Abnormal electrocardiogram recognition method based on ultra-complete characteristics

An identification method and over-complete technology, applied in the field of signal processing, can solve the problems of low accuracy, affect the performance during identification, and high computational complexity, reduce individual differences, improve the accuracy of automatic ECG and heart rhythm identification, and ensure identification. effect of ability

Inactive Publication Date: 2007-04-25
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

Its shortcoming is that it can only achieve good results for normal heartbeats, the accuracy of the other four types is not high, and it only classifies 5 types of ECG signals. Secondly, due to the large number of components used, the computational complexity of the training phase is very high. , if the dimension of the feature vector used is too high, it will affect the performance of recogn

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  • Abnormal electrocardiogram recognition method based on ultra-complete characteristics
  • Abnormal electrocardiogram recognition method based on ultra-complete characteristics

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Embodiment Construction

[0018] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings: the present embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and processes are provided, but the protection scope of the present invention is not limited to the following implementations example.

[0019] This embodiment mainly diagnoses and identifies 14 kinds of arrhythmia heartbeat data. The data adopts the data in the MIT-BIH arrhythmia database. The 14 types include: left bundle branch block, right bundle branch block, atrial premature systole, Ventricular premature contraction, deformed atrial extrasystoles, paced heartbeat, atrioventricular junction premature beat, ventricular and normal heartbeat fusion, atrial flutter, atrioventricular junction regional escape, ventricular escape, paced and normal Fusion heartbeat, premature atrial transmission, normal heartbeat.

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Abstract

The invention relates to an abnormal cardioelectric recognize method based on ultra-complete character. Wherein. it first uses secondary sample wavelet to process R-point check on the cardioelectric data, segments and pretreats the cardioelectric data based on the R-point position; then uses independent component method and disperse wavelet convert method to extract one ultra-complete character group from each heart beat, and contract the character via communication method; at last uses the vector method to train the extracted character to obtain one vector mode, to automatically recognize and classify the new heart beat data.

Description

technical field [0001] The invention relates to a method in the technical field of signal processing, in particular to a method for identifying abnormal ECG based on overcomplete features. Background technique [0002] Electrocardiogram (ECG) is often used to detect heart activity in clinical medicine and has very important clinical value. Record a complete ECG waveform on the recording paper of the electrocardiograph, mainly including: P wave (the P wave that first appears in each wave of the ECG is the P wave representing the excitation process of the left and right atria), QRS wave group (representing the propagation process of the excitation of the two ventricles) A typical QRS complex includes three connected fluctuations, the first downward wave is the Q wave, followed by the Q wave, a narrow and high upward wave is the R wave, and another connected to the R wave is the R wave. The lower wave is the S wave), the S-T segment (the flat line from the end of the QRS wave ...

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Application Information

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IPC IPC(8): A61B5/0472G06F19/00A61B5/366
Inventor 赵启斌张丽清蒋星
Owner SHANGHAI JIAO TONG UNIV
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