CN102058407A - Method and apparatus for predicting ventricular fibrillation - Google Patents
Method and apparatus for predicting ventricular fibrillation Download PDFInfo
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- CN102058407A CN102058407A CN2011100348092A CN201110034809A CN102058407A CN 102058407 A CN102058407 A CN 102058407A CN 2011100348092 A CN2011100348092 A CN 2011100348092A CN 201110034809 A CN201110034809 A CN 201110034809A CN 102058407 A CN102058407 A CN 102058407A
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Abstract
The invention belongs to the technical field of medical equipment, and in particular relates to a method and apparatus for predicting ventricular fibrillation. The method comprises the steps of: collecting electrocardiosignals of continuous five beats; pre-treating the signals; converting the electrocardiosignal of each beat into a network via a visible graph algorithm; calculating the percentage difference between a sub-graph C and a sub-graph A in the network corresponding to the electrocardiosignal of each beat and calculating the average value of the differences of the electrocardiosignals of five beats; and if the average value is smaller than 5%, sending an alarm through a ventricular fibrillation predictor and considering that ventricular fibrillation will occur. The device comprises a data collection module, a filter module, a signal conversion module and a sub-graph analyzing module. The method and the apparatus, which are provided by the invention, can be applied to instruments such as a monitor, a defibrillator, an implanted defibrillator and the like so as to improve diagnosis and treatment of ventricular fibrillation with the type of products.
Description
Technical field
The invention belongs to the medical equipment technical field, be specifically related to a kind of ventricular fibrillation forecast method and device of being used for.
Background technology
It is 41.84 examples/100,000 people that sudden cardiac death Epidemiological study in 2009 draws Chinese sudden cardiac death (SCD) incidence rate, if with 1,300,000,000 estimations of population, China's sudden cardiac death number occupied first of the global various countries up to 54.4 ten thousand example/years.Therefore, it is crucial accurately discerning the sudden cardiac death high-risk group, and is a challenging medical difficult problem.Ventricular fibrillation is the main mechanism that causes sudden cardiac death, is the result of various malignant arrhythmia development.During ventricular fibrillation, Ventricular Rate surpasses 300 times/min, arrhythmia, and QRS width, form and amplitude variation are big, and cardiac muscle has only mixed and disorderly electrical activity, does not effectively shrink and diastole, and blood circulation stops.
Prediction mainly is a research rhythm of the heart variability (HRV) to ventricular fibrillation at present.Rhythm of the heart variability is meant that the trickle time between the cardiac cycle changes and rule.Rhythm of the heart variability has been assessed autonomic functional disorder and can have been discerned the high-risk group of cardiac sudden death.Research method to rhythm of the heart variability comprises temporal analysis, frequency domain analysis, nonlinear analysis method.Yet these methods can't effectively realize short-term forecast to ventricular fibrillation.
Summary of the invention
The object of the present invention is to provide a kind of method and apparatus that can realize short-term forecast to ventricular fibrillation.
The ventricular fibrillation forecast method that the present invention proposes, concrete steps are as follows:
1,, gathers continuous 5 electrocardiosignaies of clapping according to certain sample rate;
2, signal is carried out pretreatment, remove power frequency and disturb, myoelectricity disturbs, noise and baseline drift; Specifically comprise:
1) removing power frequency by the wave trap of 50Hz disturbs;
2) by cut-off frequency be the high pass filter of 1Hz, the filtering baseline drift;
3) by cut-off frequency be the step low-pass Butterworth filter of 30Hz, the filtering myoelectricity disturbs;
But, the electrocardiosignal of each bat is converted into network 3, by View Algorithm; Wherein, but View Algorithm be: any two numbers (width of cloth) value in the electrocardiosignal
With
, as long as have a few between numerical value A and the numerical value B
(wherein
) satisfy formula:
, then numerical value A corresponds to the node of network
Corresponding to the node of network with numerical value B
In network, link to each other.
4, calculate the difference that each claps the percentage ratio of subgraph C in electrocardiosignal institute map network and subgraph A:
, and calculate 5 and clap the subgraph C of electrocardiosignal institute map network and the meansigma methods of subgraph A percentage difference:
If
, then the chamber predictor that quivers gives the alarm, and thinks to quiver in the chamber will take place.
Wherein, subgraph C is by four points, and three lines are formed, and are star-like; Subgraph A is by four points, and three lines are formed, and are chain.
The ventricular fibrillation prediction unit that the present invention proposes comprises:
Data acquisition module, being used to obtain sampling frequency is 250Hz, time span is 5 electrocardiosignaies of clapping;
Filtration module, the power frequency and the myoelectricity that are used for the filtering electrocardiosignal disturb, and eliminate baseline drift;
The signal conversion module, this module is converted into network with the electrocardiosignal of pretreated each bat;
Subgraph analysis module, this module at first calculate five and clap continuous electrocardiosignal each claps the difference of the percentage ratio of the subgraph C of the network that electrocardiosignal generates and subgraph A
, and calculate meansigma methods
If
, then the ventricular fibrillation predictor gives the alarm, and thinks and will generation chamber quiver.
Described filtration module comprises:
1, the wave trap of 50Hz is used to remove power frequency and disturbs;
2, cut-off frequency is the high pass filter of 1Hz, is used for the filtering baseline drift;
3, cut-off frequency is the step low-pass Butterworth filter of 30Hz, is used for the filtering myoelectricity and disturbs.
This wave filter is an electrocardiosignal preprocessor conventional in the automatic defibrillator.
Described signal conversion module the electrocardiosignal of each pretreated bat is converted into network, but the method for conversion is a view approach: any two numbers (width of cloth) value in the electrocardiosignal
With
, as long as have a few between numerical value A and the numerical value B
(wherein
) satisfy formula:
, then numerical value A corresponds to the node of network
Correspond to the node of network with numerical value B
In network, link to each other.
Description of drawings
Fig. 1 is a structural representation of the present invention.
Fig. 2 is the diagram of network quadravalence subgraph A and subgraph C.
Fig. 3 is a workflow diagram of the present invention.
The specific embodiment
The ventricular fibrillation prediction unit that the present invention proposes comprises:
Data acquisition module, being used to obtain sampling frequency is 250Hz, time span is 5 electrocardiosignaies of clapping;
Filtration module, the power frequency and the myoelectricity that are used for the filtering electrocardiosignal disturb, and eliminate baseline drift;
The signal conversion module, this module is converted into network with the electrocardiosignal of pretreated each bat;
Subgraph analysis module, this module at first calculate five and clap continuous electrocardiosignal each claps the difference of the percentage ratio of the subgraph C of the network that electrocardiosignal generates and subgraph A
, and calculate meansigma methods
If
, then the ventricular fibrillation predictor gives the alarm, and thinks and will generation chamber quiver.
Execution in step was as follows when the present invention worked:
A, by data acquisition module, obtaining sampling rate is 250Hz, time span is 5 electrocardiosignaies of clapping;
B, by filtration module, this electrocardiosignal is carried out filtering, the process of filtering is as follows:
1, removing power frequency by the wave trap of 50Hz disturbs;
2, by cut-off frequency be the high pass filter of 1Hz, the filtering baseline drift;
3, by cut-off frequency be the step low-pass Butterworth filter of 30Hz, the filtering myoelectricity disturbs.This filtering is electrocardiosignal pretreatment conventional in the automatic defibrillator.
C, by the signal conversion module, the electrocardiosignal of each pretreated bat is converted into network, but the method for conversion is a view approach: any two numbers (width of cloth) value in the electrocardiosignal
With
, as long as have a few between numerical value A and the numerical value B
(wherein
) satisfy formula:
, then numerical value A corresponds to the node of network
Correspond to the node of network with numerical value B
In network, link to each other.
The difference of the percentage ratio of subgraph C and subgraph A in the network that each bat electrocardiosignal generates in D, the calculating five bat electrocardiosignaies
, and calculate meansigma methods
If
, then predictor gives the alarm, and thinks and will generation chamber quiver.
The present invention can be applied in the following product: monitor, and defibrillator, implanted defibrillator (ICD) improves the Clinics and Practices of this series products to ventricular fibrillation.
Claims (4)
1. ventricular fibrillation Forecasting Methodology is characterized in that concrete steps are:
(1), according to certain sample rate, gather continuous 5 electrocardiosignaies of clapping;
(2), signal is carried out pretreatment, the interference of removal power frequency, myoelectricity interference, noise and baseline drift;
(3) but, the employing View Algorithm, the electrocardiosignal of each bat is converted into network; But described View Algorithm is: any two numerical value in the electrocardiosignal
With
, as long as have a few between numerical value A and the numerical value B
, wherein
, satisfy formula:
, then numerical value A corresponds to the node of network
Corresponding to the node of network with numerical value B
In network, link to each other;
(4), calculate the difference that each claps the percentage ratio of subgraph C in electrocardiosignal institute map network and subgraph A:
, and calculate 5 and clap the subgraph C of electrocardiosignal institute map network and the meansigma methods of subgraph A percentage difference:
If
, then the chamber predictor that quivers gives the alarm, and thinks to quiver in the chamber will take place;
Wherein, subgraph C is made up of four points, three lines, is star-like; Subgraph A is made up of four points, three lines, is chain.
2. ventricular fibrillation prediction unit is characterized in that comprising:
Data acquisition module, being used to obtain sampling frequency is 250Hz, time span is 5 electrocardiosignaies of clapping;
Filtration module, the power frequency and the myoelectricity that are used for the filtering electrocardiosignal disturb, and eliminate baseline drift;
The signal conversion module, this module is converted into network with the electrocardiosignal of pretreated each bat;
Subgraph analysis module, this module at first calculate five and clap continuous electrocardiosignal each claps the difference of the percentage ratio of the subgraph C of the network that electrocardiosignal generates and subgraph A
, and calculate meansigma methods
If
, then the ventricular fibrillation predictor gives the alarm, and thinks and will generation chamber quiver.
3. ventricular fibrillation prediction unit according to claim 2 is characterized in that described filtration module, comprising:
(1), the wave trap of 50Hz, be used to remove power frequency and disturb;
(2), cut-off frequency is the high pass filter of 1Hz, is used for the filtering baseline drift;
(3), cut-off frequency is the step low-pass Butterworth filter of 30Hz, be used for the filtering myoelectricity and disturb.
4. ventricular fibrillation prediction unit according to claim 2 is characterized in that described signal conversion module, the electrocardiosignal of each pretreated bat is converted into network, but the method for conversion is a view approach: establish any two numerical value in the electrocardiosignal
With
, have a few between numerical value A and the numerical value B
, wherein
, satisfy formula:
, then numerical value A corresponds to the node of network
Correspond to the node of network with numerical value B
In network, link to each other.
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|---|---|---|---|
| CN201110034809.2A CN102058407B (en) | 2011-02-09 | 2011-02-09 | Method and apparatus for predicting ventricular fibrillation |
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| CN201110034809.2A CN102058407B (en) | 2011-02-09 | 2011-02-09 | Method and apparatus for predicting ventricular fibrillation |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106999342A (en) * | 2014-12-12 | 2017-08-01 | 皇家飞利浦有限公司 | The option button for automated external defibrillator (AED) is analyzed using double ECG parsers |
| CN108937920A (en) * | 2017-05-26 | 2018-12-07 | 北京大学 | A kind of ventricular fibrillation signal detecting method, system and ventricular fibrillation detection device |
| 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 |
| US11944444B2 (en) | 2018-09-06 | 2024-04-02 | Technion Research & Development Foundation Limited | Predicting ventricular fibrillation |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106999342A (en) * | 2014-12-12 | 2017-08-01 | 皇家飞利浦有限公司 | The option button for automated external defibrillator (AED) is analyzed using double ECG parsers |
| CN106999342B (en) * | 2014-12-12 | 2020-09-25 | 皇家飞利浦有限公司 | Analyzing option buttons for an Automatic External Defibrillator (AED) using a dual ECG analysis algorithm |
| CN108937920A (en) * | 2017-05-26 | 2018-12-07 | 北京大学 | A kind of ventricular fibrillation signal detecting method, system and ventricular fibrillation detection device |
| CN108937920B (en) * | 2017-05-26 | 2021-05-25 | 北京大学 | Ventricular fibrillation signal detection method and system and ventricular fibrillation detection device |
| US11944444B2 (en) | 2018-09-06 | 2024-04-02 | Technion Research & Development Foundation Limited | Predicting ventricular fibrillation |
| 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 |
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| CN102058407B (en) | 2014-03-05 |
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