, DV2 and DV3.
Afterwards, the virtual formal voltage DV1 that each seasonal effect in time series one group formal electrocardio test voltage CV1, CV2 and CV3 are corresponding with it is calculated, the correlation coefficient f1 between DV2 and DV3, f2 and f3; To calculate the correlation coefficient f1 obtained, f2 and f3 and step 4 calculate the T coefficient matrix numerical value obtained and substitute into the numerical value that formula 2 obtains linear function Z, and the numerical value of function Z is substituted into decision-making formula 3, calculate the numerical value obtaining decision function g (Z).
If the numerical value calculating the decision function g (Z) obtained is more than or equal to the demarcation numerical value t of a setting, then judge that electrode position does not have wrong.
If the numerical value calculating the decision function g (Z) obtained is less than described demarcation numerical value t, then judges electrode position wrong and send the prompting that reports an error, repeating step 5, until judge that electrode position does not have wrong.
Judge that electrode position does not have wrong in fact to complete the comparison process of smart machine 300 pairs of electrocardiogram (ECG) datas, be exactly afterwards send instruction with the electrocardiogram (ECG) data controlling ECG detecting equipment 100 and obtained by Internet Transmission to server 200.
Such as, when setting demarcation numerical value t and being more than or equal to 0.5, if the numerical value calculating the decision function g (Z) obtained is more than or equal to 0.5, then judge that electrode position does not have wrong.This is because the T coefficient matrix numerical value in formula 2 utilizes the data of the data of whole tram or whole errors present to calculate to obtain, thus the formal data detected substitute into formula 2, substitute into the g (Z) that formula 3 obtains again and only likely occur two kinds of probabilities, close to 0 or close to 1, if be more than or equal to 0.5, then can judge that electrode position is very likely correct according to the curve law of decision function.In fact if the numerical result of decision function g (Z) that obtains of positional fault, should be close to equaling 0, setting is demarcated numerical value t and is equaled the threshold value that 0.5 has greatly relaxed judgement, make to judge that the correct accuracy rate in position is higher, if it is also feasible that namely setting demarcation numerical value t equals 0.3,0.4, also be possess practical accuracy rate, accuracy rate just relative to 0.5 is lower, same, demarcation numerical value t also can set and equal 0.6,0.7, and accuracy rate can be higher.Just along with the raising of accuracy rate, the requirement correct for details of operation is higher, likely needs repeated multiple times measurement with closest that position accurately of comparison.
Otherwise, if judge electrode position wrong, when then the numerical value of smart machine 300 comparison g (Z) is less than t, control loudspeaker or light or display screen send the prompting that reports an error, after tester experiences the prompting that reports an error, can electrode position be changed, retest and comparison, judge electrode position whether wrong.
In a specific embodiment, testing staff can adjust electrode position one by one according to an electrode position status list, such as, three anelectrode CH1+, the location status that CH2+ and CH3+ may possess is listed as follows:
If judge that location status 1 exists mistake after detecting formal, then can be adjusted to location status 2 retest comparison, if judge that position is correct, then store test data is for subsequent use.If position wrong, then change to location status 3 again, the rest may be inferred, only need replacing six location status just can determine correct electrode position at most, thus the electrocardiogram (ECG) data that can supply practicality accurately can be obtained, the layman that background technology part can be avoided to mention cannot judge the inaccurate defect of electrocardiogram (ECG) data obtained, and avoids the diagnosis and treatment of the heart disease to detected personnel to be forbidden, and even the serious consequence that delays treatment is brought.
In sum, dynamic electrocardiogram method for real-time monitoring based on mobile Internet of the present invention, can by the dynamic electrocardiogram (ECG) data of Network Capture detected object, simultaneously for avoiding electrocardiogram (ECG) data mistake, provide a kind of extra surveillance and control measure, before being transferred to server, first electrocardiogram (ECG) data is sent to smart machine, by smart machine, electrocardiogram (ECG) data is compared process, only have and do not find that the electrocardiogram (ECG) data of mistake just can be transferred to server, this surveillance and control measure avoids the inaccurate defect of electrocardiogram (ECG) data that layman's operated from a distance obtains, diagnosis and treatment data are avoided to be forbidden brought serious consequence.The diagnosis and treatment suggestion of doctor also can pass to smart machine by mobile Internet is long-range simultaneously, and user can also obtain diagnosis and treatment suggestion in real time, so that arrange to seek medical advice, can not worry mistaken diagnosis.
Although it will be appreciated by those skilled in the art that the present invention is described according to the mode of multiple embodiment, not each embodiment only comprises an independently technical scheme.So describe in description be only used to clear for the purpose of; description should integrally be understood by those skilled in the art, and regards technical scheme involved in each embodiment as the mode that mutually can be combined into different embodiment to understand protection scope of the present invention.
The foregoing is only the schematic detailed description of the invention of the present invention, and be not used to limit scope of the present invention.Any those skilled in the art, the equivalent variations done under the prerequisite not departing from design of the present invention and principle, amendment and combination, all should belong to the scope of protection of the invention.
Claims (10)
1., based on a dynamic electrocardiogram method for real-time monitoring for mobile Internet, described method comprises the steps:
Be worn on by ECG detecting equipment (100) on detected object (400), obtaining one section of duration every a predetermined time is the electrocardiogram (ECG) data of H;
It is that the electrocardiogram (ECG) data of S sends to a smart machine (300) by bluetooth that described ECG detecting equipment (100) intercepts one section of duration from described electrocardiogram (ECG) data, described smart machine (300) is compared to described electrocardiogram (ECG) data, and wherein duration S is less than duration H;
If described smart machine (300) finds mistake by comparison, send the prompting that reports an error, then again operate described ECG detecting equipment (100) until do not find mistake by comparison;
If described smart machine (300) does not find mistake by comparison, then send instruction with the described electrocardiogram (ECG) data controlling described ECG detecting equipment (100) and obtained all be transferred to one be connected with described ECG detecting equipment (100) upload computer (700), upload computer (700) by described ECG Data Transmission Based to server (200) by described;
The client computer (800) be connected with described server (200) downloads described electrocardiogram (ECG) data from described server (200), the diagnosis and treatment suggestion of doctor is transferred to described server (200) by described client computer (800) by described client computer (800), and described diagnosis and treatment suggestion is passed to described smart machine (300) by mobile Internet (900) by described server (200).
2. the method for claim 1, it is characterized in that, described ECG detecting equipment (100) is three to lead ECG detecting equipment, and the described three ECG detecting equipment that lead comprise CH1+, CH1-, CH2+, CH2-, CH3+, CH3-, RL is totally seven electrodes, and wherein said electrode CH1+ and CH1-forms the first both positive and negative polarity led; Described electrode CH2+ and CH2-forms the second both positive and negative polarity led; Described electrode CH3+ and CH3-forms the 3rd both positive and negative polarity led; Electrode RL is ground electrode.
3. method as claimed in claim 2, it is characterized in that, described smart machine (300) is compared to described electrocardiogram (ECG) data, and to monitor three electrode positions led in ECG detecting process whether wrong, described comparison comprises the steps:
Step one: simplify electrode position and judge, get rid of the position wrong of the described ground electrode RL away from all the other six electrodes, get rid of the position wrong of described negative electrode CH1-, CH2-and CH3-of three close positions;
Step 2: raw data acquisition, according to correct electrode position connected mode, for different tested objects, adopting described ECG detecting equipment (100) to gather the correct electrocardiogram (ECG) data of multiple seasonal effect in time series is stored in a raw data base, each time series comprises many described correct electrocardiogram (ECG) datas that one group of interval same time gathers, each article of described correct electrocardiogram (ECG) data comprises and described first to lead, second to lead and the 3rd electrocardio test voltage CV1 led, CV2 and CV3;
Step 3: assuming that the electrocardio test voltage CV1 in each described correct electrocardiogram (ECG) data, CV2 and CV3, can be undertaken rebuilding acquisition by following formula 1, described formula 1 is:
CV1＝b11*1+b12*CV2+b13*CV3
CV2＝b21*1+b22*CV1+b23*CV3
CV3＝b31*1+b32*CV1+b33*CV2
Described correct electrocardiogram (ECG) data in described raw data base is substituted into described formula 1, calculates the matrix numerical value of the coefficient bk obtained in described formula 1:
Step 4: the matrix numerical value of the described coefficient bk calculating acquisition in step 3 is substituted into described formula 1, by the described electrocardio test voltage CV1 of described for each in described raw data base correct electrocardiogram (ECG) data, CV2 and CV3 substitutes into described formula 1 equally, namely may correspond to acquisition one group virtual electrocardio voltage DV1, DV2 and DV3;
Calculate the electrocardio test voltage CV1 of correct electrocardiogram (ECG) data described in each seasonal effect in time series one group, the described virtual electrocardio voltage DV1 that CV2 and CV3 is corresponding with it, the correlation coefficient f1 between DV2 and DV3, f2 and f3;
Define a linear function formula 2:
Z＝T0+T1*f1+T2*f2+T3*f3
Described correlation coefficient f1, f2 and the f3 substitution formula 2 each group calculating obtained all can obtain a corresponding function Z, and each described function Z is substituted into a decision-making formula 3:
Described decision function g (Z) corresponding to correct electrocardiogram (ECG) data equals 1 and determined, solution formula 3, substituting into formula 2, calculating the T coefficient matrix numerical value obtained in formula 2 by formula 3 by calculating each described function Z obtained:
T＝[T0T1T2T3]
Step 5: adopt described ECG detecting equipment (100) official testing electrocardiogram (ECG) data same in step 2, the described simplification electrode position determining step of same employing step one, obtain multiple seasonal effect in time series many articles of official testing electrocardiogram (ECG) datas described first to lead, second to lead and the 3rd formal electrocardio test voltage CV1, CV2 and CV3 led;
The described formal electrocardio test voltage CV1 of many official testing electrocardiogram (ECG) data described in multiple seasonal effect in time series that test is obtained, the matrix numerical value calculating the described coefficient bk of acquisition in CV2 and CV3 and step 3 substitutes into described formula 1, each time series correspondence obtains one group of virtual formal voltage DV1, DV2 and DV3;
Calculate the virtual formal voltage DV1 that described in each seasonal effect in time series one group, formal electrocardio test voltage CV1, CV2 and CV3 are corresponding with it, the correlation coefficient f1 between DV2 and DV3, f2 and f3; The described correlation coefficient f1 obtained will be calculated, f2 and f3 and step 4 calculate the described T coefficient matrix numerical value obtained and substitute into the numerical value that formula 2 obtains linear function Z, the numerical value of described function Z is substituted into decision-making formula 3, calculates the numerical value obtaining described decision function g (Z);
If the numerical value calculating the described decision function g (Z) obtained is more than or equal to the demarcation numerical value t of a setting, then judge that electrode position does not have wrong;
If the numerical value calculating the described decision function g (Z) obtained is less than described demarcation numerical value t, then judges electrode position wrong and send the prompting that reports an error, repeating step 5, until judge that electrode position does not have wrong.
4. method as claimed in claim 3, it is characterized in that, described step 2 comprises data base further and increases step: by the described electrocardio test voltage CV1 in described for each in described raw data base correct electrocardiogram (ECG) data, CV2 and CV3 carries out permutation and combination, forms five new wrong electrocardiogram (ECG) datas and is stored in described raw data base.
5. method as claimed in claim 3, it is characterized in that, the step gathering correct electrocardiogram (ECG) data described in described step 2 is: each tested object follow-on test 24 hours, in these 24 hours, within 10 seconds, obtains a described correct electrocardiogram (ECG) data every test in 1 hour.
6. method as claimed in claim 3, it is characterized in that, described for each in described raw data base wrong electrocardiogram (ECG) data is substituted in step 4, described decision function g (Z) corresponding to wrong electrocardiogram (ECG) data equals 0 and determined, solution formula 3, substituting into formula 2 by formula 3 by calculating each described function Z obtained, calculating the T coefficient matrix numerical value obtained in formula 2.
7. method as claimed in claim 6, is characterized in that, utilizes the described correct electrocardiogram (ECG) data in described raw data base and described wrong electrocardiogram (ECG) data to adopt gradient descent method to calculate and obtains described T coefficient matrix numerical value.
8. the method as described in claim 3-6, is characterized in that, in described step 4, the demarcation numerical value t of described setting is more than or equal to 0.5.
9. the method as described in one of claim 6-8, is characterized in that, in described step 3, the matrix numerical value of the coefficient bk in described formula 1 is:
10. the method as described in one of claim 6-9, is characterized in that, in described step 4, the T coefficient matrix numerical value in described formula 2 is:
T＝[-3.41157.75074.1454-4.5733]
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