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Intelligent arrhythmia diagnosis method based on multiple-lead and convolutional neural network

A convolutional neural network, arrhythmia technology, applied in the direction of diagnosis, medical automation diagnosis, diagnosis recording/measurement, etc., can solve problems such as heavy workload

Inactive Publication Date: 2016-07-13
SHANDONG UNIV QILU HOSPITAL +1
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Problems solved by technology

The biggest disadvantage of these methods is that they rely heavily on the selection of features, and the workload is large

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  • Intelligent arrhythmia diagnosis method based on multiple-lead and convolutional neural network
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  • Intelligent arrhythmia diagnosis method based on multiple-lead and convolutional neural network

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Embodiment Construction

[0043] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0044] Suppose x is a multi-lead ECG signal sequence in one heartbeat cycle of a normal person, then x can be expressed as

[0045] S lead =[s 1 ,s 2 ,...s i ,...s n ],

[0046] where S lead Indicates that the lead is the signal sequence of lead, s i is an ECG signal value. For example, the leads of the twelve-lead ECG are six limb leads: I lead, II lead, III lead, aVL lead, aVR lead, aVF lead, and six chest leads: V1 lead , V2 lead, V3 lead, V4 lead, V5 lead, V6 lead, then

[0047] lead ∈ {I, II, III, aVL, aVR, aVF, V1, V2, V3, V4, V5, V6}.

[0048] Assuming that any heartbeat x has a unique corresponding heart rhythm type, which is either a normal heart rhythm or an abnormal heart rhythm, set y, then there is a functional relationship Γ between y and x, that is, y=Γ(x). Realize the intelligent diagnosis of arrhythmia, that is, use the ECG...

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Abstract

The invention provides an intelligent arrhythmia diagnosis method based on multiple-lead and a convolutional neural network. The method includes the steps that 1, data samples are selected; 2, arrhythmia types are labeled; 3, led heartbeat signals are intercepted; 4, a normalized heartbeat set is obtained; 5, a concealed layer and an output layer are constructed; 6, a target function is set; 7, sample training is conducted; 8, arrhythmias classification is applied. According to the intelligent arrhythmia diagnosis method, network learning efficiency and precision of automatic arrhythmia diagnosis can be improved by training the convolutional neural network (CNN) through multiple-lead electrocardiogram data, a universal frame and a specific method for training the CNN through the multiple-lead electrocardiogram data with arrhythmia type labels are achieved, the arrhythmia types of electrocardiosignals to be diagnosed can be accurately judged, and the arrhythmia types can serve as diagnosis results or as reference of doctors.

Description

technical field [0001] The invention relates to a method for intelligently diagnosing cardiac arrhythmia using a deep convolutional neural network (Convolutional Neural Network, CNN). More specifically, a large number of multi-lead ECG data with arrhythmia labels are used to train a deep convolutional neural network, and the arrhythmia information contained in it is automatically learned, so as to achieve the purpose of automatic diagnosis. . Background technique [0002] Electrocardiogram examination has become a common test item in hospitals. Clinically, doctors mainly evaluate the heart health of patients through electrocardiogram, which plays a key role in the diagnosis of arrhythmia. At the same time, long-term monitoring of electrocardiogram is an effective means to prevent and treat arrhythmia in time, and it also provides people with the ability to control their own heart conditions. possible. The ECG signal is a non-stationary periodic biological signal, which is...

Claims

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Application Information

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IPC IPC(8): A61B5/0402A61B5/024G06F19/00
CPCA61B5/024A61B5/7264A61B5/7267A61B5/318G16H50/20
Inventor 朱清高岩舒明雷马静周书旺高天雷刘照阳
Owner SHANDONG UNIV QILU HOSPITAL
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