Classification method of heart beat based on BiLSTM-Attention deep neural network

A deep neural network and classification method technology, applied in the field of heart beat detection and classification, can solve the problem of inability to extract features from ECG sampling points, achieve good spatial and frequency domain positioning characteristics, accurate ECG signal classification, and effective deep learning classification. Effect

Pending Publication Date: 2019-06-07
ZHENGZHOU UNIV
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Problems solved by technology

However, the feature extraction and model classification performed by these machine learning algorithms cannot comprehensively extract features for each ECG sampling point.

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  • Classification method of heart beat based on BiLSTM-Attention deep neural network
  • Classification method of heart beat based on BiLSTM-Attention deep neural network
  • Classification method of heart beat based on BiLSTM-Attention deep neural network

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[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0042] A heartbeat classification method based on BiLSTM-Attention deep neural network, comprising the following steps:

[0043] 1), data preprocessing, using biorthogonal wavelet transform to remove high-frequency noise and baseline drift;

[0044] 2), feature extraction, detect R wave peak through binary spline wavelet transform, then calculate RR interval and extract QRS complex data;

[0045] 3), model training, through the BiLSTM-Attention neural network...

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Abstract

The invention relates to a classification method of heart beat based on BiLSTM-Attention deep neural network. The classification method includes the following steps: 1) data pre-processing, using bi-orthogonal wavelet transformation to remove high-frequency noise and baseline drift; 2) feature extracting, detecting R-wave peak value by dyadic spline wavelet transformation, and further calculatingRR interval and extracting QRS wave complex data; and 3) model training, carrying out deep learning and classification on the detected waveform in step 2 through BiLSTM-Attention neural network. The classification method has advantages of accurate and effective classification of electrocardiogram (ECG) signals and deep learning and classification of ECG signals

Description

technical field [0001] The invention belongs to the technical field of heartbeat detection and classification, and in particular relates to a heartbeat classification method based on a BiLSTM-Attention deep neural network. Background technique [0002] Arrhythmia is a symptom of abnormal cardiac electrical activity caused by abnormal frequency or rhythm of cardiac beating due to the origin or conduction blockage of cardiac activity. It is an important group of diseases in cardiovascular diseases. Cardiac activity analysis is the key to intelligently judging various parameters of the human body, and electrocardiography (ECG) is an important means of non-invasive examination and diagnosis of various heart diseases such as arrhythmia, and also reflects the periodic activity of the heart. It is an important indicator and is widely used in clinical practice all over the world. Arrhythmia is an extremely common and very important disease type, and its identification is one of the...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0402A61B5/0472A61B5/366
Inventor 李润川王宗敏
Owner ZHENGZHOU UNIV
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