Multi-class arrhythmia detection method based on lead attention mechanism

A technology of arrhythmia and detection method, which is applied in the field of medical signal processing, can solve the problem of ignoring the characteristics of 12-lead ECG signals, etc., and achieve the effect of improving the detection performance of arrhythmia and the detection performance

Active Publication Date: 2020-03-17
UNIV OF SCI & TECH OF CHINA
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Problems solved by technology

In previous work, the 12-lead nature of the ECG signal was ignored

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  • Multi-class arrhythmia detection method based on lead attention mechanism
  • Multi-class arrhythmia detection method based on lead attention mechanism
  • Multi-class arrhythmia detection method based on lead attention mechanism

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Embodiment Construction

[0057] In this embodiment, a multi-type arrhythmia detection method based on the lead attention mechanism is named LTA-CNN, such as figure 1 As shown, it includes the following steps:

[0058] Step 1. Obtain the original ECG signal and its corresponding label, down-sample the original ECG signal to obtain the sampled ECG signal; perform clipping or mean value filling processing on the sampled ECG signal to obtain the pre-processed ECG signal;

[0059] Step 1.1. Obtain the original ECG signal and its corresponding label required for the experiment from the public data of the China Physiological Signal Challenge (CPSC) 2018. The public data of CPSC2018 provides 6877 12-lead ECG records with a time length ranging from 6 seconds to 60 seconds. Records were collected from 11 hospitals with a sampling rate of 500 Hz. The ECG records contained nine heart rhythm types, eight arrhythmias and normal heart rhythms, as shown in Table 1.

[0060] Table 1: Configuration of the dataset

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Abstract

The invention discloses a multi-class arrhythmia detection method based on a lead attention mechanism. The multi-class arrhythmia detection method comprises the steps of: 1, performing down-sampling on an original ECG signal, and processing the original ECG signal until the length of the ECG signal reaches a fixed length; 2, designing a classification model, and integrating four network structures, including a lead attention mechanism, a convolutional neural network, a bidirectional gating circulation unit and a time attention mechanism; 3, training the model on a public data set by adopting four-fold cross validation; and 4, achieving an arrhythmia classification task by utilizing the trained model. According to the invention, high-accuracy automatic arrhythmia detection can be achieved,so that assistance is provided for diagnosis of doctors.

Description

technical field [0001] The invention relates to the field of medical signal processing, in particular to a method for detecting arrhythmia from electrocardiogram signals. Background technique [0002] Cardiac arrhythmia is a condition in which the electrical activity of the heart is irregular. Many types of arrhythmias are harmful to health and even life-threatening, such as ventricular tachycardia and ventricular fibrillation are fatal arrhythmias. Electrocardiogram (ECG) records the electrical activity of the heart over a period of time through electrodes placed on the skin, and is widely used clinically to detect arrhythmia. The electrocardiogram captures the potential of the heart from different angles through different leads, and reflects the heart rhythm through changes in waveform or rhythm. Physicians can use the electrocardiogram to understand the risk of various heart diseases in patients. A 24-hour ECG monitor records hundreds of thousands of heartbeats. Analyz...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20A61B5/0402A61B5/04A61B5/00
CPCG16H50/20A61B5/7271A61B5/7267A61B5/316A61B5/318
Inventor 陈勋张静梁邓高敏张旭陈香
Owner UNIV OF SCI & TECH OF CHINA
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