CN109480819A - A method of atrial fibrillation is predicted using multichannel body surface ecg - Google Patents
A method of atrial fibrillation is predicted using multichannel body surface ecg Download PDFInfo
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- CN109480819A CN109480819A CN201710820237.8A CN201710820237A CN109480819A CN 109480819 A CN109480819 A CN 109480819A CN 201710820237 A CN201710820237 A CN 201710820237A CN 109480819 A CN109480819 A CN 109480819A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
Abstract
The present invention discloses a kind of method occurred using multichannel body surface ecg prediction atrial fibrillation.The present invention carries out ecg signal acquiring using body surface Electrophysiological mapping method, wavelet filtering is carried out to electrocardiosignal, positioning separation is carried out to the P wave window of filtered electrocardiosignal, use experience value decomposition method calculates synchronizing symbol entropy, and the size of atrial fibrillation possibility occurrence is inferred by the statistical property of symbol entropy.The present invention is after ablative surgery, before recurring atrial fibrillation, a possibility that carrying out classification of diseases to the atrium electric signal analysis under sinus property state, evaluate the degree and atrial fibrillation recurrence of atrial fibrillation, then select targeted therapeutic scheme to have important directive function the prognosis evaluation of patient and doctor.
Description
Technical field
The present invention discloses a kind of method occurred using multichannel body surface ecg prediction atrial fibrillation, and the invention is for patients with atrial fibrillation
Prognosis evaluation and selection have the therapeutic scheme being directed to that must have important directive function.
Background technique
Auricular fibrillation (atrial fibrillation, AF, atrial fibrillation) is that clinically the most common duration rhythm of the heart loses in the whole world
One of often, effective ecg analysis after ablative surgery lacks the prognosis situation for causing doctor that can not accurately hold patient, Wu Fazhen
It hides some dangers for the recurrence for giving subsequent aftertreatment largely and be atrial fibrillation of property, has aggravated the economy of patient significantly
The treatment cost of burden and hospital.
The data of the prognostic evaluation methods analysis of previous most of patients with atrial fibrillation are atrial tissues when atrial fibrillation occurs, and
The features such as the chaotic lower atrium electric signal of amplitude can not accurately pass through the electrocardio of analysis confusion according to existing method
A possibility that signal estimation atrial fibrillation recurrence.If can be after ablative surgery, before recurring atrial fibrillation, to the atrium under sinus property state
Electric signal analysis, so that a possibility that evaluating the degree and atrial fibrillation recurrence of atrial fibrillation, then have weight for the prognosis evaluation of patient
The directive function wanted.
In view of the above problems, the present invention proposes a kind of method occurred using multichannel body surface ecg prediction atrial fibrillation, helps to cure
A possibility that raw Accurate Prediction patient postoperative atrial fibrillation recurrence size, to targetedly select corresponding therapeutic scheme.
Summary of the invention
In view of the deficiencies of the prior art, the technical problem to be solved by the present invention is to provide one kind can accurately pass through analysis sinus
Property electrocardiosignal prediction atrial fibrillation postoperative recurrence possibility size method.
Technical solution of the present invention key step is as follows:
S1: ecg signal acquiring is carried out using body surface Electrophysiological mapping method
S2: wavelet filtering is carried out to electrocardiosignal
S3: positioning separation is carried out to the P wave window of filtered electrocardiosignal
S4: use experience value decomposition method calculates synchronizing symbol entropy
S5: the size of atrial fibrillation possibility occurrence is inferred by the statistical property of symbol entropy
As a further improvement of the present invention, S1 is specifically included: electrocardiogram acquisition module is led portable system using 128 and is acquired
Body surface ecg, sample frequency 1khz.The external electrode point spacing 3-4cm used, diameter 5.0-8.0mm, electrode plate length
About 35cm, width 2.8-3.2cm.The lead distribution schematic diagram of signal acquisition as shown in Figure 1, collected signal such as Fig. 2,
Middle Fig. 2 (a) and Fig. 2 (b) respectively indicate preoperative (the atrial fibrillation rhythm of the heart) electric signal of Clinical Follow-up patient acquisition schematic diagram and it is clinical with
Visit the acquisition schematic diagram of postoperative (sinus rhythm) electric signal of patient.
As a further improvement of the present invention, S2 is specifically included: the pretreatment of electrocardiosignal, we use bio6.8 small echo
Basic function to collected body surface ecg carried out 10 layers decomposition, body surface signal wavelet filtering result as shown in figure 3, from
Original electrocardiographicdigital, small echo first layer to the 4th layer of detail signal and the 10th layer of profile signal is shown in top to bottm respectively.
We select the 4th layer to 10 layers of detail signal and as filtered results.When sample frequency is 1Khz, wavelet filtering
Frequency band afterwards is 0.98hz-62.5hz.
As a further improvement of the present invention, S3 is specifically included: the interference in order to remove ventricle, pays close attention to atrial signal, I
Obtain the position at the peak R, later the third wave before each peak R by threshold method to the signal after wavelet filtering first
It is the terminal of the secondary P wave window, the starting point before window at 110ms for the secondary P wave window, so that P wave window be accurately positioned at peak
Mouthful, the P wave window navigated to is as shown in Figure 4.
As a further improvement of the present invention, S4 is specifically included: since body surface waveform is myocardial action potential in body surface
It is comprehensive, it is difficult to isolate independent ecg wave form phase, therefore use experience value decomposition method of the present invention separates the time-varying on P wave
Myocardial activation wave.Empirical mode decomposition is used alone in each P wave window, the signal of analysis such as Fig. 5, shown in 6, frequency range
For 0.98hz-500hz.
As a further improvement of the present invention, S5 is specifically included: extracting electrocardio by Hilbert method due to traditional
The method of phase can only obtain the stack result of each myocardium phase, therefore decompose (EMD, decomposed signal sequence using empirical value
A kind of method, original signal is decomposed into many narrow-band components by it) extract ecg wave form, the first layer P wave after decomposition is believed
Number normalization after constitute potential difference matrix, calculate synchronizing information entropy.Synchronizing symbol entropy and patient's atrial fibrillation recurrence situation are carried out
Comparison, discovery symbol entropy to the position of lead with combine it is more sensitive.When the lead for choosing different number constitutes current potential matrix
Afterwards, the symbol entropy of current potential matrix becomes larger with the increase of lead number, the symbol entropy of atrial fibrillation recurrence sample with normal group
Symbol entropy gap it is also smaller and smaller, as shown in Figure 7.By many experiments, 6-24 lead is selected to carry out the meter of symbol entropy
Calculation can effectively distinguish normal group and recurrence group.
The beneficial effects of the present invention are: the symbol entropy of P wave electrocardiosignal potential difference matrix after being decomposed by calculating EMD,
It can accurately predict the atrial fibrillation recurrence rate of patient.The raising of symbol entropy means the raising of electrocardio randomness, and characterizes atrial fibrillation
The raising of easy quivering property, to effectively instruct the recurrence probability of the reasonable targetedly aftertreatment reduction atrial fibrillation of doctor's selection.
Detailed description of the invention
Fig. 1: the lead distribution schematic diagram of signal acquisition.
Fig. 2: (a) the acquisition schematic diagram of preoperative (the atrial fibrillation rhythm of the heart) electric signal of patient.
(b) the acquisition schematic diagram of postoperative (sinus rhythm) electric signal of patient.
Fig. 3: body surface signal wavelet filtering as a result, be shown original electrocardiographicdigital respectively from top to bottom, small echo first layer to the
Four layers of detail signal and the 10th layer of profile signal.
Fig. 4: P wave window positions schematic diagram.
6 layers of decomposition example of empirical value of Fig. 5: P wave window, a are the cardiac electrical P wave window of original body surface, remaining b-g is 1-6
Layer decomposed signal, two width figures of left and right are selected from the different P wave window of the same lead.
Fig. 6: EMD decompose after potential waveform in some leads of body surface, the digital representation lead number on ordinate, abscissa
For sampling number.
Fig. 7: the result red obtained for synchronized samples entropy indicates that object finds recurrence atrial fibrillation in follow-up, and blue indicates multiple
Rule.
(a) the symbol entropy that only lead of front 8 is calculated.
(b) 14 leads before and after body surface are indicated.
(c) the front and back lead on electrode network top is indicated.
(d) whole leads are indicated, abscissa is patient's serial number.
Fig. 8: 6-24 lead is randomly choosed from 128 leads and carries out the calculating of symbol entropy, is repeated 100 times.
(a) blue indicates data source in the normal patient of postoperative recovery, and red indicates that patient has found that atrial fibrillation is multiple in follow-up
Hair.
(b) symbol entropy mean value.
(c) symbol entropy minimum value.
Fig. 9: algorithm flow chart.
Below with reference to each mode shown in the drawings, the present invention will be described in detail.But these embodiments are not intended to limit
The present invention, structure that those skilled in the art are made according to these embodiments, method or transformation functionally
It is included within the scope of protection of the present invention.
The present invention is a kind of preoperative body surface ecg analysis method of heart patient decomposed based on empirical value, utilizes 128
It leads after portable cardiac brain electric system collects preoperative (the atrial fibrillation rhythm of the heart) electric signal (as shown in Figure 2) of Clinical Follow-up patient, to this
Signal is analyzed, comprising the following steps:
S1: wavelet filtering
Its principle analysis and its application in the methods of the invention are as follows: wavelet transformation is as multiple dimensioned, the more resolutions of one kind
The analysis method of rate can automatically adjust the width of time window and frequency window according to the requirement to resolution ratio.It is exactly this adaptive
It answers characteristic to make the result of wavelet transformation in signal low frequency part frequency resolution with higher, has in signal high frequency section
There is higher temporal resolution.This shows no matter wavelet transformation all has in time domain or frequency domain and describes signal local feature
Ability.Continuous wavelet basic function is defined as: wherein a is scale factor, and b is shift factor
Continuous wavelet is defined as:
Solving the problems, such as that the preoperative body surface signal of heart patient analyzes in this, the present invention using wavelet filteration method,
With bio6.8 wavelet basis function, 10 layers of decomposition are carried out, have selected the 4th layer to 10 layers of detail signal and as filtered knot
Fruit.When sample frequency is 1Khz, the frequency band after wavelet filtering is 0.98hz-62.5hz.It is small to be illustrated in figure 3 body surface signal
Wave filter result falls down from above and original electrocardiographicdigital, small echo first layer to the 4th layer of detail signal and the 10th is shown respectively
The profile signal of layer.
The present invention is during solving the problems, such as this using effective place of wavelet transform filtering: being its part point
Analysis ability can adaptively change, and in the higher frequency band part of signal, the temporal resolution of wavelet transformation is higher, in signal
Low-frequency range part, the frequency resolution of wavelet transformation is higher.But wavelet transformation is also by the restriction of uncertainty principle, no
May the resolution ratio of infinite height be owned by time domain and frequency domain simultaneously.In signal high band, wavelet transformation selects narrow time ruler
Window is spent, wide time scale window is selected in low-frequency range.But too long window size can make signal generate energy leakage.
The positioning of S2:P wave window
Since our object observing is atrium, the purpose of P wave positioning is to remove the interference of ventricle electrical activity.We are first
The position for first obtaining the peak R by threshold method to the signal after wavelet filtering, later before each peak R at third wave crest
For the terminal of the secondary P wave window, before window at 110ms it is the starting point of the secondary P wave window, so that P wave window is accurately positioned,
S3: empirical value decomposition method
EMD is a kind of method of decomposed signal sequence, and original signal is decomposed into many narrow-band components, each component quilt by it
Referred to as intrinsic mode function (Intrinic Mode Function, IMF).
The beneficial effects of the present invention are: when empirical value decomposition is decomposed on electrocardiosignal when long, since electrocardio is with non-
Often big time variation, can not obtain accurate IMF.Therefore EMD is used alone the present invention in each signal window, analyzes
Signal frequency range be 0.98hz-500hz.After empirical value decomposes, the present invention selects the first layer signal to go to carry out the symbol of next step
Entropy calculates, and it is corresponding with patient's atrial fibrillation recurrence that practice also turns out that such selection can achieve symbol entropy.It is illustrated in figure 5 letter
6 floor of empirical value of number window decomposes example.
S4: synchronizing symbol entropy
It is original that electrocardio phase is extracted by hilbert since body surface signal is generated by intracardiac outer electrocardiosignal is comprehensive
The method of position can only obtain the stack result of each myocardium phase.Therefore for body surface entropy calculate, the present invention using
EMD extracts ecg wave form, and the method for potential difference matrix is constituted after normalization.
Here the symbol entropy calculated is the current potential matrix generated after EMD is decomposed based on body surface ecgThe time arrow for being N by lengthIt is formed, whereinIt is made of the normalization transient potential difference of each electrode,
To ψI, s(t) it carries out symbolism and is converted to S (ψI, s(t)), to reduce the subsequent calculating time
The matrix after symbolic algorithmBecome matrixWhereinIt is j for serial number
Character vector group, character number therein i.e. useful electrode number.To matrixEach vector calculate probability
The Shannon entropy of statistical matrix.
Another K is the number of non-zero probability, and defining symbol capacity ratio Cr=K/N, N is data length.
It is proposed in the present invention: assuming that current potential matrixIt include the atrium electric transmission synchronous working mould for becoming reference with antrum
Formula, then the Shannon entropy (SE) of symbolism matrix is commented the randomness (randomness of character vector group) of operating mode
Estimate, and symbol capacity ratio Cr is the assessment to operating mode total capacity (unduplicated character vector group sum).Due to's
Dimension is atrial lead number multiplied by data length (12000), therefore uses symbolism method and carry out quick SE meter to matrix
It calculates, to obtain the data analysis that corresponding evaluation index enters next step.
S5: data analysis result and discussion
Signal-to-noise ratio is too low to be checked to the data of patient before data analysis, the too many signal of noise no longer carries out next
The analysis of step.Since acquisition electrocardio scene can not accomplish to be completely independent peace and quiet, disengaging of personnel etc. can lead amplification to body surface 128
Device brings many noise jammings.Therefore the present invention has selected 11 when experiment is tested finally from 24 patient datas
As the research object of next step, wherein there is 3 patients to be determined as atrial fibrillation recurrence by follow-up, other patients are then in follow-up period
Heart rate is normal.Since P wave length of window is limited, signal length is caused to reduce very much, length used at present is 14000 and adopts
Sampling point, about 127 cardiac electrical cycles.It is illustrated in figure 6 the potential waveform after EMD is decomposed in some leads of body surface.
Synchronizing symbol entropy is compared with patient's atrial fibrillation recurrence situation first in data analysis process of the invention,
It can be found that symbol entropy to the position of lead with combine it is more sensitive.
Using method of the invention, the result such as Fig. 7 obtained using synchronized samples entropy, red indicates that object is sent out in follow-up
Atrial fibrillation is now recurred, blue indicates conversion.(a) the symbol entropy that only lead of front 8 is calculated.(b) it indicates before body surface
14 leads afterwards.(c) indicates the front and back lead on electrode network top.(d) whole leads are indicated.Abscissa is patient's serial number.
As shown in fig. 7, the symbol entropy of current potential matrix is with lead after the lead for choosing different number constitutes current potential matrix
The increase of number and become larger.The difference of the symbol entropy and the symbol entropy normally rented of the red atrial fibrillation recurrence sample indicated simultaneously
Away from also smaller and smaller.It is possible thereby to judge that lead number should have a zone of reasonableness, symbol entropy ability is calculated in range
Atrial fibrillation recurrence patient is distinguished with the patient normally restored.Although calculating 128 leads is 128 dimension current potentials very comprehensively
Poor matrix situation is too complicated, and the data time length needed is too big.It is therefore necessary to reduced lead number, used in the present invention
128 lead of method in random selection 6-24 lead progress symbol entropy calculating.Checkout result such as Fig. 8.Fig. 8 (a) indicate from
6-24 lead is randomly choosed in 128 leads and carries out the calculating of symbol entropy, is repeated 100 times, and blue indicates data source in operation
After restore normal patient, red indicates that patient has found that atrial fibrillation recurrence, Fig. 8 (b) indicate mean value in follow-up, and Fig. 8 (c) is most
Small value.
According to statistical result, repeats 30 random selection leads or repeat 100 selections, the influence to symbol entropy mean value
Variation less, has a significant impact symbol entropy minimum value.The symbol entropy average value standard deviation of atrial fibrillation recurrence patient is 3.08+
0.07, the mean value and standard deviation of the patient's symbol entropy normally restored are 2.56+0.12.
The finally obtained conclusion of the method for the present invention is: the symbol entropy of the postoperative body surface ecg difference matrix of patient can predict disease
The atrial fibrillation recurrence rate of people.The raising of symbol entropy means the raising of electrocardio randomness, and characterizes the raising of atrial fibrillation easily quivering property, this
It is consistent with conclusion obtained in early animal experiment.
It should be appreciated that although this specification is described in terms of embodiments, but not each embodiment only includes one
A independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should will say
As a whole, the technical solution in each embodiment may also be suitably combined to form those skilled in the art can for bright book
With the other embodiments of understanding.
The series of detailed descriptions listed above only for feasible embodiment of the invention specifically
Protection scope bright, that they are not intended to limit the invention, it is all without departing from equivalent implementations made by technical spirit of the present invention
Or change should all be included in the protection scope of the present invention.
Claims (6)
1. a kind of method occurred using multichannel body surface ecg prediction atrial fibrillation, which comprises the following steps:
S1: body surface Electrophysiological mapping method carries out ecg signal acquiring
S2: wavelet filtering is carried out to electrocardiosignal
S3: positioning separation is carried out to the P wave window of filtered electrocardiosignal
S4: use experience value decomposition method calculates synchronizing symbol entropy
S5: the size of atrial fibrillation possibility occurrence is inferred by the statistical property of symbol entropy.
2. a kind of method occurred using multichannel body surface ecg prediction atrial fibrillation as described in claim 1, which is characterized in that S1 tool
Body includes: the external electrode spacing 3-4cm that the body surface Electrophysiological mapping uses, and diameter 5.0-8.0mm, electrode plate length is about
35cm, width 2.8-3.2cm, electrocardiogram acquisition module are led portable cardiac brain electric system acquisition body surface ecg using 128, are adopted
Sample frequency 1khz.
3. a kind of method occurred using multichannel body surface ecg prediction atrial fibrillation as described in claim 1, which is characterized in that S2 tool
Body includes: the wavelet filtering using bio6.8 wavelet basis function, has carried out 10 layers of decomposition, has selected the 4th layer to 10 layers
Detail signal and as it is filtered as a result, filtered frequency band be 0.98hz-62.5hz.
4. a kind of method occurred using multichannel body surface ecg prediction atrial fibrillation as described in claim 1, which is characterized in that S3 tool
Body includes: the position that the P wave window positioning separation obtains the peak R by threshold method first, later the third before each peak R
It is the terminal of the secondary P wave window, the starting point before window at 110ms for the secondary P wave window, so that P be accurately positioned at a wave crest
Wave window.
5. a kind of method occurred using multichannel body surface ecg prediction atrial fibrillation as described in claim 1, which is characterized in that S4 tool
Body includes: that EMD is used alone the empirical value decomposition method in each P wave window, and the signal frequency range of analysis is 0.98hz-
500hz.After empirical value decomposes, the symbol entropy for selecting 6-24 lead of the first layer signal to carry out next step is calculated.
6. a kind of method occurred using multichannel body surface ecg prediction atrial fibrillation as described in claim 1, which is characterized in that S5 tool
Body includes: that the symbol entropy of the postoperative body surface ecg difference matrix of patient can predict the atrial fibrillation recurrence rate of patient, the raising of symbol entropy
Mean the raising of electrocardio randomness, and characterizes the raising of atrial fibrillation easily quivering property.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109907753A (en) * | 2019-04-23 | 2019-06-21 | 杭州电子科技大学 | A kind of various dimensions ECG signal intelligent diagnosis system |
| CN112603327A (en) * | 2019-12-18 | 2021-04-06 | 华为技术有限公司 | Electrocardiosignal detection method, device, terminal and storage medium |
| WO2021103796A1 (en) * | 2019-11-29 | 2021-06-03 | 京东方科技集团股份有限公司 | Electrocardiosignal processing method, electrocardiosignal processing apparatus, and electronic device |
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