CN110037686A - Neural network training method and convolutional neural networks for room morning heartbeat positioning - Google Patents
Neural network training method and convolutional neural networks for room morning heartbeat positioning Download PDFInfo
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Abstract
This application involves a kind of based on the room morning heartbeat localization method and device for improving convolutional neural networks, it will acquire that ECG signal with step-length is 0.015-0.025s, length intercepts ECG signal for the sliding window of 0.4-0.8s and forms interception section, and training convolutional neural networks are come with this and form ventricular premature beat heartbeat judgment models, 0.4-0.8s just corresponds to the length of a heart beat cycle, step-length is set for 0.015-0.025s it is possible to prevente effectively from the influence of R wave, meets and judge to be conducive to reduce calculation amount while precision to ventricular premature beat heartbeat again.
Description
Technical field
The application belongs to electrocardiogram processing technology field, more particularly, to a kind of based on the room property for improving convolutional neural networks
Premature beat heartbeat localization method and device.
Background technique
Before sinoatrial node impulsion not yet arrives at ventricle, by any one of ventricle position or the ectopic rhythm of interventricular septum
Point issues the depolarization that electricity impulsion causes ventricle, referred to as Premature Ventricular Beats in advance, and abbreviation room is early.Although seeing normal healthy people
Sporadic room is early, and clinically there is no significances, but in the case where tester suffers from organic heart disease, then must tie
Clinical disease and medical history are closed, is analyzed according to different situations and gives necessary treatment.Particularly with the dynamic electrocardiogram of patient
Figure monitoring even more needs the room of picking out early and counts to its quantity.
For the automated diagnostic of room morning heartbeat, many achievements with directive significance are had already appeared both at home and abroad, commonly
Technology path is progress R wave detection first, is then diagnosed again to the heartbeat.The diagnosis accuracy of such methods not only with the heart
It is related to jump diagnosis algorithm, and is influenced by R wave detection algorithm, therefore leads to that diagnostic method is complicated, accuracy rate is low etc. and is many
Drawback.
Chinese patent literature CN108511055A discloses a kind of ventricular premature beat based on Multiple Classifier Fusion and diagnostic rule
Identifying system and method know ventricular premature beat using two classifiers of lead convolutional neural networks and recurrent neural network
Not, however this application not can avoid influence of the R wave detection to ventricular premature beat accuracy of identification, and the technical solution of this application can not obtain
Take the initial time of ventricular premature beat.
Summary of the invention
The technical problem to be solved by the present invention is to solve deficiency in the prior art, to provide a kind of without making
With filtering out room morning heartbeat under the premise of R wave detection algorithm and provide the neural network training method and convolution mind of its time of origin
Through network.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of improvement convolutional neural networks training method for room morning heartbeat positioning, comprising the following steps:
S1: the ECG signal for being known as other heartbeats of ventricular premature beat heartbeat and homogenous type is obtained；
S2: with step-length be 0.015-0.025s, length intercepts ECG signal for the sliding window of 0.4-0.8s and forms interception
Section；
S3: the importing of each ECG signal is output in convolutional neural networks, convolutional neural networks is trained, often
A ECG signal includes all interception sections, obtains the improvement convolutional neural networks positioned for ventricular premature beat heartbeat, institute
Stating and improving the output valve of convolutional neural networks is one section of successive value.
Preferably, the improvement convolutional neural networks training method for room morning heartbeat positioning of the invention, in S1 step, also
Electrocardiogram (ECG) data is pre-processed, using upper lower limiting frequency is respectively 0.1Hz, 100Hz to electrocardiogram (ECG) data when pretreatment
Fir filter is filtered.
Preferably, the improvement convolutional neural networks training method for room morning heartbeat positioning of the invention, in S1 step, if
When electrocardiosignal sample frequency is not 500Hz, then use closest interpolation method by electrocardiosignal resampling for 500Hz.
Preferably, the improvement convolutional neural networks training method for room morning heartbeat positioning of the invention, it is known that room property
The ECG signal of premature beat heartbeat is 10,000 or more, it is known that the ECG signal of other types heartbeat is 10,000 or more.
Preferably, the improvement convolutional neural networks training method for room morning heartbeat positioning of the invention is long in S2 step
Degree is 0.6s, step-length 0.02s.
Preferably, the improvement convolutional neural networks training method for room morning heartbeat positioning of the invention, convolutional Neural net
Network is by including that 8 layer networks of a classifier layer form；Wherein in 8 layer networks 1-6 layers be layer1-layer6, by one
A convolutional layer and a pond layer composition；Convolutional layer includes 5 cores in layer1, and convolution kernel size is pond in 224, layer1
Step-length and core size in change layer are 2；Layer2 convolutional layer includes 5 cores, and convolution kernel size is pond in 112, layer2
Step-length and core size in change layer are 2；Layer3 convolutional layer includes 10 cores, and convolution kernel size is pond in 100, layer3
Step-length and core size in change layer are 2；Layer4 convolutional layer includes 10 cores, and convolution kernel size is pond in 50, layer4
Step-length and core size in change layer are 2；Layer5 convolutional layer includes 10 cores, and convolution kernel size is pond in 48, layer5
Step-length and core size in change layer are 2；Layer6 convolutional layer includes 10 cores, and convolution kernel size is pond in 24, layer6
Step-length and core size in change layer are 2；The input layer number of full articulamentum layer7 and the output feature of layer6
Number is consistent, and output layer neuron number is 30, therefore 30 features are exported after calculating；Using 30 features as point
The input of the input layer of class device layer layer8, output layer neuron number are 10；The input layer of classifier
Number is 10, and output layer neuron number is 1, and output valve is [0,1] intermediate arbitrary value.
Preferably, the improvement convolutional neural networks training method for room morning heartbeat positioning of the invention, full articulamentum
The kurtosis value of the heart sequence between a upper heartbeat in the output feature and the heartbeat and electrocardiogram of layer7, degree of bias value group
At 32 features together as the input of classifier layer layer8.
Preferably, the improvement convolutional neural networks training method for room morning heartbeat positioning of the invention, convolutional Neural net
Network training algorithm are as follows: stochastic gradient descent algorithm, Adam algorithm, RMSProp algorithm, Adagrad algorithm, Adadelta algorithm,
One of Adamax algorithm.
The present invention also provides a kind of improvement convolutional neural networks for room morning heartbeat positioning, are used for the room morning heart by above-mentioned
The improvement convolutional neural networks training method training for jumping positioning obtains.
The beneficial effects of the present invention are:
The ventricular premature beat heartbeat localization method and device based on improvement convolutional neural networks of the application, will acquire electrocardiogram
Signal with step-length is 0.015-0.025s, length intercepts ECG signal for the sliding window of 0.4-0.8s and forms interception section, and with
This carrys out training convolutional neural networks and forms ventricular premature beat heartbeat judgment models, and 0.4-0.8s just corresponds to the length of a heart beat cycle
Degree, setting step-length is for 0.015-0.025s it is possible to prevente effectively from the influence of R wave, satisfaction judge the same of precision to ventricular premature beat heartbeat
When be conducive to reduce calculation amount again, and the application can also obtain the initial time of ventricular premature beat.
Detailed description of the invention
The technical solution of the application is further illustrated with reference to the accompanying drawings and examples.
Fig. 1 is the schematic diagram of the ECG signal interception and treatment process of the embodiment of the present application；
Fig. 2 is the structural schematic diagram of the improvement convolutional neural networks for room morning heartbeat positioning of the embodiment of the present application；
Fig. 3 is the application method flow chart of the improvement convolutional neural networks for room morning heartbeat positioning.
Specific embodiment
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.
It is described in detail the technical solution of the application below with reference to the accompanying drawings and in conjunction with the embodiments.
Embodiment 1
The present embodiment provides a kind of improvement convolutional neural networks training methods for room morning heartbeat positioning, as shown in figure 3,
The following steps are included:
S1: obtaining the ECG signal for being known as other heartbeats of ventricular premature beat heartbeat and homogenous type, can also be to electrocardio
Data are pre-processed, and are filtered to electrocardiogram (ECG) data using the fir filter that upper lower limiting frequency is respectively 0.1Hz, 100Hz
Wave uses closest interpolation method by electrocardiosignal resampling for 500Hz if electrocardiosignal sample frequency is not 500Hz；
S2: with step-length be 0.015-0.025s, length intercepts ECG signal for the sliding window of 0.4-0.8s and forms interception
Section, length are preferably 0.6s, and 0.4-0.8s just corresponds to the length of a heart beat cycle, and setting step-length is that 0.015-0.025s can
Effectively to avoid the influence of R wave, be conducive to reduce calculation amount again while meeting clinical precision, interception section is first from ECG signal
End or tail end start, as shown in Figure 1；
S3: the importing of each ECG signal is output in convolutional neural networks, convolutional neural networks is trained, often
A ECG signal includes all interception sections, obtains the improvement convolutional neural networks positioned for ventricular premature beat heartbeat, institute
Stating and improving the output valve of convolutional neural networks is one section of successive value.
Using at least 10,000 ventricular premature beat heartbeats and other heartbeats of at least 10,000 homogenous types as training when training
Data are trained, wherein used training algorithm can use existing any training algorithm.Training algorithm can be with
Are as follows: stochastic gradient descent algorithm, Adam algorithm, RMSProp algorithm, Adagrad algorithm, Adadelta algorithm, Adamax algorithm
Deng.Convolutional neural networks are made of 8 layer network layer 1-7s (layer1-layer7) and a classifier layer (layer8).Wherein 8
1-6 layers are layer1-layer6 in layer network, are made of a convolutional layer and a pond layer；Convolutional layer in layer1
Comprising 5 cores, convolution kernel size is step-length in 224, layer1 in the layer of pond and core size is 2；Layer2 convolutional layer
Comprising 5 cores, convolution kernel size is step-length in 112, layer2 in the layer of pond and core size is 2；Layer3 convolutional layer
Comprising 10 cores, convolution kernel size is step-length in 100, layer3 in the layer of pond and core size is 2；Layer4 convolutional layer
Comprising 10 cores, convolution kernel size is step-length in 50, layer4 in the layer of pond and core size is 2；Layer5 convolutional layer
Comprising 10 cores, convolution kernel size is step-length in 48, layer5 in the layer of pond and core size is 2；Layer6 convolutional layer
Comprising 10 cores, convolution kernel size is step-length in 24, layer6 in the layer of pond and core size is 2；Full articulamentum
The input layer number of layer7 and the output Characteristic Number of layer6 are consistent, and output layer neuron number is 30, because
This exports 30 features after calculating.Heartbeat sequence between a upper heartbeat in this 30 features and the heartbeat and electrocardiogram
The kurtosis value of column, degree of bias value form input of 32 features as classifier layer (layer8), naturally it is also possible to will directly connect entirely
Connect 30 features input classifier layer (layer8) of layer layer7.Therefore, the input layer of classifier layer (layer8)
Number it is identical as the number that full articulamentum layer7 is exported, output layer neuron number be 10.The input layer mind of classifier
It is 10 through first number, output layer neuron number is 1, and output valve is [0,1] intermediate arbitrary value.
Improvement convolutional neural networks (CNN) each clathrum of table 1 is as shown in the table, and schematic diagram is as shown in Figure 2:
Embodiment 2
The present embodiment provides a kind of improvement convolutional neural networks for room morning heartbeat positioning, are used for room by embodiment 1
The improvement convolutional neural networks training method training of early heartbeat positioning obtains.
The application method of the improvement convolutional neural networks for room morning heartbeat positioning of embodiment 2 are as follows:
By the ECG signal of unknown heartbeat type, with step-length be 0.015-0.025s, length for 0.4-0.8s sliding
Window intercepts ECG signal and forms interception section, and all interception sections are imported to the improvement convolutional Neural net for being used for room morning heartbeat positioning
In network, the output valve for improving convolutional neural networks is obtained, if output valve is greater than the midrange of successive value (when output valve is [0,1]
When for midrange being 0.5), then it is assumed that there is ventricular premature beat heartbeat, and when the starting of ventricular premature beat heartbeat in the electrocardiosignal
Carve be output valve be more than successive value midrange local maximum at the time of, if output valve without be greater than successive value midrange,
Then think there is no ventricular premature beat heartbeat in electrocardiosignal.
Local maxima minimum value is realized using the method for traversal formula search, and one section of all of ECG signal interception is cut
It takes section to import in ventricular premature beat heartbeat judgment models with the chronological order of ECG signal and exports as a result, and being formed with defeated
Result is ordinate out, and the serial number for intercepting section is that abscissa forms image, in image abscissa be in length/step-length most
Big value (peak value) is local maximum, and greater than 0.5, the time of the corresponding electrocardiosignal of local maximum is the local maximum
The starting point occurred for room morning heartbeat.
It is enlightenment with the above-mentioned desirable embodiment according to the application, through the above description, relevant staff is complete
Full various changes and amendments can be carried out in the range of without departing from this item application technical idea.The technology of this item application
Property range is not limited to the contents of the specification, it is necessary to which the technical scope thereof is determined according to the scope of the claim.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Claims (9)
1. a kind of improvement convolutional neural networks training method for room morning heartbeat positioning, which comprises the following steps:
S1: the ECG signal for being known as other heartbeats of ventricular premature beat heartbeat and homogenous type is obtained；
S2: with step-length be 0.015-0.025s, length intercepts ECG signal for the sliding window of 0.4-0.8s and forms interception section；
S3: the importing of each ECG signal being output in convolutional neural networks and is trained to convolutional neural networks, Mei Gexin
Electrical picture signal includes all interception sections, obtains the improvement convolutional neural networks positioned for ventricular premature beat heartbeat, described to change
Output valve into convolutional neural networks is one section of successive value.
2. the improvement convolutional neural networks training method according to claim 1 for room morning heartbeat positioning, feature exist
In in S1 step, also being pre-processed to electrocardiogram (ECG) data, when pretreatment, be respectively using upper lower limiting frequency to electrocardiogram (ECG) data
The fir filter of 0.1Hz, 100Hz are filtered.
3. the improvement convolutional neural networks training method according to claim 1 or 2 for room morning heartbeat positioning, feature
It is, in S1 step, if electrocardiosignal sample frequency is not 500Hz, is adopted electrocardiosignal again using closest interpolation method
Sample is 500Hz.
4. the improvement convolutional neural networks training method according to claim 1-3 for room morning heartbeat positioning,
It is characterized in that, it is known that the ECG signal of ventricular premature beat heartbeat is 10,000 or more, it is known that the heart of other types heartbeat
Electrical picture signal is 10,000 or more.
5. the improvement convolutional neural networks training method according to claim 1-4 for room morning heartbeat positioning,
It is characterized in that, length is 0.6s, step-length 0.02s in S2 step.
6. the improvement convolutional neural networks training method according to claim 1-5 for room morning heartbeat positioning,
It is characterized in that, convolutional neural networks are by including that 8 layer networks of a classifier layer form；Wherein in 8 layer networks 1-6 layers be
Layer1-layer6 is made of a convolutional layer and a pond layer；Convolutional layer includes 5 cores in layer1, and convolution kernel is big
Small is the step-length in 224, layer1 in the layer of pond and core size is 2；Layer2 convolutional layer includes 5 cores, and convolution kernel is big
Small is the step-length in 112, layer2 in the layer of pond and core size is 2；Layer3 convolutional layer includes 10 cores, and convolution kernel is big
Small is the step-length in 100, layer3 in the layer of pond and core size is 2；Layer4 convolutional layer includes 10 cores, and convolution kernel is big
Small is the step-length in 50, layer4 in the layer of pond and core size is 2；Layer5 convolutional layer includes 10 cores, and convolution kernel is big
Small is the step-length in 48, layer5 in the layer of pond and core size is 2；Layer6 convolutional layer includes 10 cores, and convolution kernel is big
Small is the step-length in 24, layer6 in the layer of pond and core size is 2；The input layer number of full articulamentum layer7
Consistent with the output Characteristic Number of layer6, output layer neuron number is 30, therefore 30 features are exported after calculating；
Using 30 features as the input of the input layer of classifier layer layer8, output layer neuron number is 10；Point
The input layer number of class device is 10, and output layer neuron number is 1, and output valve is [0,1] intermediate arbitrary value.
7. the improvement convolutional neural networks training method according to claim 6 for room morning heartbeat positioning, feature exist
In the kurtosis of the heart sequence between a upper heartbeat in the output feature and the heartbeat and electrocardiogram of full articulamentum layer7
Value, degree of bias value form 32 features together as the input of classifier layer layer8.
8. the improvement convolutional neural networks training method according to claim 1-7 for room morning heartbeat positioning,
It is characterized in that, convolutional neural networks training algorithm are as follows: stochastic gradient descent algorithm, Adam algorithm, RMSProp algorithm,
One of Adagrad algorithm, Adadelta algorithm, Adamax algorithm.
9. a kind of improvement convolutional neural networks for room morning heartbeat positioning, which is characterized in that by any one of claim 1-8 institute
The improvement convolutional neural networks training method training for room morning heartbeat positioning stated obtains.
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Application publication date: 20190723 |