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Distributed calculation and deep learning-based electrocardio beat classification method

A distributed computing and deep learning technology, applied in computing, computer parts, character and pattern recognition, etc., can solve problems such as misclassification and deviation in sign description, and achieve the goal of reducing data volume, efficient learning, and capacity reduction. Effect

Active Publication Date: 2018-08-14
SOUTHEAST UNIV
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Problems solved by technology

[0005] Purpose of the invention: In order to solve the problems of the prior art, the present invention proposes a ECG beat classification method based on distributed computing and deep learning, which is suitable for scenarios with a large amount of ECG data to be classified, and can solve the problem of easy deviation in the description of signs Especially when the characteristics of the ECG data are not obvious, it is easy to misclassify the problem

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  • Distributed calculation and deep learning-based electrocardio beat classification method
  • Distributed calculation and deep learning-based electrocardio beat classification method
  • Distributed calculation and deep learning-based electrocardio beat classification method

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Embodiment Construction

[0019] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0020] refer to figure 1 , the present invention adopts a distributed computing structure, and the distributed computing divides M different ECG data into M parts according to types, and distributes them to M working nodes for processing respectively. The main reason is that each type of ECG data requires different computing power through the deep learning framework. If one type can be trained in advance, the working nodes can be vacated to avoid the waste of computing power, which can save the overall computing time. Greatly improve computing efficiency. Compared with traditional classification methods and simple deep learning classification learning, it has the ability to process ECG big data, which can greatly speed up the data processing process. There are 16 types of ECG data processed in this embodiment, and the number of working nodes...

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Abstract

The invention discloses a distributed calculation and deep learning-based electrocardio beat classification method. The method comprises the following steps of: firstly obtaining an electrocardio beatsignal, dividing a sample set, and carrying out local regionalization on electrocardio data in a training set; constructing a distributed deep learning field, carrying out training by utilizing dataof the training set, and realizing data parallelization by adoption of a software synchronization method in the training; and finally classifying electrocardio data of a test set by utilizing the trained deep learning field. By utilizing the method, potential information in data can be discovered, so that the problem that sign description in traditional electrocardio beat classification process iseasy to cause deviations and wrong classification is easy to occur when electrocardio data features are not obvious are solved, and the problem that single-machine training consume too much time is solved; and the method can be applied to the classification of mass EGG data and remarkably improve the calculation efficiency.

Description

technical field [0001] The invention relates to a method for classifying electrocardiographic beats, in particular to a method for classifying electrocardiographic beats based on distributed computing and deep learning algorithms. Background technique [0002] Electrocardiography (ECG) signal analysis plays an important role in the diagnosis of cardiovascular diseases, because the ECG signal records the electrical activity of the heart and can provide important pathological information about the state of the human heart. However, due to the complex changes in ECG data and the limited capabilities of the human eye, it is actually impractical for doctors to analyze a large amount of ECG data in a short period of time. Therefore, computer-aided diagnosis systems have attracted more and more attention in recent years. With the continuous development of wearable ECG acquisition equipment, the ECG data collected by such equipment in the future will be massive, and the ECG data th...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06N3/045G06F2218/12G06F18/214
Inventor 李潍孙琦胡振原李建清
Owner SOUTHEAST UNIV
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