WO2020061987A1 - 多导联联合心电图分类方法 - Google Patents

多导联联合心电图分类方法 Download PDF

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WO2020061987A1
WO2020061987A1 PCT/CN2018/108192 CN2018108192W WO2020061987A1 WO 2020061987 A1 WO2020061987 A1 WO 2020061987A1 CN 2018108192 W CN2018108192 W CN 2018108192W WO 2020061987 A1 WO2020061987 A1 WO 2020061987A1
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lead
channel
data
classification method
electrocardiogram
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PCT/CN2018/108192
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French (fr)
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陈彬
郭维
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深圳大学
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]

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  • the invention relates to the field of data processing, and more particularly, to a multi-lead combined electrocardiogram classification method.
  • the 12-lead synchronous recording electrocardiograph has been widely used in clinical practice.
  • the 12-lead synchronous recording electrocardiograph can simultaneously record the ECG signals of the same cardiac cycle on the 12-lead. It can identify and locate single-source or multi-source premature beats, classify arrhythmias, and diagnose indoor conduction block. Instrument has superiority.
  • the 12-lead synchronous recording electrocardiograph can simultaneously and collectively observe the waveforms of the same 12-lead cardiac cycle, which greatly improves the accuracy of various measurements and reduces the variability of existing ECG measurements.
  • the mainstream clinical ECG on the market is multi-lead multi-channel signals. Therefore, the ECG classification algorithm running on large equipment such as electrocardiographs must be combined with multi-channel signals for auxiliary detection of ECG. It is not logical, and on the one hand, it will lead to a negative impact on accuracy. Therefore, for a 12-lead simultaneous recording of multi-lead ECG signals, a normal and abnormal ECG signal can be automatically detected to improve classification accuracy. Multilead Joint ECG Classification Method.
  • the technical problem to be solved by the present invention is to provide a multi-lead combined electrocardiogram classification method capable of automatically detecting normal and abnormal ECG signals to improve the classification accuracy in response to the above-mentioned defects of the prior art.
  • the technical solution adopted by the present invention to solve its technical problems is to construct a multi-lead combined electrocardiogram classification method, including:
  • the pre-processed multi-channel electrocardiogram data is encoded into a one-channel signal, and the one-channel signal is classified.
  • the step S1 further includes:
  • step S12 the electrocardiogram data in the XML format with a sampling rate of 500 Hz is converted into the electrocardiogram data in the mat format with a sampling rate of 250 Hz.
  • the step S2 further includes:
  • the pre-processed multi-channel electrocardiogram data is regarded as eight-channel one-dimensional data and encoded by channel changes, and then the encoded data is classified by a D-LSTM network structure.
  • the D-LSTM network structure includes a deep LSTM network formed by a plurality of LSTM network units, a first fully connected layer connected to the deep LSTM network, and A second fully connected layer connected to the first fully connected layer.
  • the second fully connected layer is a SoftMax layer.
  • the pre-processed multi-channel ECG data of each lead sets the convolution kernel and pooling layer to one through the three-layer two-dimensional convolution network.
  • Another technical solution adopted by the present invention to solve its technical problem is to construct a computer-readable storage medium on which a computer program is stored, and the multi-lead combined electrocardiogram classification method is implemented when the program is executed by a processor. .
  • FIG. 1 is a method flowchart of a first embodiment of a multi-lead combined electrocardiogram classification method according to the present invention
  • FIG. 2 is a schematic structural diagram of a Multi-RNN model of a multi-lead combined electrocardiogram classification method of the present invention
  • FIG. 3 is a schematic structural diagram of a Vanilla-CNN model of a multi-lead combined electrocardiogram classification method of the present invention
  • FIG. 4 is a schematic structural diagram of a Feature-CNN model of the multi-lead combined electrocardiogram classification method of the present invention.
  • FIG. 5 is a schematic structural diagram of a Channel-CNN model of a multi-lead combined electrocardiogram classification method of the present invention.
  • FIG. 6 is a schematic structural diagram of a D-LSTM network model of the multi-lead combined electrocardiogram classification method of the present invention.
  • FIG. 7 shows the accuracy of a multi-lead combined electrocardiogram classification method according to different embodiments of the present invention.
  • the invention relates to a multi-lead combined ECG classification method, which includes: S1, preprocessing ECG data in a CCDD database; S2, encoding the pre-processed multi-channel ECG data into a channel signal, and One channel signal is classified.
  • S1 preprocessing ECG data in a CCDD database
  • S2 encoding the pre-processed multi-channel ECG data into a channel signal, and One channel signal is classified.
  • FIG. 1 is a method flowchart of a first embodiment of a multi-lead combined electrocardiogram classification method of the present invention.
  • step S1 the ECG data in the CCDD database is pre-processed.
  • the step S1 further includes: S11. Excluding normal ECG data and duplicate ECG data marked as disease labels in the CCDD database; S12. Formatting the remaining ECG data and converting Set the sampling frequency; S13. Select 8-lead ECG data as experimental data.
  • the CCDD database uses a large amount of clinical ECG data, so there are some problems in the data.
  • the database file needs to be preprocessed as follows.
  • the raw data file of the CCDD database is in xml format, and the sampling rate is 500Hz.
  • the selected data is first converted to Mat format which is easily processed by Matlab, and downsampled to 250Hz by Matlab. This can ensure the integrity of the ECG waveform while reducing the amount of calculation.
  • the normal and abnormal ECG data are divided into training set and test set.
  • step S2 the pre-processed multi-channel electrocardiogram data is encoded into a channel signal, and the one channel signal is classified.
  • a D-LSTM network structure is used for feature extraction of pre-processed multi-channel ECG data of each lead, and the output features are classified by a fully connected layer, hereinafter referred to as " Multi-RNN "network model.
  • Multi-RNN fully connected layer
  • a three-layer two-dimensional convolutional network is used to extract features from the pre-processed multi-channel ECG data of each lead, and classify the output features through a fully connected layer; the following; Called "Vanilla-CNN" network model.
  • a three-layer two-dimensional convolutional network is used to extract features from the pre-processed multi-channel ECG data of each lead, and classify the output features through the D-LSTM network structure. ; Hereinafter referred to as the "Feature-CNN” network model.
  • pre-processed multi-channel electrocardiogram data is taken as eight-channel one-dimensional data and encoded by channel changes, and then the encoded data is classified by a D-LSTM network structure, This is called the "Channel-CNN" network model.
  • FIG. 2 is a schematic structural diagram of a Multi-RNN model of a multi-lead combined electrocardiogram classification method of the present invention.
  • the Multi-RNN model since each lead of the electrocardiogram is a one-dimensional signal, it can be processed by the RNN. What is obtained is the characteristics of the timing signal, and then the output features are fully connected to obtain the classification result.
  • the RNN model may adopt the D-LSTM network model shown in FIG. 6. As shown in FIG. 6, it includes a deep LSTM network composed of multiple LSTM network units, a fully connected layer connected to the deep LSTM network, and a SoftMax layer connected to the fully connected layer.
  • the network uses a many-to-one model of sequence-to-label, that is, a time series uses only the output value of the last unit to match the label.
  • the back end of the model uses two fully connected layers to complete the final classification task, and the final output is the classification result.
  • the ECG sample contains multiple time states.
  • the multiple time states of the ECG signal are separated into t vectors of x1, x2 ... xt, and sent to the LSTM network to obtain the coding vector containing the sequence connection. After the full connection and the SoftMax layer are converted into probability values, the output classification results are obtained.
  • a four-layer D-LSTM network model is preferably used.
  • FIG. 3 is a schematic structural diagram of a Vanilla-CNN model of the multi-lead combined electrocardiogram classification method of the present invention.
  • a two-dimensional convolution network is used in the Vanilla-CNN model.
  • the convolution kernel and pooling layer are set to one dimension.
  • the final result is a 64-channel 8 * n
  • Features are equivalent to extracting features independently for each lead, and each lead will not affect each other.
  • FIG. 4 is a schematic structural diagram of a Feature-CNN model of the multi-lead combined electrocardiogram classification method of the present invention.
  • the CNN processing part is the same as Vanilla-CNN, and the multi-channel independent 8-lead feature is obtained.
  • Feature end did fusion coding operation.
  • FIG. 5 is a schematic structural diagram of a Channel-CNN model of the multi-lead combined electrocardiogram classification method of the present invention.
  • the ECG data is treated as a multi-channel one-dimensional signal instead of an independent multi-conductor signal. Therefore, the CNN network chooses to use one-dimensional convolution and performs channel operations.
  • the ECG signal is regarded as one-dimensional data of 8 channels, which is encoded by the change of the channel. Also access D-LSTM finally for classification.
  • FIG. 7 shows the accuracy of a multi-lead combined electrocardiogram classification method according to different embodiments of the present invention.
  • four different coding models achieved 81.64%, 81.5%, 82.5%, and 83.9% accuracy, respectively.
  • Multi-RNN and Vanilla-CNN a model in which each lead uses RNN or CNN to perform independent coding classification and then make classification decisions, has obtained similar classification accuracy. This result is better than the optimal V5 in a single lead classification The 80.36% accuracy of the lead is higher. From this result, we can see that the multi-lead joint classification has certain advantages over the classification using only a single lead. The increase in the amount of original information makes the classification more accurate.
  • Feature-CNN uses a model of CNN encoding and then RNN classification. After experimental results, it is found that it reaches about 82.5%, and the accuracy rate is 1% higher than the previous two models. This result shows that the fusion of CNN and RNN is effective in the classification of ECG signals.
  • CNN is used for front-end coding fusion
  • RNN is used for classification at the back end. Good results have been achieved on multi-channel timing signals such as ECG.
  • Channel-CNN uses the best class accuracy.
  • multi-lead ECG signals are treated as two-dimensional matrices. This model treats ECG signals as one-dimensional signals from multiple channels. When performing the convolution operation, a one-dimensional convolution layer is used, and then the channel fusion operation is performed. The data of all channels is weighted at the beginning, instead of each lead's independent coding and finally fusion.
  • the back end of the model still uses the RNN structure for classification. The experimental results prove that using this model structure is the most suitable classification method for ECG signals
  • Another embodiment of the present invention provides a machine-readable memory and / or storage medium, and the machine code and / or computer program stored therein includes at least one code segment, which is executed by a machine and / or a computer such that the machine and / or Or the computer executes each step of the multi-lead combined electrocardiogram classification method described in this application.
  • the present invention can be implemented by hardware, software, or a combination of software and hardware.
  • the invention may be implemented in a centralized manner in at least one computer system or in a decentralized manner by different parts distributed among several interconnected computer systems. Any computer system or other device that can implement the method of the present invention is applicable.
  • the combination of commonly used software and hardware can be a general-purpose computer system with a computer program installed, and the computer system can be controlled to run according to the method of the present invention by installing and executing the program.
  • the present invention can also be implemented by a computer program product.
  • the program includes all the features capable of implementing the method of the present invention. When installed in a computer system, the method of the present invention can be implemented.
  • the computer program in this document refers to: any expression that can use a set of instructions written in any programming language, code, or symbol, which enables the system to have information processing capabilities to directly implement specific functions, or to perform A specific function is achieved after describing one or two steps: a) conversion to other languages, codes or symbols; b) reproduction in different formats.

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Abstract

一种多导联联合心电图分类方法，包括：S1、将CCDD数据库中的心电图数据进行预处理；S2、将预处理后的多通道的心电图数据编码为一通道信号，并对该一通道信号进行分类。计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现该多导联联合心电图分类方法。实施该多导联联合心电图分类方法，能够自动检测正常和异常心电信号，以提高分类准确率。

Description

多导联联合心电图分类方法 技术领域
本发明涉及数据处理领域，更具体地说，涉及一种多导联联合心电图分类方法。
背景技术
目前，12导同步记录心电图仪已经在临床中广泛使用了。12导同步记录心电图仪可同时在12导联上记录同一心动周期的心电信号，对单源或多源早搏的识别及定位、心律失常的分类、室内传导阻滞的诊断等都比其他心电图仪具有优越性。12导同步记录心电图仪可同步整体观察12导联同一心动周期的波形，大大提高各种测量的准确性，降低了目前存在的心电图测量的变异性。
目前市面上的主流临床心电图为多导联多通道信号，因此在心电图机等大型设备上运行的心电分类算法必须结合多通道信号来进行心电图的辅助检测，如果单纯的将12导串联输入一方面不符合逻辑，一方面会导致准确率的负影响，因此对于12导同步记录心电图仪采集的多导联心电信号，需要一种能够自动检测正常和异常心电信号，以提高分类准确率的多导联联合心电图分类方法。
发明内容
本发明要解决的技术问题在于，针对现有技术的上述缺陷，提供一种能够自动检测正常和异常心电信号，以提高分类准确率的多导联联合心电图分类方法。
本发明解决其技术问题所采用的技术方案是：构造一种多导联联合心电图分类方法，包括：
S1、将CCDD数据库中的心电图数据进行预处理；
S2、将预处理后的多通道的心电图数据编码为一通道信号，并对所述一通道信号进行分类。
在本发明所述的多导联联合心电图分类方法中，所述步骤S1进一步包括：
S11、剔除所述CCDD数据库中被标记为疾病标签的正常心电图数据和重复的心电图数据；
S12、对剩余的心电图数据进行格式转换并设置采样频率；
S13、选择8导联的心电图数据作为实验数据。
在本发明所述的多导联联合心电图分类方法中，在所述步骤S12中，将采样率为500Hz的xml格式的心电图数据转换成采样率为250Hz的mat格式的心电图数据。
在本发明所述的多导联联合心电图分类方法中，所述步骤S2进一步包括：
S21、采用D-LSTM网络结构对每个导联的预处理后的多通道的心电图数据进行特征提取，并将输出特征通过全连接层进行分类；
S22、采用三层二维卷积网络对每个导联的预处理后的多通道的心电图数据进行特征提取，并将输出特征通过全连接层进行分类；
S23、采用三层二维卷积网络对每个导联的预处理后的多通道的心电图数据进行特征提取，并将输出特征通过D-LSTM网络结构进行分类；或
S24、将预处理后的多通道的心电图数据作为八通道的一维数据并通过通道变化进行编码，然后将编码后的数据通过D-LSTM网络结构进行分类。
在本发明所述的多导联联合心电图分类方法中，所述D-LSTM网络结构包括：多个LSTM网络单元构成的深度LSTM网络，与所述深度LSTM网络连接的第一全连接层，以及与所述第一全连接层连接的第二全连接层。
在本发明所述的多导联联合心电图分类方法中，所述第二全连接层是SoftMax层。
在本发明所述的多导联联合心电图分类方法中，每个导联的预处理后的多通道的心电图数据通过所述三层二维卷积网络将卷积核和池化层设置为一维，经过三层的卷积后，得到的是64通道8*n的特征。
本发明解决其技术问题所采用的另一技术方案是：构造一种计算机可读存 储介质，其上存储有计算机程序，所述程序被处理器执行时实现所述的多导联联合心电图分类方法。
实施本发明的多导联联合心电图分类方法，能够自动检测正常和异常心电信号，以提高分类准确率。
附图说明
下面将结合附图及实施例对本发明作进一步说明，附图中：
图1是本发明的多导联联合心电图分类方法的第一实施例的方法流程图；
图2是本发明的多导联联合心电图分类方法的Multi-RNN模型的结构示意图；
图3是本发明的多导联联合心电图分类方法的Vanilla-CNN模型的结构示意图；
图4是本发明的多导联联合心电图分类方法的Feature-CNN模型的结构示意图；
图5是本发明的多导联联合心电图分类方法的Channel-CNN模型的结构示意图；
图6是本发明的多导联联合心电图分类方法的D-LSTM网络模型的结构示意图；
图7示出了本发明的不同实施例的多导联联合心电图分类方法的准确率。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
本发明涉及一种多导联联合心电图分类方法，包括：S1、将CCDD数据库中的心电图数据进行预处理；S2、将预处理后的多通道的心电图数据编码为一通道信号，并对所述一通道信号进行分类。实施本发明的多导联联合心电图分类方法，能够自动检测正常和异常心电信号，以提高分类准确率。
图1是本发明的多导联联合心电图分类方法的第一实施例的方法流程图。如图1所示，在步骤S1中，将CCDD数据库中的心电图数据进行预处理。在本发明的优选实施例中，所述步骤S1进一步包括：S11、剔除所述CCDD数据库中被标记为疾病标签的正常心电图数据和重复的心电图数据；S12、对剩余的心电图数据进行格式转换并设置采样频率；S13、选择8导联的心电图数据作为实验数据。
例如，CCDD数据库使用了大量的临床心电图数据，因此数据方面存在一些问题，为了保证实验的正确性和标准型，需要对数据库文件进行如下预处理操作。
(1)CCDD数据库中有些正常心电图混入了疾病标签，这些被污染的数据会使得分类实验中对正常心电信号的识别造成负面影响。因此首先将被污染的正常心电数据剔除，只保存有正常心电图标签的样本作为正样本，保证了数据文件的纯净性。
(2)数据库原始文件中存在两万个完全重复的样本，经过验证除了序号不同，其他信息全部相同。应该是数据库的存储错误。这两万个样本如果混入训练集和测试集将会轻微降低分类难度，使得分类准确率升高。无法反映出模型的真实能力，因此首先将重复的样本剔除，保证实验的公平。
(3)CCDD数据库的原始数据文件为xml格式，采样率为500Hz。为了方便使用，首先将挑选出来的数据转换为Matlab方便处理的mat格式，并通过Matlab降采样至250Hz，这样做可以在减少运算量的情况下保证心电波形的完整。并且将正常和异常的心电数据分为训练集和测试集。
(4)因为心电图是正交的，使用8个导联即可推导出另外四个，所以做多通道分类时可以只选取8个导联。在保证分类结果的同时减少了输入数据的维度。大大减少了训练的时间和难度。
进一步如图1所示，在步骤S2中，将预处理后的多通道的心电图数据编码为一通道信号，并对所述一通道信号进行分类。在本发明的一个优选实施例中，采用D-LSTM网络结构对每个导联的预处理后的多通道的心电图数据进行特征提取，并将输出特征通过全连接层进行分类，以下称为“Multi-RNN” 网络模型。在本发明的又一优选实施例中，采用三层二维卷积网络对每个导联的预处理后的多通道的心电图数据进行特征提取，并将输出特征通过全连接层进行分类；以下称为“Vanilla-CNN”网络模型。在本发明的又一优选实施例中，采用三层二维卷积网络对每个导联的预处理后的多通道的心电图数据进行特征提取，并将输出特征通过D-LSTM网络结构进行分类；以下称为“Feature-CNN”网络模型。在本发明的又一优选实施例中，将预处理后的多通道的心电图数据作为八通道的一维数据并通过通道变化进行编码，然后将编码后的数据通过D-LSTM网络结构进行分类，以下称为“Channel-CNN”网络模型。
图2是本发明的多导联联合心电图分类方法的Multi-RNN模型的结构示意图。如图2所示，在Multi-RNN模型中，因为心电图的每个导联都是一维信号，可以通过RNN进行处理。得到的是时序信号的特征，然后将输出的特征进行全连接得到分类结果。RNN模型可以采用图6所示的D-LSTM网络模型。如图6所示，其包括多个LSTM网络单元构成的深度LSTM网络，与所述深度LSTM网络连接的全连接层，以及与所述全连接层连接的SoftMax层。该网络使用序列对标签的多对一模型，即一个时序序列只使用最后单元的输出值来匹配标签。模型后端使用两个全连接层来完成最后的分类任务，最后输出的是分类的结果。心电图样本包含多个时间状态，将心电信号的多个时间状态分离为x1,x2…xt共t个向量，送入LSTM网络，得到包含序列前后联系的编码向量。经过全连接和SoftMax层转换为概率值，得到输出的分类结果。在本发明的优选实施例中，优选采用四层的D-LSTM网络模型。
图3是本发明的多导联联合心电图分类方法的Vanilla-CNN模型的结构示意图。如图3所示，在Vanilla-CNN模型中，使用二维卷积网络，卷积核和池化层设置为一维，经过三层的卷积后，最后得到的是64通道8*n的特征，相当于每个导联独立提取特征，每个导联间不会相互影响。
图4是本发明的多导联联合心电图分类方法的Feature-CNN模型的结构示意图。如图4所示，在Feature-CNN模型中，CNN处理部分与Vanilla-CNN相同，得到的是多通道独立的8导特征，将后端的全连接替换为D-LSMT模型 进行分类，相当于在特征端做了融合编码操作。
图5是本发明的多导联联合心电图分类方法的Channel-CNN模型的结构示意图。如图5所示，在Channel-CNN模型中，将心电图数据视多通道的一维信号进行处理，而不是独立的多导信号，因此CNN网络选择使用一维卷积，而且进行的是通道操作，将心电信号视作8通道的一维数据，通过通道的变化进行编码。同样最后接入D-LSTM进行分类。
图7示出了本发明的不同实施例的多导联联合心电图分类方法的准确率。如图7所示，四种不同的编码模型分别取得了81.64％、81.5％、82.5％和83.9％的准确率。Multi-RNN与Vanilla-CNN这种每个导联使用RNN或者CNN进行独立编码分类然后进行分类判决的模型，得到了相似的分类准确率，这样的结果比单个导联分类中的最优的V5导联的80.36％准确率要高。通过这个结果我们可以看出多导联联合分类相对于只使用单导联的分类有一定优势，原始信息量的增加使得分类更为精确。Feature-CNN使用了CNN编码而后RNN分类的模型，经过实验结果发现达到了82.5％左右，准确率比前两个模型要高出1％。这个结果说明CNN和RNN的融合，在心电信号的分类领域是有效的，使用CNN进行前端的编码融合，后端使用RNN进行分类，在心电这种多通道的时序信号上取得了不错的效果。Channel-CNN使用取得了最好的类准确率，不同于多数研究将多导联心电信号视作二维矩阵来进行处理，该模型是将心电信号视作多个通道的一维信号，在进行卷积操作时使用一维卷积层，然后进行通道的融合操作，在一开始就对所有通道的数据进行了加权，而不是每个导联独立的编码最后再进行融合。模型后端仍使用RNN结构进行分类。实验结果证明使用这种模型结构是最适合心电信号的分类方法。
实施本发明的多导联联合心电图分类方法，能够自动检测正常和异常心电信号，以提高分类准确率。
本发明的另一个实施例提供一种可机读存储器和/或存储介质，其内存储的机器代码和/或计算机程序包括至少一个代码段，由机器和/或计算机执行而使得该机器和/或计算机执行本申请中描述的所述的多导联联合心电图分类方法的各个步骤。
因此，本发明可以通过硬件、软件或者软、硬件结合来实现。本发明可以在至少一个计算机***中以集中方式实现，或者由分布在几个互连的计算机***中的不同部分以分散方式实现。任何可以实现本发明方法的计算机***或其它设备都是可适用的。常用软硬件的结合可以是安装有计算机程序的通用计算机***，通过安装和执行程序控制计算机***，使其按本发明方法运行。
本发明还可以通过计算机程序产品进行实施，程序包含能够实现本发明方法的全部特征，当其安装到计算机***中时，可以实现本发明的方法。本文件中的计算机程序所指的是：可以采用任何程序语言、代码或符号编写的一组指令的任何表达式，该指令组使***具有信息处理能力，以直接实现特定功能，或在进行下述一个或两个步骤之后实现特定功能：a)转换成其它语言、编码或符号；b)以不同的格式再现。
虽然本发明是通过具体实施例进行说明的，本领域技术人员应当明白，在不脱离本发明范围的情况下，还可以对本发明进行各种变换及等同替代。另外，针对特定情形或材料，可以对本发明做各种修改，而不脱离本发明的范围。因此，本发明不局限于所公开的具体实施例，而应当包括落入本发明权利要求范围内的全部实施方式。

Claims (8)

  1. 一种多导联联合心电图分类方法，其特征在于，包括：
    S1、将CCDD数据库中的心电图数据进行预处理；
    S2、将预处理后的多通道的心电图数据编码为一通道信号，并对所述一通道信号进行分类。
  2. 根据权利要求1所述的多导联联合心电图分类方法，其特征在于，所述步骤S1进一步包括：
    S11、剔除所述CCDD数据库中被标记为疾病标签的正常心电图数据和重复的心电图数据；
    S12、对剩余的心电图数据进行格式转换并设置采样频率；
    S13、选择8导联的心电图数据作为实验数据。
  3. 根据权利要求2所述的多导联联合心电图分类方法，其特征在于，在所述步骤S12中，将采样率为500Hz的xml格式的心电图数据转换成采样率为250Hz的mat格式的心电图数据。
  4. 根据权利要求1所述的多导联联合心电图分类方法，其特征在于，所述步骤S2进一步包括：
    S21、采用D-LSTM网络结构对每个导联的预处理后的多通道的心电图数据进行特征提取，并将输出特征通过全连接层进行分类；
    S22、采用三层二维卷积网络对每个导联的预处理后的多通道的心电图数据进行特征提取，并将输出特征通过全连接层进行分类；
    S23、采用三层二维卷积网络对每个导联的预处理后的多通道的心电图数据进行特征提取，并将输出特征通过D-LSTM网络结构进行分类；或
    S24、将预处理后的多通道的心电图数据作为八通道的一维数据并通过通道变化进行编码，然后将编码后的数据通过D-LSTM网络结构进行分类。
  5. 根据权利要求4所述的多导联联合心电图分类方法，其特征在于，所述D-LSTM网络结构包括：多个LSTM网络单元构成的深度LSTM网络，与所述深度LSTM网络连接的第一全连接层，以及与所述第一全连接层连接的 第二全连接层。
  6. 根据权利要求5所述的多导联联合心电图分类方法，其特征在于，所述第二全连接层是SoftMax层。
  7. 根据权利要求4所述的多导联联合心电图分类方法，其特征在于，每个导联的预处理后的多通道的心电图数据通过所述三层二维卷积网络将卷积核和池化层设置为一维，经过三层的卷积后，得到的是64通道8*n的特征。
  8. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，所述程序被处理器执行时实现根据权利要求1-7中任意一项权利要求所述的多导联联合心电图分类方法。
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