阅读说明：本技术 心电数据识别装置及方法、设备、计算机可读存储介质 (Electrocardio data recognition device and method, equipment and computer readable storage medium ) 是由 欧歌 吴琼 唐大伟 杨志明 马小惠 于 2020-04-30 设计创作，主要内容包括：本发明提供一种心电数据识别装置及方法、设备、计算机可读存储介质,属于数据识别技术领域。本发明的一种心电数据识别装置,用于对心电数据的类别进行识别,其特征在于,包括：数据特征提取器、特征识别分类器,其中,所述数据特征提取器被配置为通过第一神经网络对待识别的心电数据进行特征提取,得到所述待识别的心电数据的特征数据；所述特征识别分类器被配置为通过机器学习算法对所述特征数据进行分类处理,以实现对所述心电数据的类别识别。(The invention provides an electrocardiogram data recognition device, an electrocardiogram data recognition method, electrocardiogram data recognition equipment and a computer readable storage medium, and belongs to the technical field of data recognition. An electrocardiographic data recognition apparatus according to the present invention is a device for recognizing a category of electrocardiographic data, comprising: the device comprises a data feature extractor and a feature recognition classifier, wherein the data feature extractor is configured to perform feature extraction on electrocardiogram data to be recognized through a first neural network to obtain feature data of the electrocardiogram data to be recognized; the feature recognition classifier is configured to classify the feature data through a machine learning algorithm to realize class recognition of the electrocardiographic data.)

1. An electrocardiographic data recognition apparatus for recognizing a category of electrocardiographic data, comprising: a data characteristic extractor and a characteristic identification classifier, wherein,

the data feature extractor is configured to perform feature extraction on the electrocardiogram data to be identified through a first neural network to obtain feature data of the electrocardiogram data to be identified;

the feature recognition classifier is configured to classify the feature data through a machine learning algorithm to realize class recognition of the electrocardiographic data.

2. The electrocardiogram data recognition apparatus according to claim 1, wherein the data feature extractor is configured to perform feature extraction on the electrocardiogram data to be recognized through a convolutional neural network, so as to obtain feature data of the electrocardiogram data to be recognized.

3. The electrocardiogram data recognition apparatus according to claim 2, wherein the feature recognition classifier is configured to classify the feature data outputted by the data feature extractor by a support vector machine algorithm, so as to realize class recognition of the electrocardiogram data.

4. The ecg data recognition device of claim 2, wherein prior to recognizing the ecg data class to be recognized, the data feature extractor is further configured to perform training of a first neural network using sample ecg data; wherein the content of the first and second substances,

the sample electrocardiographic data comprises electrocardiographic data of different categories;

the data feature extractor is configured to obtain a parameter group F1 of a feature extraction function of a first neural network by training the first neural network using the electrocardiographic data of different classes as an input and feature data of the electrocardiographic data of different classes as an output, and to form the first neural network based on the parameter group F1.

5. The ecg data recognition device of claim 4, wherein prior to recognizing the ecg data class to be recognized, the feature recognition classifier is further configured to train a machine learning algorithm using the feature data output by the data feature extractor; wherein the content of the first and second substances,

the characteristic data output by the data characteristic extractor comprises characteristic data output after the first neural network performs characteristic extraction on the electrocardio data of different types;

the feature recognition classifier is configured to obtain a parameter set F2 of a machine learning algorithm by training the machine learning algorithm using the feature data as input and the electrocardiographic data category as output, and the machine learning algorithm based on the parameter set F2.

6. An electrocardiogram data identification method is used for identifying the category of electrocardiogram data, and is characterized by comprising the following steps:

performing feature extraction on the electrocardiogram data to be identified through a neural network to obtain feature data of the electrocardiogram data to be identified;

and classifying the characteristic data through a machine learning algorithm to realize the category identification of the electrocardiogram data.

7. The method according to claim 6, wherein the step of extracting the characteristic of the electrocardiographic data to be recognized to obtain the characteristic data of the electrocardiographic data to be recognized comprises:

and performing feature extraction on the electrocardiogram data to be identified through a convolutional neural network to obtain feature data of the electrocardiogram data to be identified.

8. The method for recognizing the electrocardiographic data according to claim 7, further comprising training a first neural network by using sample electrocardiographic data before recognizing the category of the electrocardiographic data to be recognized; wherein the content of the first and second substances,

the sample electrocardiographic data comprises electrocardiographic data of different categories;

the step of training the first neural network by adopting the sample electrocardiogram data comprises the following steps: the electrocardiogram data of different types are used as input, the feature data of the electrocardiogram data of different types are used as output, a first neural network is trained, and a parameter group F1 of a feature extraction function of the first neural network and the first neural network formed based on the parameter group F1 are obtained.

9. The method for recognizing the electrocardiographic data according to claim 8, further comprising training a machine learning algorithm by using the feature data outputted from the data feature extractor before recognizing the category of the electrocardiographic data to be recognized; wherein the content of the first and second substances,

the characteristic data output by the data characteristic extractor comprises characteristic data output after the first neural network performs characteristic extraction on the electrocardio data of different types;

the step of training a machine learning algorithm by using the feature data output by the data feature extractor comprises: and training a machine learning algorithm by using the feature data as input and the electrocardiogram data type as output, and obtaining a parameter group F2 of the machine learning algorithm and a machine learning algorithm based on the parameter group F2.

10. An electrocardiographic data reading apparatus, comprising: at least one processor, at least one memory, and computer instructions stored in the memory, the processor being configured to implement one or more steps of the method of any one of claims 6-9 when executing the computer instructions.

11. A computer-readable storage medium having stored thereon computer instructions, which when executed by a processor, perform one or more steps of the method of any one of claims 6-9.

Technical Field

The invention belongs to the technical field of data identification, and particularly relates to an electrocardiogram data identification device, an electrocardiogram data identification method, electrocardiogram data identification equipment and a computer readable storage medium.

Background

The electrocardio monitoring is the most effective means for monitoring the heart rhythm, and whether the heart rhythm is normal or not can be found by monitoring the heart rhythm, so that the electrocardio monitoring is used for checking various symptoms such as arrhythmia and the like.

Disclosure of Invention

The invention aims to solve at least one technical problem in the prior art and provides an electrocardiogram data recognition device which can accurately recognize the category of electrocardiogram data.

The technical scheme adopted for solving the technical problem of the invention is an electrocardiogram data identification device which is used for identifying the category of electrocardiogram data and is characterized by comprising the following components: a data characteristic extractor and a characteristic identification classifier, wherein,

the data feature extractor is configured to perform feature extraction on the electrocardiogram data to be identified through a first neural network to obtain feature data of the electrocardiogram data to be identified;

the feature recognition classifier is configured to classify the feature data through a machine learning algorithm to realize class recognition of the electrocardiographic data.

Optionally, the data feature extractor is configured to perform feature extraction on the electrocardiographic data to be identified through a convolutional neural network, so as to obtain feature data of the electrocardiographic data to be identified.

Further optionally, the feature recognition classifier is configured to perform classification processing on the feature data output by the data feature extractor through a support vector machine algorithm to realize class recognition on the electrocardiographic data.

Optionally, before identifying the category of the electrocardiographic data to be identified, the data feature extractor is further configured to train a first neural network by using sample electrocardiographic data; wherein the content of the first and second substances,

the sample electrocardiographic data comprises electrocardiographic data of different categories;

the data feature extractor is configured to obtain a parameter group F1 of a feature extraction function of a first neural network by training the first neural network using the electrocardiographic data of different classes as an input and feature data of the electrocardiographic data of different classes as an output, and to form the first neural network based on the parameter group F1.

Further optionally, before the category of the electrocardiographic data to be recognized is recognized, the feature recognition classifier is further configured to train a machine learning algorithm by using feature data output by the data feature extractor; wherein the content of the first and second substances,

the characteristic data output by the data characteristic extractor comprises characteristic data output after the first neural network performs characteristic extraction on the electrocardio data of different types;

the feature recognition classifier is configured to obtain a parameter set F2 of a machine learning algorithm by training the machine learning algorithm using the feature data as input and the electrocardiographic data category as output, and the machine learning algorithm based on the parameter set F2.

Another technical solution adopted to solve the technical problem of the present invention is an electrocardiographic data recognition method for recognizing the category of electrocardiographic data, including:

performing feature extraction on the electrocardiogram data to be identified through a neural network to obtain feature data of the electrocardiogram data to be identified;

and classifying the characteristic data through a machine learning algorithm to realize the category identification of the electrocardiogram data.

Optionally, the step of performing feature extraction on the electrocardiographic data to be identified to obtain feature data of the electrocardiographic data to be identified includes:

and performing feature extraction on the electrocardiogram data to be identified through a convolutional neural network to obtain feature data of the electrocardiogram data to be identified.

Further optionally, before identifying the category of the electrocardiographic data to be identified, training a first neural network by using sample electrocardiographic data is further included; wherein the content of the first and second substances,

the sample electrocardiographic data comprises electrocardiographic data of different categories;

the step of training the first neural network by adopting the sample electrocardiogram data comprises the following steps: the electrocardiogram data of different types are used as input, the feature data of the electrocardiogram data of different types are used as output, a first neural network is trained, and a parameter group F1 of a feature extraction function of the first neural network and the first neural network formed based on the parameter group F1 are obtained.

Further optionally, before the category of the electrocardiographic data to be recognized is recognized, training a machine learning algorithm by using feature data output by the data feature extractor; wherein the content of the first and second substances,

the characteristic data output by the data characteristic extractor comprises characteristic data output after the first neural network performs characteristic extraction on the electrocardio data of different types;

the step of training a machine learning algorithm by using the feature data output by the data feature extractor comprises: and training a machine learning algorithm by using the feature data as input and the electrocardiogram data type as output, and obtaining a parameter group F2 of the machine learning algorithm and a machine learning algorithm based on the parameter group F2.

Another technical solution adopted to solve the technical problem of the present invention is an electrocardiographic data recognition apparatus, comprising: at least one processor, at least one memory, and computer instructions stored in the memory, the processor being configured to implement one or more steps of any one of the methods described above when executing the computer instructions.

Another technical solution to solve the technical problem of the present invention is a computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions, when executed by a processor, implement one or more steps of any one of the above methods.

Drawings

Fig. 1 and 2 are schematic views of an electrocardiographic data recognition apparatus according to an embodiment of the present invention;

fig. 3 is a schematic diagram of an electrocardiographic data recognition structure according to an embodiment of the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.