CN101268938A - Method and apparatus for electrocardiogram recognition and specification - Google Patents
Method and apparatus for electrocardiogram recognition and specification Download PDFInfo
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Abstract
The invention discloses a method which is used for the recognition and the classification of an ECG and a device thereof. The method and the device obtain a QRS wave group queue obtained by an ECG diagnostic instrument, thereby being capable of obtaining the starting point, the end point, the slope and the R wave position of each QRS wave, the amplitude and the direction of the Q wave, the R wave and the S wave, the interval of two adjacent R waves, the form model of the QRS wave and the incisure and the slurring of the QRS wave and so on. The invention also discloses a mode classification method which is used for the ECG diagnostic instrument and fully borrows ideas from the thought processes of experts. The invention is the method and the device which realize the effective characteristic recognition and the mode classification of the ECG by fewer operations.
Description
Technical field
(Electrocardiogram, ECG) diagnostic apparatus field more particularly, relate to the feature identification and method for classifying modes and the equipment that are used for electrocardiogram (ECG) diagnostic apparatus to the present invention relates to electrocardiogram.
Background technology
Deep transformation is taking place in the medical model of Chinese society, need be from being the medical model at center with big, institute of traditional Chinese medicine, progressively carry out the transition to individual people and be the center, be unit, be scope with the community with the family, integrating medical treatment, disease-prevention or healthcare manages, pay attention to the health services of early diagnosis and treatment, to improve whole people quality of life, the unnecessary cost of minimizing.State Council's " about instruction of development urban community health services " proposes to realize in 2010 the blueprint of " everybody enjoys basic medical and public health service ".Therefore, " wireless penetration " of medical health equipment, " portable ", " family oriented " developing direction that is inevitable, as be used for the auscultation of the heart, lungs sound, but the wireless wearable device of the identification of brain electricity, electrocardiosignal.
In the life, cardiovascular and cerebrovascular disease has the advantages that onset is hurried, be difficult to predict, has become to threaten the most serious, the modal disease of 21 century human life health.For example many heart diseases usually happen suddenly outside hospital, and the outbreak of corresponding symptom stopped when patient was sent to hospital, did not have the ECG record, and this moment, the doctor can not make diagnosis.Use " can wear wireless ECG diagnostic apparatus " ECG in the time of just can in time writing down seizure of disease, and it is transferred to palmtop computer by the short-distance wireless communication mode, the catching exception situation.In case it is dangerous, relevant information can be sent to hospital or emergency center by cordless communication network, notify the doctor to carry out " visit to the parents of schoolchildren or young workers " or allow the timely hospital treatment of patient/family members, and patient just can access tentative diagnosis before arriving emergency room, social and economic benefit can be very huge.
Usually, the electrocardiogram (ECG) data of gathering carried out pretreatment after, enter promptly that electrocardiographic wave detects and the stage of characteristic information extraction.Characteristics of electrocardiogram information extraction algorithm whether accurate is the key problem in the automatic analysis system research process, and precision of analysis depends on the accuracy and the reliability of feature extraction.In electrocardiogram, one group of the P ripple that occurs in sequence, QRS ripple, T ripple and U ripple are represented a complete cardiac cycle (being also referred to as a heart claps), and electrocardiogram is made up of the cardiac cycle that goes round and begins again again and again just.The ecg characteristics information that is comprised in cardiac electrical cycle as shown in Figure 1.
The key problem of ECG computer identification is characteristic signal such as the identification of P ripple, QRS ripple and T ripple and the classification of abnormal conditions of ECG.The identification of QRS ripple is prerequisite, and the main ripple R ripple that accurately detects the QRS ripple is the basis of the whole QRS ripple of identification.
The such equipment requirements of ECG diagnostic apparatus has stronger real-time having only under the situation of less memory headroom, thereby waveform recognition is faced with bigger challenge, but the further existing ECG recognizer of research and design new algorithm at the special demand of wearable device and become inevitable requirement.
Relevant research method has a lot, comprises wavelet analysis, artificial neural network, differential threshold value, knowledge base, syntactic analysis, Markov process, mathematical morphology, syntactic analysis, support vector machine, Artificial Immune Algorithm etc., and constitutes diagnostic system.Because fuzzy, the information of the interference of simplified models, signal, feature is incomplete, and lacks the reason to the aspects such as analysis ability of form, the accuracy rate of COMPUTER DETECTION reaches the level of clinical cardiovascular doctor range estimation far away at present.
If accurately be not target (this is the acceptable practical situations of application such as community medicine) with " absolutely ", the detection effect of said method all can, but the test specification of most of method all is partial, be difficult to be generalized to wider, they have these something in commons: with relating to parameters directly perceived such as slope, amplitude or to them dependence is arranged; Pay attention to not enough to doctor's experience; Calculation of complex; Real-time is not enough.Be the conclusion of the characteristics of main method below.
Parameter experience computation complexity real-time test specification QRS ripple directly perceived detects
1) the explicit part of threshold method utilize common better all good
2) that high relatively poor part is not discussed is good for sentence structure method implicit expression
3) wavelet method implicit expression do not discuss high relatively poor all good
4) that height/general relatively poor part is not discussed is good for morphological approach implicit expression
5) hidden Markov model method
It is good that implicit expression is not discussed high relatively poor part
6) neutral net method
It is good that implicit expression is not discussed high relatively poor part
7) knowledge base Faxian formula partly utilizes high relatively poor part good
These methods mainly are conceived to detection this basis and prerequisite work of QRS ripple.Take all factors into consideration test specification and QRS ripple testing result, threshold method and wavelet method are best, but the wavelet method calculation of complex, and the parameter that threshold method is considered is more limited.Comprehensive, the present concrete grammar that does not satisfy actual ECG feature identification and pattern classification requirement.Should fully pay attention to and absorb to seek solution route aspect doctor's thinking process and experience, the reduction computational complexity.
Summary of the invention
The present invention aims to provide a kind of feature identification and method for classifying modes of the ECG of being applicable to diagnostic apparatus, can realize effective ECG identification and diagnosis with lower amount of calculation.
According to an aspect of the present invention, a kind of method that is used for electrocardiogram identification and classification is provided, this method obtain each QRS ripple starting point and terminal point [start, stop] with and R ripple position P (k), obtain each QRS ripple slope thus, Q ripple, R ripple and S wave amplitude and direction; The interval of adjacent two R ripples; Shape mode such as the thick blunt QRS ripple of QRS ripple incisura and QRS ripple; Wherein, described method comprises:
The waveform calculation procedure is calculated R ripple, Q ripple and S wave amplitude respectively, the slope of QRS ripple, the interval of adjacent two R ripples;
The form identification step according to R ripple, Q ripple and the S wave amplitude that the waveform calculation procedure is calculated, is discerned R ripple, Q ripple and S ripple;
QRS waveform attitude identification step, the morphological characteristic of identification QRS ripple.
Above-mentioned waveform calculation procedure comprises:
The R wave-amplitude calculates substep, calculates R wave amplitude Rs;
The Q wave-amplitude calculates substep, the some f (q) of search amplitude minimum in [start, P (k)] interval range, and calculate Q wave-amplitude Qs;
The S wave-amplitude calculates substep, the some f (s) of search amplitude minimum in [P (k), stop] interval range, and calculate S wave-amplitude Ss;
R ' wave-amplitude calculates substep, the some f (r ') of search amplitude maximum in [P (k), stop] interval range, and calculate r ' wave amplitude r ' s;
Slope meter operator step, the slope of calculating QRS ripple; And
The interval calculation substep, the interval of calculating adjacent two R ripples.
Wherein, QRS waveform attitude is discerned according to the result of form identification step,
Wherein, this QRS waveform attitude identification step comprises that to judge QRS ripple incisura and QRS ripple slightly blunt.
According to a second aspect of the invention, a kind of equipment that is used for electrocardiogram identification and classification is provided, this equipment obtain each QRS ripple starting point and terminal point [start, stop] with and R ripple position P (k), obtain each QRS ripple slope thus, Q ripple, R ripple and S wave amplitude and direction; The interval of adjacent two R ripples; Shape mode such as the thick blunt QRS ripple of QRS ripple incisura and QRS ripple; Wherein, described equipment comprises:
The waveform accountant calculates R ripple, Q ripple and S wave amplitude respectively, the slope of QRS ripple, the interval of adjacent two R ripples;
The form recognition device according to R ripple, Q ripple and the S wave amplitude that the waveform accountant calculates, is discerned R ripple, Q ripple and S ripple;
QRS waveform attitude recognition device, the morphological characteristic of identification QRS ripple.
Wherein, above-mentioned waveform accountant comprises:
The R wave-amplitude calculates sub-device, calculates R wave amplitude Rs;
The Q wave-amplitude calculates sub-device, the some f (q) of search amplitude minimum in [start, P (k)] interval range, and calculate Q wave-amplitude Qs;
The S wave-amplitude calculates sub-device, the some f (s) of search amplitude minimum in [P (k), stop] interval range, and calculate S wave-amplitude Ss;
R ' wave-amplitude calculates sub-device, the some f (r ') of search amplitude maximum in [P (k), stop] interval range, and calculate r ' wave amplitude r ' s;
Slope meter operator device, the slope of calculating QRS ripple; And
The sub-device of interval calculation, the interval of calculating adjacent two R ripples.
This QRS waveform attitude recognition device is discerned QRS waveform attitude according to the result of form recognition device.
This QRS waveform attitude recognition device judgement QRS ripple incisura and QRS ripple are slightly blunt.
Adopt technical scheme of the present invention, fully by identifying for the morphological characteristic that is derived from doctor's thinking and empirical QRS wave group, realized the effect of the ECG feature identification accuracy rate that realized reaching higher with less workload, for the application of ECG diagnostic apparatus provides good basis.
Description of drawings
The above and other feature of the present invention, character and advantage will be by becoming more obvious below in conjunction with accompanying drawing to the description of embodiment, and in the accompanying drawings, identical Reference numeral is represented identical feature all the time, wherein:
Fig. 1 is the wave character of typical ECG;
Fig. 2 is the morphological characteristic of ECG;
Fig. 3 is the process block diagram according to the ECG of being used for identification according to the present invention and sorting technique;
Fig. 4 is the functional block diagram according to the ECG of being used for identification according to the present invention and sorting device.
The specific embodiment
Because existing algorithm computation amount is huge, can't on equipment, be applied, so the present invention's experience of being intended to fully to pay attention to doctor's thinking process and absorb the doctor, seeks solution route aspect the computational complexity reducing.
In ECG, with regard to QRS waveform attitude, common have 20 surplus kind.So-called different form is meant to have different compositions in the QRS ripple.For example the QRS ripple has only downward ripple, so just is called the QS ripple; In addition, identical composition also has the different forms of expression in different ECG, and medically, q, r, the s with small letter represents the less relatively composition of amplitude in the QRS ripple usually, as shown in Figure 2.
For example at qRs, rS and QRS ripple incisura (so-called incisura is meant that the turning point more than 2 or 2 appears in a waveform in the same side of reference levels line), it is as follows to detect step:
The first, actually or decide the big S ripple of the big R ripple of this waveform according to the main peak direction of QRS ripple.If the main peak direction for just, then is big R ripple; Otherwise, if the main peak direction then is big S ripple for negative;
The second, if big R ripple is then sought turning point respectively in two intervals of [start, P (k)] and [P (k), stop], if big S ripple is not then carried out this step and following step;
The 3rd, after finding turning point, check that whether this point is positioned at the same side of the reference levels line of QRS ripple with main peak.With first interval is example, if this below the reference levels line, this point is exactly the Q point so, otherwise is exactly incisura.Equally, can determine second form in the interval.
Morphological characteristic and Time-Frequency Analysis Method respectively have suitable part, but lack specializing in, discussing and analyzing at form up to now.The template of experienced doctor in memoriter can judge rapidly what disease ECG has reflected, be actually one stratified, by thick and thin comprehensive and analytic process.The doctor is based on the ECG pattern of known exception, and they are compared, contrast with the ECG that obtained at that time, here do not have numerical computations or date processing under the ordinary meaning, be difficult to split into small logic step, but comprehensive, macroscopic feature identification and matching process:
The pan electrocardiographic recorder, feature wave modes such as location P ripple, QRS ripple and T ripple;
Measure P-P or R-R at interval;
Observe the various forms such as slope, amplitude and flex point of waveform;
Or compare with divider;
Abnormal wave and individual features wave mode are compared, reach a conclusion.
Based on above-mentioned judgement principle and process, the invention provides following scheme and realize effective ECG feature identification with lower amount of calculation.
With reference to figure 3, it has disclosed the process block diagram of the method for the ECG of being used for identification of the present invention and classification.This method is at first obtained QRS wave group formation R (k), wherein comprised each QRS ripple starting point and terminal point [start, stop] with and R ripple position P (k), based on the morphological characteristic of described information retrieval QRS ripple, comprise following step afterwards:
102. calculate R ripple, Q ripple and S wave amplitude respectively.With reference to shown in Figure 3, this step 102 further comprises following plurality of processes:
120. calculate R wave amplitude Rs;
122. the some f (q) of search amplitude minimum in [start, P (k)] interval range, and calculate Q wave-amplitude Qs;
124. the some f (s) of search amplitude minimum in [P (k), stop] interval range, and calculate S wave-amplitude Ss.
126. the some f (r ') of search amplitude maximum in [P (k), stop] interval range, and calculate r ' wave amplitude r ' s;
128. slope meter operator step, the slope of calculating QRS ripple; And
129. the interval calculation substep, the interval of calculating adjacent two R ripples.
104., R ripple, Q ripple and S ripple are discerned according to R ripple, Q ripple and the S wave amplitude that the waveform calculation procedure is calculated.With reference to shown in Figure 3, this step 104 further comprises following substep:
R ripple recognin step 140, if Rs＞0, and Rs 〉=max (Qs Ss), then is judged to be big R ripple; If Rs＞0, (Qs Ss), then is little r ripple but do not satisfy Rs 〉=max; If Rs＜0 then directly be judged to be the QS ripple;
Q ripple recognin step 142 is judged to be little q ripple or big Q ripple; Otherwise think and do not have the Q ripple on the form; And
S ripple recognin step 144 is judged to be little s ripple or big S ripple; Judge whether there is r '; Otherwise think and do not have the S ripple.
106. the morphological characteristic of identification QRS ripple.The morphological characteristic of QRS ripple comprises multiple, and these all are the known contents of those of skill in the art.And,, also be the important evidence of in doctor's artificial judgment, being utilized for the identification of the morphological characteristic of QRS ripple.In the existing automatic identification technology,, therefore make basis for estimation imperfect, and accuracy is lower owing to do not use these morphological characteristics.The present invention has formally added the identification factor of these morphological characteristics in the process of QRS ripple identification automatically, make accuracy rate greatly improve.According to an example, the step of the morphological characteristic of this identification QRS ripple comprises based on the flex point quantity of waveform in the predetermined interval judging whether there is QRS ripple incisura.According to an embodiment, this step 106 comprises search [f (q), f (s)] all flex points in interval, has QRS ripple incisura if its quantity more than or equal to 2, is then thought, otherwise thinks and do not have QRS ripple incisura.According to another example, the step of the morphological characteristic of this identification QRS ripple comprises based on the extreme point of waveform in the predetermined interval judges that whether the QRS ripple is for slightly blunt; And, if all extreme points are followed successively by maximum point and minimum point occurs at interval, a pair of maximum point and minimum are called one group, if the group number thinks so that greater than 2 this QRS ripple is sawtooth pattern ripple (a F ripple).
With reference to figure 4, corresponding with method shown in Figure 3, it has disclosed the structured flowchart of the equipment of the ECG of being used for identification of the present invention and classification.This method is at first obtained QRS wave group formation R (k), wherein comprised each QRS ripple starting point and terminal point [start, stop] with and R ripple position P (k), based on the morphological characteristic of information retrieval QRS ripple, this equipment comprises afterwards:
Waveform accountant 202 calculates R ripple, Q ripple and S wave amplitude respectively.With reference to shown in Figure 4, this waveform accountant 202 further comprises following several parts:
The R wave-amplitude calculates sub-device 220, calculates R wave amplitude Rs;
The Q wave-amplitude calculates sub-device 222, the some f (q) of search amplitude minimum in [start, P (k)] interval range, and calculate Q wave-amplitude Qs;
The S wave-amplitude calculates sub-device 224, the some f (s) of search amplitude minimum in [P (k), stop] interval range, and calculate S wave-amplitude Ss;
R ' wave-amplitude calculates sub-device 226, the some f (r ') of search amplitude maximum in [P (k), stop] interval range, and calculate r ' wave amplitude r ' s;
Slope meter operator device 228, the slope of calculating QRS ripple; And
The sub-device 229 of interval calculation, the interval of calculating adjacent two R ripples.
Form recognition device 204. is discerned R ripple, Q ripple and S ripple according to R ripple, Q ripple and S wave amplitude that waveform accountant 202 calculates.With reference to shown in Figure 4, this form recognition device 204 further comprises following parts:
R ripple recognin device 240, if Rs＞0, and Rs 〉=max (Qs Ss), then is judged to be big R ripple; If Rs＞0, (Qs Ss), then is little r ripple but do not satisfy Rs 〉=max; If Rs＜0 then directly be judged to be the QS ripple;
Q ripple recognin device 242 is judged to be little q ripple or big Q ripple; Otherwise think and do not have the Q ripple on the form; And
S ripple recognin device 244 is judged to be little s ripple or big S ripple; Judge whether there is r '; Otherwise think and do not have the S ripple;
QRS waveform attitude recognition device 206, the morphological characteristic of identification QRS ripple.The morphological characteristic of QRS ripple comprises multiple, and these all are the known contents of those of skill in the art.And,, also be the important evidence of in doctor's artificial judgment, being utilized for the identification of the morphological characteristic of QRS ripple.In the existing automatic identification technology,, therefore make basis for estimation imperfect, and accuracy is lower owing to do not use these morphological characteristics.The present invention has formally added the identification factor of these morphological characteristics in the process of QRS ripple identification automatically, make accuracy rate greatly improve.According to an embodiment, the step of the morphological characteristic of this identification QRS ripple comprises based on the flex point quantity of waveform in the predetermined interval judging whether there is QRS ripple incisura.According to an embodiment, this QRS waveform attitude recognition device 206 comprises search [f (q), f (s)] all flex points in interval, has QRS ripple incisura if its quantity more than or equal to 2, is then thought, otherwise thinks and do not have QRS ripple incisura.According to another embodiment, the step of the morphological characteristic of this identification QRS ripple comprises based on the extreme point of waveform in the predetermined interval judges that whether the QRS ripple is for slightly blunt; And, if all extreme points are followed successively by maximum point and minimum point occurs at interval, a pair of maximum point and minimum are called one group, if the group number thinks so that greater than 2 this QRS ripple is sawtooth pattern ripple (a F ripple).
According to above-mentioned characteristic recognition method, can extract the morphological characteristic parameter preferably, for the arrhythmia decision rule that has added the morphological characteristic parameter provides necessary information.Illustrate, for premature ventricular beat:
1) QRS ripple 〉=0.10s;
2) the R-times normal R-R in R-R interval=2 interval early; Be that compensatory interval is complete;
3) V1 leads and is rSR ', RS R ' and Rsr ', and R (or r) wave amplitude on the R on the left side (or r) wave amplitude＞the right; Or V6 leads and is rS shape;
4) morphological parameters:
1) QRS ripple deformity;
2) ST slope over 10 and QRS ripple master phase of wave are anti-;
3) T ripple direction and QRS ripple master phase of wave are anti-.
As an expansion of ECG identification of the present invention and sorting technique and equipment, can also add the part of P ripple identification.In current electrocardiogram was analyzed and researched automatically, very little, the easier disturbed P ripple of amplitude existed the problem of location difficulty and easy erroneous judgement, few, the test data disunity of while test specimens given figure, and its automatic identification is the particularly work of difficulty always.
Seek rule, training study, predict it is the basic ideas of discerning for the P ripple the present invention from observed data.Clap on the basis of note at the MIT/BIH data base QRS ripple heart earlier, each P wave-wave peak dot of sample is confirmed, form the P wave datum that sign is annotated by the expert.Use SVM (SVM) to learn automatically and discern then.On sample was selected, the data of previous section (such as 2/3rds) of using the MIT/BIH data sample that note crosses were as training sample, and are remaining as test sample book.With each P wave-wave peak dot is the center, and the intercepting Wave data is as the positive sample of training usefulness; In addition with three, six points before and after the P wave-wave peak be the center data intercept as negative sample, each MIT/BIH data sample can produce about up to ten thousand and train waveform like this, and test.For complexity that reduces algorithm and the problem that overcame training, before training, use hierarchical clustering to having carried out screening sample.At last, use the algorithm after the SVM training that test sample book is discerned, use multinomial, radially base and Sigmoid three class kernel functions compare, and seek optimum P ripple recognizer.
Discover,,, in the discrimination that forms more than 90%, also can cause higher misdiagnosis rate because the feature of P ripple own is not obvious though original waveform data can characterize complete P ripple; By using filtering to strengthen, get methods such as characteristic point, give prominence to the P wave characteristic, reduced the misdiagnosis rate of SVM algorithm.
Adopt technical scheme of the present invention, fully by identifying for the morphological characteristic that is derived from doctor's thinking and empirical QRS wave group, realized the effect of the ECG feature identification accuracy rate that realized reaching higher with less workload, for the application of ECG diagnostic apparatus provides good basis.
Those skilled in the art will appreciate that described each step of previous embodiment can realize by computer hardware, computer software or both combinations.In order to clearly demonstrate the interchangeability between hardware and software, as various illustrative assemblies, block diagram, module, circuit and the step 1 according to its functional elaboration.The design of these functional specific application systems that actually realize depending on that whole system adopts as hardware or software.The technical staff can recognize the interactivity of hardware and software in these cases, and how to realize the described function of each application-specific best.The technical staff may be realizing described function for the different mode of each application-specific, but this realization should not be interpreted as causing and deviates from scope of the present invention.
The realization of the various steps of describing in conjunction with embodiment as described herein or carry out and to use: general processor, digital signal processor (DSP), special IC (ASIC), field programmable gate array (FPGA) or other PLD, discrete gate or transistor logic, discrete hardware components or for carrying out the combination in any that function described here designs.General processor may be a microprocessor, and processor can be processor, controller, microcontroller or the state machine of any routine.Processor also may realize with the combination of computing equipment, as, the combination of DSP and microprocessor, a plurality of microprocessor, in conjunction with one or more microprocessors of DSP kernel or other this configuration arbitrarily.
Although more than described preferred embodiment of the present invention, the present invention is not limited only to this.The those skilled in the art of this area can carry out various variations and change on basis described above.Do not break away from the various changes of invention spirit and change and all should drop within protection scope of the present invention.The protection domain of invention is limited by appending claims.
Claims (10)
1. a method that is used for electrocardiogram identification and classification is characterized in that, this method obtain each QRS ripple starting point and terminal point [start, stop] with and R ripple position P (k), obtain each QRS ripple slope thus, Q ripple, R ripple and S wave amplitude and direction; The interval of adjacent two R ripples; Shape mode such as the thick blunt QRS ripple of QRS ripple incisura and QRS ripple; Wherein, described method comprises:
The waveform calculation procedure is calculated R ripple, Q ripple and S wave amplitude respectively, the slope of QRS ripple, the interval of adjacent two R ripples;
The form identification step according to R ripple, Q ripple and the S wave amplitude that the waveform calculation procedure is calculated, is discerned R ripple, Q ripple and S ripple;
QRS waveform attitude identification step, the morphological characteristic of identification QRS ripple.
2. the method for claim 1 is characterized in that, described waveform calculation procedure comprises:
The R wave-amplitude calculates substep, calculates R wave amplitude Rs;
The Q wave-amplitude calculates substep, the some f (q) of search amplitude minimum in [start, P (k)] interval range, and calculate Q wave-amplitude Qs;
The S wave-amplitude calculates substep, the some f (s) of search amplitude minimum in [P (k), stop] interval range, and calculate S wave-amplitude Ss;
R ' wave-amplitude calculates substep, the some f (r ') of search amplitude maximum in [P (k), stop] interval range, and calculate r ' wave amplitude r ' s;
Slope meter operator step, the slope of calculating QRS ripple; And
The interval calculation substep, the interval of calculating adjacent two R ripples.
3. method as claimed in claim 2 is characterized in that, according to the result of described form identification step QRS waveform attitude is discerned.
4. the method for claim 1 is characterized in that, described QRS waveform attitude identification step is judged QRS ripple incisura.
5. the method for claim 1 is characterized in that, described QRS waveform attitude identification step judges that the QRS ripple is slightly blunt.
6. an equipment that is used for electrocardiogram identification and classification is characterized in that, this equipment obtain each QRS ripple starting point and terminal point [start, stop] with and R ripple position P (k), obtain each QRS ripple slope thus, Q ripple, R ripple and S wave amplitude and direction; The interval of adjacent two R ripples; Shape mode such as the thick blunt QRS ripple of QRS ripple incisura and QRS ripple; Wherein, described equipment comprises:
The waveform accountant calculates R ripple, Q ripple and S wave amplitude respectively, the slope of QRS ripple, the interval of adjacent two R ripples;
The form recognition device according to R ripple, Q ripple and the S wave amplitude that the waveform accountant calculates, is discerned R ripple, Q ripple and S ripple;
QRS waveform attitude recognition device, the morphological characteristic of identification QRS ripple.
7. equipment as claimed in claim 6 is characterized in that, described waveform accountant comprises:
The R wave-amplitude calculates sub-device, calculates R wave amplitude Rs;
The Q wave-amplitude calculates sub-device, the some f (q) of search amplitude minimum in [start, P (k)] interval range, and calculate Q wave-amplitude Qs;
The S wave-amplitude calculates sub-device, the some f (s) of search amplitude minimum in [P (k), stop] interval range, and calculate S wave-amplitude Ss;
R ' wave-amplitude calculates sub-device, the some f (r ') of search amplitude maximum in [P (k), stop] interval range, and calculate r ' wave amplitude r ' s;
Slope meter operator device, the slope of calculating QRS ripple; And
The sub-device of interval calculation, the interval of calculating adjacent two R ripples.
8. equipment as claimed in claim 7 is characterized in that, described QRS waveform attitude recognition device is discerned QRS waveform attitude according to the result of described form identification.
9. equipment as claimed in claim 6 is characterized in that, described QRS waveform attitude recognition device is judged QRS ripple incisura.
10. method as claimed in claim 6 is characterized in that, described QRS waveform attitude recognition device judges that the QRS ripple is slightly blunt.
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| CN101766484B (en) * | 2010-01-18 | 2011-09-07 | 董军 | Method and equipment for identification and classification of electrocardiogram |
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| CN104783786A (en) * | 2014-01-20 | 2015-07-22 | 中国科学院苏州纳米技术与纳米仿生研究所 | Feature recognition system and method for electrocardiogram |
| CN105411579A (en) * | 2015-12-28 | 2016-03-23 | 中科院微电子研究所昆山分所 | Electrocardiogram R wave detecting method and device |
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