阅读说明：本技术 房颤的检测方法、装置、计算机设备及存储介质 (atrial fibrillation detection method and device, computer equipment and storage medium ) 是由 康延妮 李响 贾晓雨 谢国彤 于 2019-07-31 设计创作，主要内容包括：本发明公开了房颤的检测方法、装置、计算机设备及存储介质,属于医疗数据分析领域。本发明通过对待检测心电信号进行预处理获取周期特征值数组,将周期特征值数组输入分类模型,采用迭代的方式将上一时间节点对应的一组周期特征值的输出结果与当前时间节点对应的周期特征值相加作为输入,从而记忆之前心跳的特点,获取待检测心电信号的分类结果,以便于从正常波形中区分出异常波形,提高了房颤检测的准确率。(The invention discloses a method and a device for detecting atrial fibrillation, computer equipment and a storage medium, and belongs to the field of medical data analysis. According to the method, the electrocardiosignal to be detected is preprocessed to obtain the periodic characteristic value array, the periodic characteristic value array is input into the classification model, the output result of a group of periodic characteristic values corresponding to the previous time node and the periodic characteristic value corresponding to the current time node are added in an iteration mode to serve as input, so that the characteristics of previous heartbeats are memorized, the classification result of the electrocardiosignal to be detected is obtained, the abnormal waveform is distinguished from the normal waveform conveniently, and the accuracy of atrial fibrillation detection is improved.)

1. a method for detecting atrial fibrillation, comprising the steps of:

s1, training a neural network by adopting training data to obtain a classification model;

S2, preprocessing an electrocardiosignal to be detected to obtain a period characteristic value array, dividing the period characteristic value array into a plurality of groups of period characteristic values according to time nodes, wherein each group of period characteristic values corresponds to one time node;

And S3, inputting the period characteristic value groups into the classification model, and adding the output result of the group of period characteristic values corresponding to the previous time node and the period characteristic value corresponding to the current time node by the classification model in an iteration mode to serve as input so as to obtain the classification result of the electrocardiosignal to be detected.

2. The method of claim 1, wherein each set of cycle feature values comprises:

Duration of P wave, duration of QRS complex, duration of T wave, amplitude of P wave, amplitude of QRS complex, amplitude of T wave, interval distance of RR wave, interval time difference of RR wave, and longitudinal section area of P wave.

3. The method of detecting atrial fibrillation according to claim 1, wherein the neural network comprises a plurality of layers of perceptrons, classifiers, fully-connected layers and four hidden layers; the four hidden layers are respectively: a first long short term memory network layer, a second long short term memory network layer, a third long short term memory network layer and a fourth long short term memory network layer.

4. The method of claim 3, wherein the training data comprises a plurality of time nodes and a plurality of sets of periodic eigenvalues, each time node corresponding to a set of periodic eigenvalues;

Step S1 is to train the neural network with training data to obtain a classification model, including:

inputting the training data into the multilayer perceptron, converting the training data into multi-dimensional characteristic value data corresponding to multi-dimensional time nodes by the multilayer perceptron, inputting the multi-dimensional characteristic value data into a first long and short term memory network layer for training, respectively inputting the trained sequences into a second long and short term memory network layer, a third long and short term memory network layer and a fourth long and short term memory network layer for training, calculating the time average value of each characteristic value sequence through a pooling layer of the sequences trained by the second long and short term memory network layer and dividing the time average value by the time dimension, calculating the maximum value of each characteristic value sequence through the pooling layer of the sequences trained by the second long and short term memory network layer and dividing the maximum value by the time dimension, extracting the predicted result of the dimensional characteristic value sequence corresponding to the previous time node by the third long and short term memory network layer, sending the output results of the second long and short term memory network layer, the third long and short term memory network layer and the fourth long and short term memory network layer to a full connection layer and calculating the predicted result And outputting the classification probability, and adjusting the training parameters in the neural network until the learning rate of the neural network reaches a preset condition to obtain a classification model.

5. The method for detecting atrial fibrillation according to claim 1, wherein the preset condition is that the learning rate is not reduced in at least 3 rounds of verification, and the verification is performed by using a verification set to verify the classification result of the neural network.

6. The method for detecting atrial fibrillation according to claim 1, wherein the step S2 of preprocessing the cardiac signal to be detected to obtain the array of periodic characteristic values includes:

and splitting the electrocardiosignal to be detected into a plurality of groups of period characteristic values according to each cardiac cycle.

7. The method according to claim 1, wherein the step S3 of inputting the set of periodic feature values into the classification model, and the classification model iteratively adds an output result of a set of periodic feature values corresponding to a previous time node and a periodic feature value corresponding to a current time node to obtain a classification result of the to-be-detected cardiac electrical signal includes:

And inputting the period characteristic value group into the classification model, wherein the classification model adds the output result of the group of period characteristic values corresponding to the previous time node and the period characteristic value corresponding to the current time node in an iteration mode at a hidden layer to be used as input so as to obtain the classification result of the electrocardiosignal to be detected.

8. A device for detecting atrial fibrillation, comprising:

the training unit is used for training the neural network by adopting training data to obtain a classification model;

the system comprises a preprocessing unit, a time node and a time node, wherein the preprocessing unit is used for preprocessing an electrocardiosignal to be detected to obtain a period characteristic value array, dividing the period characteristic value array into a plurality of groups of period characteristic values according to the time node, and enabling each group of period characteristic values to correspond to one time node;

And the classification unit is used for inputting the period characteristic value array into the classification model, and the classification model adds the output result of the group of period characteristic values corresponding to the previous time node and the period characteristic value corresponding to the current time node in an iteration mode to be used as input so as to obtain the classification result of the electrocardiosignal to be detected.

9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of claims 1 to 7 when executing the computer program.

10. a computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program when executed by a processor implements the steps of the method of any one of claims 1 to 7.

Technical Field

The invention relates to the field of medical data analysis, in particular to a method and a device for detecting atrial fibrillation, computer equipment and a storage medium.

Background

Atrial fibrillation (AF for short) is the most common sustained arrhythmia disease as shown in fig. 1, the incidence rate of the atrial fibrillation in people is 1% -2%, the incidence rate of the atrial fibrillation is continuously increased with the age, the incidence rate of the atrial fibrillation in people over 75 years is 10%, and the atrial fibrillation is highly related to the attack of malignant events such as cerebral apoplexy, heart failure, coronary artery disease and the like. The atrial fibrillation not only threatens the life of people, but also seriously affects the life quality, and although the problem of atrial fibrillation is serious, the atrial fibrillation brings great trouble to detection because of the sporadic nature.

From a physiological perspective, atrial fibrillation detection improves detection performance by combining atrial activity and ventricular response per beat of the heart cycle. The existing detection method mainly comprises three modes, one mode is that RR interval Markov modeling is combined with PR interval variability and P waveform state similarity measurement; one is a fuzzy logic classification method combining irregular shapes of RR intervals, P wave loss and f wave appearance; another multivariate approach based on machine learning in combination with the above-described electrocardiographic features can also provide enhanced AF detection.

However, the three atrial fibrillation detection methods generally adopt artificial knowledge to extract characteristic values and combine artificial intelligence and machine learning technology to classify, and the detection accuracy is relatively poor.

Disclosure of Invention

Aiming at the problem of poor accuracy of existing atrial fibrillation detection, a detection method and device, computer equipment and a storage medium capable of improving the accuracy of atrial fibrillation detection are provided.

In order to achieve the above object, the present invention provides a method for detecting atrial fibrillation, comprising the following steps:

s1, training a neural network by adopting training data to obtain a classification model;

S2, preprocessing an electrocardiosignal to be detected to obtain a period characteristic value array, dividing the period characteristic value array into a plurality of groups of period characteristic values according to time nodes, wherein each group of period characteristic values corresponds to one time node;

And S3, inputting the period characteristic value groups into the classification model, and adding the output result of the group of period characteristic values corresponding to the previous time node and the period characteristic value corresponding to the current time node by the classification model in an iteration mode to serve as input so as to obtain the classification result of the electrocardiosignal to be detected.

Preferably, each set of cycle characteristic values includes:

Duration of P wave, duration of QRS complex, duration of T wave, amplitude of P wave, amplitude of QRS complex, amplitude of T wave, interval distance of RR wave, interval time difference of RR wave, and longitudinal section area of P wave.

Preferably, the neural network comprises a multilayer perceptron, a classifier, a full connection layer and four hidden layers; the four hidden layers are respectively: a first long short term memory network layer, a second long short term memory network layer, a third long short term memory network layer and a fourth long short term memory network layer.

Preferably, the training data includes a plurality of time nodes and a plurality of sets of period eigenvalues, and each time node corresponds to one set of period eigenvalues;

Step S1 is to train the neural network with training data to obtain a classification model, including:

Inputting the training data into the multilayer perceptron, converting the training data into multi-dimensional characteristic value data corresponding to multi-dimensional time nodes by the multilayer perceptron, inputting the multi-dimensional characteristic value data into a first long and short term memory network layer for training, respectively inputting the trained sequences into a second long and short term memory network layer, a third long and short term memory network layer and a fourth long and short term memory network layer for training, calculating the time average value of each characteristic value sequence by the sequence trained by the second long and short term memory network layer through a pooling layer and dividing the time average value by the time dimension, calculating the maximum value of each characteristic value sequence by the sequence trained by the second long and short term memory network layer through the pooling layer and dividing the maximum value by the time dimension, extracting the predicted result of the dimensional characteristic value sequence corresponding to the previous time node by the third long and short term memory network layer, and sending the output results of the second long and short term memory network layer, the third long and short term memory network layer and the fourth long and short term memory network layer to a full connection layer and a classifier And performing calculation to output classification probability, and adjusting training parameters in the neural network until the learning rate of the neural network reaches a preset condition to obtain a classification model.

Preferably, the preset condition is that the learning rate is not reduced in at least 3 rounds of verification, and the verification is to verify the classification result of the neural network by using a verification set.

preferably, in step S2, the preprocessing the electrocardiographic signal to be detected to obtain a periodic characteristic value array includes:

And splitting the electrocardiosignal to be detected into a plurality of groups of period characteristic values according to each cardiac cycle.

preferably, in the step S3, inputting the group of the period feature values into the classification model, and the classification model adds the output result of the group of the period feature values corresponding to the previous time node and the period feature value corresponding to the current time node in an iterative manner to obtain the classification result of the to-be-detected electrocardiographic signal, which includes:

And inputting the period characteristic value group into the classification model, wherein the classification model adds the output result of the group of period characteristic values corresponding to the previous time node and the period characteristic value corresponding to the current time node in an iteration mode at a hidden layer to be used as input so as to obtain the classification result of the electrocardiosignal to be detected.

In order to achieve the above object, the present invention provides an atrial fibrillation detection apparatus, including:

the training unit is used for training the neural network by adopting training data to obtain a classification model;

The system comprises a preprocessing unit, a time node and a time node, wherein the preprocessing unit is used for preprocessing an electrocardiosignal to be detected to obtain a period characteristic value array, dividing the period characteristic value array into a plurality of groups of period characteristic values according to the time node, and enabling each group of period characteristic values to correspond to one time node;

and the classification unit is used for inputting the period characteristic value array into the classification model, and the classification model adds the output result of the group of period characteristic values corresponding to the previous time node and the period characteristic value corresponding to the current time node in an iteration mode to be used as input so as to obtain the classification result of the electrocardiosignal to be detected.

to achieve the above object, the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.

to achieve the above object, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the above method.

To achieve the above object, the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.

to achieve the above object, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the above method.

According to the method, the electrocardiosignal to be detected is preprocessed to obtain the periodic characteristic value array, the periodic characteristic value array is input into the classification model, the output result of a group of periodic characteristic values corresponding to the previous time node and the periodic characteristic value corresponding to the current time node are added in an iteration mode to serve as input, so that the characteristics of previous heartbeats are memorized, the classification result of the electrocardiosignal to be detected is obtained, the abnormal waveform is distinguished from the normal waveform conveniently, and the accuracy of atrial fibrillation detection is improved.

Drawings

FIG. 1 is a waveform diagram of atrial fibrillation;

FIG. 2 is a flowchart of a method of one embodiment of detection of atrial fibrillation according to the present invention;

FIG. 3 is a schematic diagram of a neural network architecture;

FIG. 4 is a waveform diagram of an ECG signal;

FIG. 5 is a block diagram of an embodiment of an apparatus for detecting atrial fibrillation according to the present invention;

Fig. 6 is a schematic hardware architecture diagram of an embodiment of a computer device according to the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The invention discloses a detection method and device of atrial fibrillation, computer equipment and a storage medium, which are mainly suitable for the fields of medical treatment, health care and the like, and provides the detection method of the atrial fibrillation, which can memorize heartbeats and improve the detection accuracy rate of the atrial fibrillation. According to the method, the electrocardiosignal to be detected is preprocessed to obtain the periodic characteristic value array, the periodic characteristic value array is input into the classification model, the output result of a group of periodic characteristic values corresponding to the previous time node and the periodic characteristic value corresponding to the current time node are added in an iteration mode to serve as input, so that the characteristics of previous heartbeats are memorized, the classification result of the electrocardiosignal to be detected is obtained, the abnormal waveform is distinguished from the normal waveform conveniently, and the accuracy of atrial fibrillation detection is improved.