CN109199361A - Ventricular fibrillation detection method, storage medium and device - Google Patents
Ventricular fibrillation detection method, storage medium and device Download PDFInfo
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- A61B5/346—Analysis of electrocardiograms
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Abstract
The present invention provides a kind of ventricular fibrillation detection method, including step 101: sequentially extracting the characteristic information of ecg signal data, meet the first Rule of judgment when continuously extracting characteristic information in the first preset duration, 103 are thened follow the steps, the first Rule of judgment is characterized distinguishing characteristics of the information in ventricular fibrillation and when there is not ventricular fibrillation；Step 103: calculating the complexity of ecg signal data in the first preset duration, judge whether complexity is greater than the first preset value, if it is, being judged to detecting ventricular fibrillation；Otherwise it is judged to that ventricular fibrillation is not detected.Based on method of the invention, it can quickly and accurately discriminate whether ventricular fibrillation occur automatically based on acquisition data, improve defibrillation success rate.
Description
Technical field
The present invention relates to field of signal processing, in particular to a kind of ventricular fibrillation detection method, storage medium and device.
Background technique
Ventricular fibrillation (Ventricular Fibrillation, VF, abbreviation ventricular fibrillation) is a kind of serious cardiovascular disease,
It is caused by the external causes such as the human bodies such as coronary heart disease, myocardial infarction reason or operation, drug poisoning.When ventricular fibrillation occurs, patient is usual
Unconscious, pulseless may threaten patient vitals in highly dangerous without blood pressure at any time.
When ventricular fibrillation occurs in patient, detecting in time and carrying out electric defibrillation is to be treated to generally acknowledged at present to patient
Important means.If can occur implementing defibrillation in ventricular fibrillation 1 minute, the success rate of defibrillation can be close to 100%, ventricular fibrillation is occurring
5 minutes defibrillations, the success rate of defibrillation drop to 33%, if there is ventricular fibrillation 10 minutes still without implementing effective defibrillation behaviour
Make, then the success rate of defibrillation is almost nil.And differentiate that ventricular fibrillation occur is to carry out the premise of defibrillation therapy, in order to improve defibrillation
Success rate, it is necessary to rapidly and accurately detect ventricular fibrillation.
Electrocardiosignal (ECG) is the foundation for detecting ventricular fibrillation.If manually checking electrocardiogram by doctor, room is judged whether it is
It quivers and implements defibrillation, at least need 5 minutes, to miss the Best Times of ventricular fibrillation treatment.
Therefore, it is badly in need of the method for exploitation detection ventricular fibrillation, realization automatically analyzes electrocardiosignal, whether judges automatically ventricular fibrillation
Occur, improves defibrillation success rate.
Summary of the invention
In view of this, the present invention provides a kind of ventricular fibrillation detection method, storage medium and device, to solve artificial detection ventricular fibrillation
There are the problem of.
The present invention provides a kind of ventricular fibrillation detection method, this method comprises:
Step 101: the characteristic information of ecg signal data is sequentially extracted, when continuously extracting feature in the first preset duration
Information meets the first Rule of judgment, thens follow the steps 103, the first Rule of judgment is characterized information in ventricular fibrillation and does not occur ventricular fibrillation
When distinguishing characteristics；
Step 103: calculating the complexity of ecg signal data in the first preset duration, judge whether complexity is greater than first
Preset value, if it is, being judged to detecting ventricular fibrillation；Otherwise it is judged to that ventricular fibrillation is not detected.
The present invention also provides a kind of non-transitory computer-readable storage medium, which is deposited
Storage instruction, instruction make processor execute the step in the application ventricular fibrillation detection method when executed by the processor.
The present invention also provides a kind of ventricular fibrillation detection devices, which is characterized in that including processor and above-mentioned non-instantaneous calculating
Machine readable storage medium storing program for executing.
The invention proposes ventricular fibrillation detection methods, whether ventricular fibrillation can occur based on ecg signal data automatic discrimination, when
The detection method be used for acquire in real time ecg signal data when, can in real time, quickly, accurately differentiate ventricular fibrillation, be timely defibrillation
Foundation is provided, defibrillation success rate is improved.
Detailed description of the invention
Fig. 1 is typical electrocardiosignal figure；
Fig. 2 is ventricular fibrillation detection method flow chart of the present invention；
Fig. 3 is one embodiment of step 103 in Fig. 2；
Fig. 4 is one embodiment of step 1031 in Fig. 3；
Fig. 5 is the first embodiment of Fig. 2；
Fig. 6 is the second embodiment of Fig. 2；
Fig. 7 is the 3rd embodiment of Fig. 2；
Fig. 8 is the fourth embodiment of Fig. 2；
Fig. 9 is the 5th embodiment of Fig. 2；
Figure 10 is the sixth embodiment of Fig. 2；
Figure 11 is one embodiment that RR interphase is extracted in Fig. 5-Figure 10；
Figure 12 is one embodiment of step 1011 in Figure 11；
Figure 13 is the structure chart of ventricular fibrillation detection device of the present invention.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, right in the following with reference to the drawings and specific embodiments
The present invention is described in detail.
The application ventricular fibrillation detection method is analyzed ecg signal data, and Fig. 1 is typical electrocardiosignal figure, is such as schemed
Shown in 1, electrocardiosignal is by three P wave, QRS wave, T wave main feature wave components.Wherein, P wave is the waveform of first appearance, instead
Atrial depolarization is reflected, the P wave duration is usually no more than 0.11 second.QRS wave reflects ventricular depolarization, by Q wave, R wave and S wave group
At the duration is generally 0.06~0.10 second.Wherein, Q wave and S wave direction are downward, and R wave direction is upward, and R wave is one
The most apparent waveform of peak value in cardiac electrical cycle.T wave reflects ventricular bipolar, after QRS wave.PR interphase is P wave starting point to QRS
The time of wave starting point, QT interphase are time of the QRS wave starting point to T wave terminal.RR interphase is between the time between two adjacent R peaks
Every or a ventricular depolarization start to start the required time to ventricular depolarization next time.
RR interphase is also referred to as heart rate interphase, the reduction formula of heart rate and RR interphase are as follows:
Heart rate=60/RR interphase
Wherein, the unit of heart rate is " beat/min ", and the unit of RR interphase is " second ", and the peak R is judged in ecg signal data
Whether there is and position the peak position R is the key that obtain RR interphase and heart rate data.
As shown in Fig. 2, a kind of ventricular fibrillation detection method of the present invention, this method comprises:
Step 101: the characteristic information of ecg signal data is sequentially extracted, when continuously extracting feature in the first preset duration
Information meets the first Rule of judgment, thens follow the steps 103, the first Rule of judgment is characterized information in ventricular fibrillation and does not occur room
Distinguishing characteristics when quivering；
In a step 101, ecg signal data includes the data of non real-time variation and the data of real-time change, wherein non-
The data of real-time change can be historical data or data acquisition equipment is acquired after fixing duration (such as 6 seconds) and automatically saved
Or the data of caching；The data of real-time change can be the data or data cached of the real-time update recorded in acquisition.
For the data of non real-time variation, the implementation of characteristic information and judging characteristic information is extracted in step 101, it can
Having been judged again after having extracted all information datas in ecg signal data, can also be believed with every extraction one or more features
Breath is just judged.
For the ecg signal data of real-time change, by the successive time of the acquisition of data, Dynamic Extraction is one or more
Characteristic information is judged again.
It should be noted that in the present invention, Dynamic Extraction characteristic information or dynamical save characteristic information or dynamic are sentenced
Disconnected characteristic information is sequentially to carry out, i.e., executes according to the sequencing of data time, and the data first acquired are first analyzed, rear to acquire
Data post analysis.
Characteristic information be by carrying out information acquired in waveforms detection identification and parameter extraction to ecg signal data,
The information has notable difference after not occurring ventricular fibrillation and ventricular fibrillation occur, one or more parameters as shown in figure 1.
Preferentially, the application selects the wave character of RR interphase and electrocardiosignal as the characteristic information in step 101, RR
Interval data and wave character are easily identified or are extracted, and have notable difference before and after ventricular fibrillation appearance.
Concussion state of the wave character to indicate ecg signal data.Wave character includes: the wave of ecg signal data
Local peaking's information of the inflection point information or ecg signal data of shape information or ecg signal data, wherein shape information can be with
It is position or the corresponding data time of waveform of waveform；When inflection point information can be position or the inflection point corresponding data of inflection point
Between；Local peaking's information can be the corresponding data time in position or local peaking of local peaking.In unit time, waveform
The more or inflection point number of number is more or local peaking's number is more, then electro-cardiologic signal waveforms more shake.
It is assumed that z (n) is the ecg signal data of step 101, then the method for calculating inflection point includes:
Step A-1: the difference dz (n) of z (n) is calculated, wherein dz (n)=z (n)-z (n-1).
Step A-2: compare the difference value dz (n) at the current time and difference value dz (n-1) of previous moment symbol whether
Identical, if the two symbol is different, i.e. dz (n) dz (n-1) < 0, then recording dz (n) or dz (n-1) is inflection point.
The setting of first preset duration need to take into account the efficiency and reliability of detection, and the setting of the first preset duration is longer, then tie
The reliability of fruit is higher, but understands the growth detection time simultaneously, detection efficiency is reduced, for example, the first preset duration may be configured as 20
Second.
Preferably, the first preset duration is 10 seconds~20 seconds.Because occurring in ventricular fibrillation 1 minute if the real-time defibrillation of energy,
The success rate of defibrillation can be close to 100%, it is contemplated that the time of subsequent calculating and operation, when the first preset duration is 10 seconds~20
When the second, it can satisfy and carry out defibrillation in 1 minute that ventricular fibrillation occurs.
Occur ventricular fibrillation when, the RR interphase and wave character of electrocardiosignal from it is normal when data or feature it is different, RR interphase
RR interval data feature of first Rule of judgment when can be according to ventricular fibrillation be arranged, the first Rule of judgment of wave character can also be according to
It is arranged according to wave character when ventricular fibrillation, the application does not limit this.For example, can refer to the numberical range or conversion of RR interphase
Heart rate range judges, waveform number in the unit time or inflection point number can be selected to judge in wave character.
On the other hand, if in the first preset duration, RR interphase or wave character do not meet the first Rule of judgment, then after
The RR interphase and wave character of continuous Detection and Extraction.
Step 103: calculating the complexity of ecg signal data in the first preset duration, judge whether complexity is greater than first
Preset value, if it is, being judged to detecting ventricular fibrillation；Otherwise it is judged to that ventricular fibrillation is not detected.
First preset value is related to the first preset duration, and the first different preset durations corresponds to the first different preset values.
Since ecg signal data is easy to mix noise or interference signal, the reliability of the analysis result of step 101 is influenced,
When i.e. characteristic information meets the first Rule of judgment, it is also possible to not occur ventricular fibrillation.
Therefore the application also adds step 103, wherein complexity reflects one after the differentiation for carrying out characteristic information
There is the rate of new model with the increase of its length in time series, is demonstrated by sequence close to random degree.Calculate electrocardiosignal
The complexity of data can identify that ecg signal data belongs to ventricular fibrillation data characteristics or random data feature, complexity are greater than
First preset value indicates that ecg signal data belongs to ventricular fibrillation data characteristics.
Ventricular fibrillation judgment method based on the application that above-mentioned steps 101 and step 103 are constituted, not only allows for electrocardiosignal
Characteristic information, and also contemplate the feature of the complexity of electrocardiogram (ECG) data, so can ensure that the application method can be quickly quasi-
Really differentiate ventricular fibrillation, improves defibrillation success rate.
In step 103, the prior art can be used in the complexity for calculating ecg signal data in the first preset duration,
Use method as shown in Figure 3:
Step 1031: ecg signal data in the first preset duration is converted into binary sequence；
Step 1032: the complexity of binary sequence is calculated using Lempel-Ziv.
Further, as shown in figure 4, step 1031 can also include:
Step 1031-1: enabling ecg signal data in the first preset duration is z (n), n=1,2 ... L, calculates the flat of z (n)
Mean value is zm；Count z (n) > zmNumber be E, z (n) < zmNumber be F；
Step 1031-2: if E+F < ɑ L, H=zm；If E+F>=ɑ L and E<F, H=β E+zm；If E+F >=ɑ L
And E >=F, then H=β F+zm, 0 < ɑ, β < 1；
Step 1031-3: comparing z (n) and H item by item, if z (n) > H, z (n) is set to 1, z (n) is otherwise set to 0.
The value of above-mentioned ɑ, β can refer to the feature of the complexity of historical data to be arranged, such as setting ɑ=0.5, β=
0.2。
When characteristic information is RR interphase and wave character, in the step 101 of Fig. 2 " sequentially extraction and judging characteristic information "
Specific implementation can first judge that RR interphase judges wave character again, or first judge that wave character judges RR interphase again, or same
When judge RR interphase and wave character.The embodiment of these three implementations is provided individually below.
Embodiment one and embodiment two
Embodiment one is as shown in Figure 5.
Step 201: sequentially extracting the RR interphase in ecg signal data, the RR interphase newly extracted is stored between new record RR
Phase sequence；
Step 202: when new record RR interval series are not sky, successively between the RR in taking-up new record RR interval series
Phase is greater than the second preset value when the RR interphase being continuously withdrawn in the first preset duration is in abnormal ranges probability, then executes step
Rapid 203；
Step 203: identification the first preset duration in ecg signal data wave character, if wave character meet it is default
Concussion state, thens follow the steps 103, is otherwise judged to being not detected ventricular fibrillation, return step 202.
Step 103: calculating the complexity of ecg signal data in the first preset duration, judge whether complexity is greater than first
Preset value, if it is, being judged to detecting ventricular fibrillation；Otherwise it is judged to being not detected ventricular fibrillation, return step 202.
Above-mentioned steps 201 and step 202 can be directed to " new record RR interval series " with paired running, two steps, step
Rapid 201 Dynamic Extraction RR interphase is simultaneously stored in " new record RR interval series ", and step 202 is directed toward " new record RR interval series ", when
When " new record RR interval series " are not empty, take out data therein and execute judgement, therefore " new record RR interval series " one
The sequence of a dynamic change, dynamic deposit and dynamic taking-up.
When there is ventricular fibrillation, the data of RR interphase can deviate the range of normal value, that is, show as exceptional value, such as the RR of taking-up
Interphase such as RRaNumerical value be exceptional value (be in abnormal ranges), possible situation is in RRaBefore, the value data taken out is equal
For normal value, then from RRaStart, continues the data that monitor subsequent takes out RR interphase in the first preset duration, if at this
Long interior major part RR interval data is in abnormal ranges, and (the RR interphase being continuously withdrawn in the first preset duration is in abnormal
Coverage probability is greater than the second preset value), then follow the steps 203.Second preset value is mainly investigated abnormal in the first preset duration
RR interval data and the during this period ratio of all RR interval datas, to ensure that RR interphase exception is not incident.Such as second
Preset value may be configured as being more than or equal to 90%.
RR interval data includes the temporal information or location information of data, therefore can be according to start-stop in the first preset duration
The temporal information or location information of RR interphase determine the start-stop of the corresponding ecg signal data of the first preset duration RR interval data
Time or start-stop position, so that it is determined that the data area that the wave character of step 203 identifies.
On the other hand, if in the first preset duration, it is default less than or equal to second that RR interphase is in abnormal ranges probability
Value then continues to take out data from new record RR interval series and executes judgement.
Presetting concussion state in step 203 can be inflection point in the first preset duration or local peaking or waveform number
More than or equal to a certain set point value.
Embodiment two is as shown in Figure 6.
Step 201-2: sequentially extracting the RR interphase in ecg signal data, and the RR interphase newly extracted is stored in RR interphase sequence
Column, the RR interphase newly extracted are the RR interphase that do not analyze in RR interval series；
Step 202-2: it when the RR interphase that do not analyze in RR interval series is not sky, is successively read between the RR not analyzed
Phase, the RR interphase that do not analyze is converted to the RR interphase analyzed after being read, as the RR continuously read in the first preset duration
Interphase is in abnormal ranges probability greater than the second preset value, thens follow the steps 203-2；
Step 203-2: identification the first preset duration in ecg signal data wave character, if wave character meet it is pre-
If the state of concussion, thens follow the steps 103, is otherwise judged to being not detected ventricular fibrillation, return step 202-2.
Step 103: calculating the complexity of ecg signal data in the first preset duration, judge whether complexity is greater than first
Preset value, if it is, detecting ventricular fibrillation；Otherwise ventricular fibrillation, return step 202-2 is not detected.
The RR interphase that do not analyze in RR interval series refers to the RR interphase not yet detected by step 202-2.Specific implementation
When, it can be set and distinguished by setting identifier, such as the identifier for the RR interval data analyzed is " 1 ", is not analyzed
The tag mark of RR interphase is " 0 "；Or record has analyzed RR interval data corresponding latest data moment or Data Position,
RR interval data after the data moment or the Data Position belongs to the RR interval data that do not analyze.
Since RR interval series are ceaselessly updated by step 201-2, RR interval series are the sequence of real-time update, such as
RR1、RR2、…RRa、RRa+1,….In step 202-2, it is assumed that the RR interphase such as RR currently readaNumerical value be exceptional value
(being in abnormal ranges), possible situation is in RRaBefore, the value data of reading is normal value, then from RRaStart, first
The data for continuing the RR interphase that monitor subsequent is read in preset duration, if major part RR interval data is in the duration
Abnormal ranges then follow the steps 203.
The implementation of step 101 is first to judge that RR interphase judges wave character again in above-mentioned Fig. 5 and Fig. 6.
Embodiment three and example IV
Embodiment three is as shown in Figure 7.
Step 301: sequentially extracting the wave character in ecg signal data, the wave character newly extracted is stored in new record
Wave character sequence；
Step 302: when new record wave character sequence is not sky, successively taking out the wave in new record wave character sequence
Shape feature thens follow the steps 303 when the wave character being continuously withdrawn in the first preset duration meets default concussion state；
Step 303: the RR interphase of ecg signal data in the first preset duration is extracted, if RR interphase is in abnormal ranges
Probability is greater than the second preset value, thens follow the steps 103, is otherwise judged to being not detected ventricular fibrillation, return step 302.
Step 103: calculating the complexity of ecg signal data in the first preset duration, judge whether complexity is greater than first
Preset value, if it is, being judged to detecting ventricular fibrillation；Otherwise it is judged to being not detected ventricular fibrillation, return step 302.
Example IV is as shown in Figure 8.
Step 301-2: sequentially extracting the wave character in ecg signal data, and the wave character newly extracted is stored in waveform
Characteristic sequence, the wave character newly extracted are the wave character that do not analyze in wave character sequence；
Step 302-2: when the wave character that do not analyze in wave character sequence is not sky, it is successively read the wave that do not analyze
Shape feature, the wave character that do not analyze are converted to the wave character analyzed after being read, when continuous in the first preset duration
The wave character of reading meets default concussion state, thens follow the steps 303-2；
Step 303-2: extracting the RR interphase of ecg signal data in the first preset duration, if RR interphase is in abnormal model
Probability is enclosed greater than the second preset value, is thened follow the steps 103, is otherwise judged to being not detected ventricular fibrillation, return step 302-2.
Step 103: calculating the complexity of ecg signal data in the first preset duration, judge whether complexity is greater than first
Preset value, if it is, being judged to detecting ventricular fibrillation；Otherwise it is judged to being not detected ventricular fibrillation, return step 302-2.
The implementation of step 101 is first to judge that wave character judges RR interphase again in above-mentioned Fig. 7 and Fig. 8.
Embodiment five and embodiment six
Embodiment five is as shown in Figure 9.
Step 401: sequentially extracting the wave character of the RR interphase and each RR interphase in ecg signal data, will newly extract
RR interphase deposit new record RR interval series；By the wave character sequence of the wave character newly extracted deposit new record；
Step 402: when the wave character sequence of new record RR interval series or new record is not sky, successively taking out new note
The RR interphase wave character corresponding with the RR interphase in RR interval series is recorded, as the RR being continuously withdrawn in the first preset duration
Interphase is in abnormal ranges probability greater than the second preset value and corresponding wave character also complies with default concussion state, then executes step
Rapid 103；
Step 103: calculating the complexity of ecg signal data in the first preset duration, judge whether complexity is greater than first
Preset value, if it is, detecting ventricular fibrillation；Otherwise ventricular fibrillation, return step 402 is not detected.
Embodiment six is as shown in Figure 10.
Step 401-2: the wave character of the RR interphase and each RR interphase in ecg signal data is sequentially extracted, will newly be mentioned
The RR interphase taken is saved to RR interval series；The wave character newly extracted is saved to wave character sequence；The waveform newly extracted
Feature is the wave character that do not analyze in wave character sequence；Between the RR interphase newly extracted is the RR not analyzed in RR interval series
Phase；
Step 402-2: when the wave character that do not analyzed in the RR interphase or wave character sequence that RR interval series are not analyzed
When being not empty, the RR interphase that do not analyze being successively read in RR interval series wave character corresponding with the RR interphase is not analyzed
RR interphase be read after be converted to the RR interphase analyzed, the wave character that do not analyze is converted to the wave analyzed after being read
Shape feature, when the RR interphase continuously read in the first preset duration is in abnormal ranges probability greater than the second preset value and correspondence
Wave character also comply with default concussion state, then follow the steps 103；
Step 103: calculating the complexity of ecg signal data in the first preset duration, judge whether complexity is greater than first
Preset value, if it is, detecting ventricular fibrillation；Otherwise ventricular fibrillation, return step 402-2 is not detected.
Furthermore invention also improves the method for extracting RR interphase, include: as shown in figure 11
Step 1011: in chronological sequence sequence extracts the peak the R moment of ecg signal data one by one, calculates the peak R newly extracted
Time interval △ t between moment and the peak the R moment of a upper record；
Step 1012: judge whether △ t is greater than refractory period duration and is less than RR interval threshold, RR interval threshold be it is current
Otherwise the presupposition multiple (such as 1.5 times or 2 times) of the maximum value of the RR interphase of record returns to step if so, executing step 1013
Rapid 1011；
If current RR interphase is sky, RR interval threshold uses default value.
Step 1013: the peak R moment for newly extracting of record, calculate new record R peak moment and upper one record the peak the R moment it
Between time interval be the RR interphase newly extracted.
It further, as shown in figure 12 include: step 1011 further include:
Step 1011-1: reading and do not analyze the top n data of ecg signal data, be denoted as z (i), i=1,2 ... N, N number of number
According to QRS wave is contained at least one, calculus of differences is executed to z (i) and obtains dz (i), the amplitude for calculating z (i) is Az (i)；
Step 1011-2: judging whether ecg signal data is initial detecting, if so, executing step 1011-3, otherwise
Execute step 1011-4；
Such as it can be by judging TM、TmAnd TAIt whether is default value to determine whether being initial detecting, or setting is just
Begin detection label.
Step 1011-3: z (i) is subdivided into K sections, wherein TMIt (j) is the difference maximum value of jth section dz (i), TmIt (j) is the
The difference minimum value of j sections of dz (i), TA(j) amplitude maximum for being jth section Az (i)；It updatesExecute step 1011-4；
Step 1011-4: positioning is all to meet dz (i) >=TMInitial position be P1 sequence, position and all meet dz (i)
≤TmInitial position be that P2 sequence if P1 and P2 are non-empty thens follow the steps 1011-5, otherwise return step 1011-
1；
Step 1011-5: judge in P1 sequence item by item each P1 with the presence or absence of matching P2, matching P2 belong to P2 sequence and with
Less than one, interval QRS wave interval between corresponding P1；If it is present continuing to judge whether deposit between P1 and matching P2
In Az (i) >=TAData point, if not, return step 1011-1, if it is, extracting Az (i) between P1 and matching P2
Maximum value corresponding to the data moment be the peak the R moment, after P1 Sequence Detection, if it is initial detecting, return step
1011-1 returns again to step 1011-1 after thening follow the steps 1011-3 if not initial detecting.
On the other hand, the initial ecg signal data collected is obtained mentioning in Fig. 2-Figure 11 by the application after filtering
The ecg signal data arrived, filtering include low-pass filtering and high-pass filtering.
Enabling initial ecg signal data is x (j), and the output of low-pass filtering is y (j), then meets between y (j) and x (j):
Y (j)=2y (j-1)-y (j-2)+x (j) -2x (j-6)+x (j-12)
The output for enabling high-pass filtering is z (j), then meets between z (j) and y (j):
Z (j)=- z (j-1)+32y (j-16)-y (j)+y (j-32)
The present inventor also provides a kind of non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium
Store instruction, which is characterized in that instruction makes processor execute the present invention as described in any of the above-described when executed by the processor
Step in ventricular fibrillation detection method.
The present inventor also provides a kind of ventricular fibrillation detection device, which is characterized in that including processor and non-wink described above
When computer readable storage medium.
As shown in figure 13, ventricular fibrillation detection device of the invention includes:
Characteristic information extracts and judgment module: the characteristic information of ecg signal data is extracted, when in the first preset duration
The continuous characteristic information that extracts meets the first Rule of judgment, then executes complexity extraction and judgment module, and the first Rule of judgment is spy
Distinguishing characteristics of the reference breath in ventricular fibrillation and when there is not ventricular fibrillation；
Complexity is extracted and judgment module: calculating the complexity of ecg signal data in the first preset duration, judges complexity
Whether degree is greater than the first preset value, if it is, determining to detect ventricular fibrillation；Otherwise it is judged to that ventricular fibrillation is not detected.
Preferentially, characteristic information includes RR interphase and wave character.
Optionally, characteristic information extracts and judgment module includes:
New record RR interval series update module: the RR interphase in ecg signal data, the RR interphase that will newly extract are extracted
It is stored in new record RR interval series；
RR interphase judgment module 1: it when new record RR interval series are not sky, successively takes out in new record RR interval series
RR interphase, when the RR interphase being continuously withdrawn in the first preset duration is in abnormal ranges probability greater than the second preset value, then
Execute wave character judgment module 1；
Wave character judgment module 1: the wave character of ecg signal data in the first preset duration of identification, if waveform is special
Sign meets default concussion state, then executes complexity and extract and judgment module, be otherwise judged to being not detected ventricular fibrillation, returns between RR
Phase sequence judgment module 1.
Optionally, characteristic information extracts and judgment module includes:
RR interval series update module: extracting the RR interphase in ecg signal data, and the RR interphase newly extracted is stored in RR
Interval series, the RR interphase newly extracted are the RR interphase that do not analyze in RR interval series；
RR interphase judgment module 2: it when the RR interphase that do not analyze in RR interval series is not sky, is successively read and does not analyze
RR interphase, the RR interphase that do not analyze are converted to the RR interphase analyzed after being read, when continuously reading in the first preset duration
RR interphase be in abnormal ranges probability greater than the second preset value, then execute wave character judgment module 2；
Wave character judgment module 2: the wave character of ecg signal data in the first preset duration of identification, if waveform is special
Sign meets default concussion state, then executes complexity and extract and judgment module, be otherwise judged to being not detected ventricular fibrillation, returns between RR
Phase sequence judgment module 2.
Optionally, characteristic information extracts and judgment module includes:
New record wave character sequence update module: the wave character in ecg signal data, the wave that will newly extract are extracted
Shape feature is stored in new record wave character sequence；
Wave character judgment module 3: when new record wave character sequence is not sky, new record wave character is successively taken out
Wave character in sequence is then executed when the wave character being continuously withdrawn in the first preset duration meets default concussion state
RR interphase judgment module 3；
RR interphase judgment module 3: the RR interphase of ecg signal data in the first preset duration is extracted, if RR interphase is in
Abnormal ranges probability is greater than the second preset value, then executes complexity extraction and judgment module, be otherwise judged to that ventricular fibrillation is not detected,
Return to wave character judgment module 3.
Optionally, characteristic information extracts and judgment module includes:
Wave character sequence update module: the wave character in ecg signal data, the wave character that will newly extract are extracted
It is stored in wave character sequence, the wave character newly extracted is the wave character that do not analyze in wave character sequence；
Wave character judgment module 4: it when the wave character that do not analyze in wave character sequence is not sky, is successively read not
The wave character of analysis, the wave character that do not analyze are converted to the wave character analyzed after being read, when first is default
The wave character continuously read in long meets default concussion state, then executes RR interphase judgment module 4；
RR interphase judgment module 4: the RR interphase of ecg signal data in the first preset duration is extracted, if RR interphase is in
Abnormal ranges probability is greater than the second preset value, then executes complexity extraction and judgment module, be otherwise judged to that ventricular fibrillation is not detected,
Return to wave character judgment module 4.
Optionally, characteristic information extracts and judgment module includes:
New record sequence update module: extracting the wave character of the RR interphase and each RR interphase in ecg signal data,
By the RR interval series of the RR interphase newly extracted deposit new record；By the wave character of the wave character newly extracted deposit new record
Sequence；
Characteristic information judgment module 1: when the wave character sequence of new record RR interval series or new record is not sky, according to
The secondary RR interphase wave character corresponding with the RR interphase taken out in new record RR interval series, connects when in the first preset duration
The continuous RR interphase taken out is in abnormal ranges probability greater than the second preset value and corresponding wave character also complies with default concussion shape
State then executes complexity extraction and judgment module.
Optionally, characteristic information extracts and judgment module includes:
Sequence update module: the wave character of the RR interphase and each RR interphase in ecg signal data is extracted, will newly be mentioned
The RR interphase taken is saved to RR interval series；The wave character newly extracted is saved to wave character sequence；The waveform newly extracted
Feature is the wave character that do not analyze in wave character sequence；Between the RR interphase newly extracted is the RR not analyzed in RR interval series
Phase；
Characteristic information judgment module 2: when what is do not analyzed in the RR interphase or wave character sequence that RR interval series are not analyzed
When wave character is not sky, the RR interphase that do not analyze being successively read in RR interval series waveform corresponding with the RR interphase is special
Sign, the RR interphase that do not analyze are converted to the RR interphase analyzed after being read, the wave character that do not analyze is converted to after being read
The wave character analyzed, when the RR interphase continuously read in the first preset duration is in abnormal ranges probability in advance greater than second
If value and corresponding wave character also comply with default concussion state, then complexity extraction and judgment module are executed.
Preferentially, the first preset duration be 10 seconds~20 seconds, the first preset value be it is related to the first preset duration, second in advance
If value >=90%.
Preferentially, the complexity of ecg signal data includes: in the first preset duration of calculating
Binary transforming module: after ecg signal data in the first preset duration is converted to binary sequence；
Complexity extraction module: the complexity of binary sequence is calculated using Lempel-Ziv.
Further, binary transforming module includes:
Statistical module: enabling ecg signal data in the first preset duration is z (n), n=1,2 ... L, calculates being averaged for z (n)
Value is zm；Count z (n) > zmNumber be E, z (n) < zmNumber be F；
Reference value computing module: if E+F < ɑ L, H=zm；If E+F>=ɑ L and E<F, H=β E+zm；If E+F
>=ɑ L and E >=F, then H=β F+zm, 0 < ɑ, β < 1；
Two-value conversion module: comparing z (n) and H item by item, if z (n) > H, z (n) is set to 1, is otherwise set to z (n)
0。
Further, ɑ=0.5, β=0.2.
Optionally, wave character includes: the shape information of ecg signal data or the inflection point information of ecg signal data,
Or local peaking's information of ecg signal data.
Optionally, the RR interphase in extraction ecg signal data includes:
The peak R extraction module: in chronological sequence sequence extracts the peak the R moment of ecg signal data one by one, calculates the R newly extracted
Time interval △ t between peak moment and the peak the R moment of a upper record；
The peak R judgment module: judging whether △ t is greater than refractory period duration and is less than RR interval threshold, and RR interval threshold is to work as
Otherwise the presupposition multiple of the maximum value of the preceding RR interphase recorded returns to the peak R and extracts mould if so, executing the peak R logging modle
Block；
The peak R logging modle: the peak the R moment that record newly extracts, when calculating new record R peak moment and the peak R of upper one record
Time interval between quarter is the RR interphase newly extracted.
Optionally, the peak R extraction module further include:
Ecg signal data read module: reading the top n data for not analyzing ecg signal data, be denoted as z (i), i=1,
2 ... N, N number of data contain at least one QRS wave, execute calculus of differences to z (i) and obtain dz (i), and the amplitude for calculating z (i) is Az
(i)；
Threshold value judgment module: judging whether ecg signal data is initial detecting, if so, after executing threshold value update module
Otherwise P1, P2 locating module again directly executes P1, P2 locating module；
Threshold value update module: z (i) is subdivided into K sections, wherein TMIt (j) is the difference maximum value of jth section dz (i), Tm(j) it is
The difference minimum value of jth section dz (i), TA(j) amplitude maximum for being jth section Az (i)；It updates
P1, P2 locating module: positioning is all to meet dz (i) >=TMInitial position be P1 sequence, position all dz (i)≤
TmInitial position be P2 sequence execute the peak R locating module if P1 sequence and P2 sequence are non-empty, otherwise return to the heart
Electrical signal data read module；
The peak R locating module: judge in P1 sequence item by item each P1 with the presence or absence of matching P2, matching P2 belong to P2 sequence and with
Less than one, interval QRS wave interval between corresponding P1；If it is present continuing to judge whether deposit between P1 and matching P2
In Az (i) >=TAData point, if it is, extract Az (i) P1 and matching P2 between maximum value corresponding to data when
Carving is the peak the R moment, after P1 Sequence Detection, if it is initial detecting, directly return ecg signal data read module, such as
Fruit is not initial detecting, then returns again to ecg signal data read module after executing threshold value update module.
Optionally, the initial ecg signal data collected is obtained into ecg signal data, filtering packet after filtering
Include low-pass filtering and high-pass filtering.
Enabling initial ecg signal data is x (j), and the output of low-pass filtering is y (j), is met between y (j) and x (j): y (j)
=2y (j-1)-y (j-2)+x (j) -2x (j-6)+x (j-12)；The output for enabling high-pass filtering is z (j), full between z (j) and y (j)
Foot: z (j)=- z (j-1)+32y (j-16)-y (j)+y (j-32).
It should be noted that the embodiment of ventricular fibrillation detection device of the present invention, the embodiment principle phase with ventricular fibrillation detection method
Together, related place can mutual reference.
The foregoing is merely illustrative of the preferred embodiments of the present invention, not to limit scope of the invention, it is all
Within the spirit and principle of technical solution of the present invention, any modification, equivalent substitution, improvement and etc. done should be included in this hair
Within bright protection scope.
Claims (11)
1. a kind of ventricular fibrillation detection method, which is characterized in that the described method includes:
Step 101: the characteristic information of ecg signal data is sequentially extracted, when continuously extracting characteristic information in the first preset duration
Meet the first Rule of judgment, then follow the steps 103, first Rule of judgment be the characteristic information in ventricular fibrillation with do not occur
Distinguishing characteristics when ventricular fibrillation；
Step 103: calculating the complexity of ecg signal data in first preset duration, judge whether the complexity is greater than
First preset value, if it is, being judged to detecting ventricular fibrillation；Otherwise it is judged to that ventricular fibrillation is not detected.
2. the method according to claim 1, wherein the characteristic information includes RR interphase and wave character, institute
State concussion state of the wave character to indicate the ecg signal data.
3. according to the method described in claim 2, it is characterized in that,
The step 101 includes:
Step 201: sequentially extracting the RR interphase in ecg signal data, the RR interphase newly extracted is stored in new record RR interphase sequence
Column；
Step 202: when the new record RR interval series are not sky, successively taking out the RR in the new record RR interval series
Interphase is greater than the second preset value when the RR interphase being continuously withdrawn in the first preset duration is in abnormal ranges probability, then executes
Step 203；
Step 203: the wave character of ecg signal data in identification first preset duration, if the wave character meets
Default concussion state, thens follow the steps 103, is otherwise judged to being not detected ventricular fibrillation, return step 202；
Or the step 101 includes:
Step 201-2: sequentially extracting the RR interphase in ecg signal data, and the RR interphase newly extracted is stored in RR interval series,
The RR interphase of the new extraction is the RR interphase that do not analyze in the RR interval series；
Step 202-2: it when the RR interphase that do not analyze in the RR interval series is not sky, is successively read between the RR not analyzed
Phase, the RR interphase that do not analyze are converted to the RR interphase analyzed after being read, when continuously reading in the first preset duration
RR interphase be in abnormal ranges probability greater than the second preset value, then follow the steps 203-2；
Step 203-2: the wave character of ecg signal data in the first preset duration of identification, if wave character meets default shake
State is swung, thens follow the steps 103, is otherwise judged to being not detected ventricular fibrillation, return step 202-2；
Or the step 101 includes:
Step 301: sequentially extracting the wave character in ecg signal data, the wave character newly extracted is stored in new record waveform
Characteristic sequence；
Step 302: when the new record wave character sequence is not sky, successively taking out in the new record wave character sequence
Wave character then follow the steps when the wave character being continuously withdrawn in the first preset duration meets default concussion state
303；
Step 303: the RR interphase of ecg signal data in first preset duration is extracted, if the RR interphase is in abnormal
Coverage probability is greater than the second preset value, thens follow the steps 103, is otherwise judged to being not detected ventricular fibrillation, return step 302；
Or the step 101 includes:
Step 301-2: sequentially extracting the wave character in ecg signal data, and the wave character of the new extraction is stored in waveform
Characteristic sequence, the wave character of the new extraction are the wave character that do not analyze in the wave character sequence；
Step 302-2: when the wave character that do not analyze in the wave character sequence is not sky, it is successively read the wave that do not analyze
Shape feature, the wave character that do not analyze is converted to the wave character analyzed after being read, when in the first preset duration
The wave character continuously read meets default concussion state, thens follow the steps 303-2；
Step 303-2: extracting the RR interphase of ecg signal data in first preset duration, if the RR interphase is in different
Normal coverage probability is greater than the second preset value, thens follow the steps 103, is otherwise judged to being not detected ventricular fibrillation, return step 302-2；
Or the step 101 includes:
Step 401: sequentially extracting the wave character of the RR interphase and each RR interphase in ecg signal data, the RR that will newly extract
The RR interval series of interphase deposit new record；The wave character newly extracted is stored in the wave character sequence of new record；
Step 402: when the wave character sequence of the new record RR interval series or the new record is not sky, successively taking out
RR interphase wave character corresponding with the RR interphase in the new record RR interval series, when continuous in the first preset duration
The RR interphase of taking-up is in abnormal ranges probability greater than the second preset value and corresponding wave character also complies with default concussion state,
Then follow the steps 103；
Or the method step 101 includes:
Step 401-2: the wave character of the RR interphase and each RR interphase in ecg signal data is sequentially extracted, by what is newly extracted
RR interphase is stored in RR interval series；The wave character newly extracted is stored in wave character sequence；The wave character newly extracted is institute
State the wave character that do not analyze in wave character sequence；Between the RR interphase newly extracted is the RR not analyzed in the RR interval series
Phase；
Step 402-2: when the waveform that do not analyzed in the RR interphase or the wave character sequence that the RR interval series are not analyzed
When feature is not sky, the RR interphase that do not analyze being successively read in RR interval series waveform corresponding with the RR interphase is special
Sign, the RR interphase that do not analyze are converted to the RR interphase analyzed after being read, the wave character that do not analyze is read
The wave character analyzed is converted to afterwards, when to be in abnormal ranges probability big for the RR interphase continuously read in the first preset duration
In the second preset value and corresponding wave character also complies with default concussion state, thens follow the steps 103.
4. according to the method described in claim 3, it is characterized in that, the 10 seconds≤first preset duration≤20 second, described second is pre-
If value >=90%.
5. the method according to claim 1, wherein described calculate electrocardiosignal number in first preset duration
According to complexity include:
Step 1031: ecg signal data in first preset duration is converted into binary sequence；
Step 1032: the complexity of the binary sequence is calculated using Lempel-Ziv.
6. according to the method described in claim 5, it is characterized in that, the step 1031 includes:
Step 1031-1: enabling ecg signal data in first preset duration is z (n), n=1,2 ... L, calculates the z (n)
Average value be zm；Count z (n) > zmNumber be E, z (n) < zmNumber be F；
Step 1031-2: if E+F < ɑ L, H=zm；If E+F>=ɑ L and E<F, H=β E+zm；If E+F >=ɑ L and E >=
F, then H=β F+zm, 0 < ɑ, β < 1；
Step 1031-3: comparing z (n) and H item by item, if z (n) > H, z (n) is set to 1, z (n) is otherwise set to 0.
7. according to the method described in claim 3, it is characterized in that, the RR interphase extracted in ecg signal data includes:
Step 1011: in chronological sequence sequence extracts the peak the R moment of the ecg signal data one by one, calculates the peak R newly extracted
Time interval △ t between moment and the peak the R moment of a upper record；
Step 1012: judging whether the △ t is greater than refractory period duration and is less than RR interval threshold, the RR interval threshold is to work as
The presupposition multiple of the maximum value of the preceding RR interphase recorded, if so, executing step 1013, otherwise return step 1011；
Step 1013: the peak R moment for newly extracting of record, calculate new record R peak moment and upper one record the peak the R moment it
Between time interval be the RR interphase newly extracted.
8. the method according to the description of claim 7 is characterized in that the step 1011 includes:
Step 1011-1: reading and do not analyze the top n data of ecg signal data, be denoted as z (i), i=1,2 ... N, N number of number
According to QRS wave is contained at least one, calculus of differences is executed to z (i) and obtains dz (i), the amplitude for calculating z (i) is Az (i)；
Step 1011-2: judging whether the ecg signal data is initial detecting, if so, holding again after executing step 1011-3
Otherwise row step 1011-4 directly executes step 1011-4；
Step 1011-3: z (i) is subdivided into K sections, wherein TMIt (j) is the difference maximum value of jth section dz (i), TmIt (j) is jth section
The difference minimum value of dz (i), TA(j) amplitude maximum for being jth section Az (i)；It updates
Step 1011-4: positioning is all to meet dz (i) >=TMInitial position be P1 sequence, position and all meet dz (i)≤Tm's
Initial position thens follow the steps 1011-5, otherwise return step if the P1 sequence and P2 sequence are non-empty for P2 sequence
1011-1；
Step 1011-5: judge in P1 sequence that each P1 belongs to the P2 sequence with the presence or absence of matching P2, the matching P2 item by item
And less than one, the interval between corresponding P1 QRS wave interval；If it is present continuing to judge in the P1 and the matching
It whether there is Az (i) >=T between P2AData point, if it is, extract Az (i) between the P1 and the matching P2
The data moment corresponding to maximum value is, if it is initial detecting, directly to return after the P1 Sequence Detection at the peak the R moment
It returns step 1011-1 and returns again to step 1011-1 after thening follow the steps 1011-3 if not initial detecting.
9. the method according to claim 1, wherein the initial ecg signal data collected is passed through low pass
The ecg signal data is obtained after filtering and high-pass filtering；
Enabling the initial ecg signal data is x (j), and the output of the low-pass filtering is y (j), then the y (j) and the x
(j) meet between: y (j)=2y (j-1)-y (j-2)+x (j) -2x (j-6)+x (j-12)；
The output for enabling the high-pass filtering is z (j), then meets between the z (j) and the y (j): z (j)=- z (j-1)+32y
(j-16)–y(j)+y(j-32)。
10. a kind of non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium store instruction is special
Sign is that described instruction makes the processor execute the room as described in any in claim 1 to 9 when executed by the processor
Step in detection method of quivering.
11. a kind of ventricular fibrillation detection device, which is characterized in that including processor and non-instantaneous computer as claimed in claim 10
Readable storage medium storing program for executing.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112914589A (en) * | 2021-03-02 | 2021-06-08 | 钦州市第二人民医院 | Multi-sleep-guidance monitoring wireless net cap device and monitoring method |
| CN113100779A (en) * | 2020-01-10 | 2021-07-13 | 深圳市理邦精密仪器股份有限公司 | Ventricular fibrillation detection method and device and monitoring equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5545186A (en) * | 1995-03-30 | 1996-08-13 | Medtronic, Inc. | Prioritized rule based method and apparatus for diagnosis and treatment of arrhythmias |
| US6490478B1 (en) * | 2000-09-25 | 2002-12-03 | Cardiac Science Inc. | System and method for complexity analysis-based cardiac tachyarrhythmia detection |
| CN1989897A (en) * | 2005-12-29 | 2007-07-04 | 深圳迈瑞生物医疗电子股份有限公司 | Ventricular fibrillation combined detecting method based on complexity |
-
2018
- 2018-07-18 CN CN201810788599.8A patent/CN109199361B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5545186A (en) * | 1995-03-30 | 1996-08-13 | Medtronic, Inc. | Prioritized rule based method and apparatus for diagnosis and treatment of arrhythmias |
| US6490478B1 (en) * | 2000-09-25 | 2002-12-03 | Cardiac Science Inc. | System and method for complexity analysis-based cardiac tachyarrhythmia detection |
| CN1989897A (en) * | 2005-12-29 | 2007-07-04 | 深圳迈瑞生物医疗电子股份有限公司 | Ventricular fibrillation combined detecting method based on complexity |
Non-Patent Citations (1)
| Title |
|---|
| XU-SHENG ZHANG ET.AL.: "Detecting Ventricular Tachycardia and Fibrillation by Complexity Measure", 《IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113100779A (en) * | 2020-01-10 | 2021-07-13 | 深圳市理邦精密仪器股份有限公司 | Ventricular fibrillation detection method and device and monitoring equipment |
| CN112914589A (en) * | 2021-03-02 | 2021-06-08 | 钦州市第二人民医院 | Multi-sleep-guidance monitoring wireless net cap device and monitoring method |
| CN112914589B (en) * | 2021-03-02 | 2023-04-18 | 钦州市第二人民医院 | Multi-sleep-guidance monitoring wireless net cap device and monitoring method |
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