ECG recognition method and device based on multi-scale differential feature
A recognition method and multi-scale technology, applied in character and pattern recognition, medical science, diagnosis, etc., can solve problems such as insufficient robustness, pattern aliasing, non-discrimination, etc., and achieve good discrimination and robustness, The effect of improving speed and accuracy and improving efficiency
Active Publication Date: 2019-04-16
SHANDONG UNIV
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[0005] However, wavelet decomposition lacks adaptability. Although EMD can adaptively decompose signals, there is mode aliasing phenomenon. EEMD method eliminates the mode aliasing phenomenon in the process of EMD decomposition, but it needs multiple iterative operations, which is difficult to meet the biological requirements. Real-time requirements for feature recognition
Moreover, using sub-signals directly as features is not discriminative and not robust enough
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[0044] The technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in one or more embodiments of the present disclosure. Obviously, the described embodiments are only part of the implementation of the present invention. example, not all examples. Based on one or more embodiments of the present disclosure, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts fall within the protection scope of the present invention.
[0045] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless otherwise specified, all technical and scientific terms used in this embodiment have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
[0046] It should be noted that the terminology used here is ...
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The invention discloses an ECG recognition method and device based on a multi-scale differential feature. The method comprises receiving an acquired ECG signal to be recognized and preprocessing the ECG signal to obtain heartbeat cycles; adaptively separating the heartbeat cycles to obtain empirical modal components of the heartbeat cycles, combining the empirical modal components of multiple heartbeat cycles and extracting the multi-scale difference feature to obtain a feature vector; recognizing the extracted feature vector according to the individual feature vector in an ECG template library to obtain an ECG recognition result .
Description
technical field [0001] The disclosure belongs to the technical field of biological feature recognition, and relates to a method and device for electrocardiographic recognition based on multi-scale differential features. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] In recent years, biometric identification technology based on ECG signals has received widespread attention. ECG signals are easy to collect and can be collected based on the two fingers of the left and right hands of the human body. Moreover, ECG signals can only be collected when they are alive. The internal continuous electrical signal is very safe and has broad application prospects. [0004] With the continuous development of signal processing and machine learning theory, many new methods and ideas have been applied to the field of ECG recognition in recent decades. Am...
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IPC IPC(8): A61B5/0402A61B5/00G06K9/62
CPCA61B5/7203A61B5/7235A61B5/725A61B5/7253A61B5/316A61B5/318G06F18/22G06F18/2135
Inventor 杨公平吴波王奎奎黄玉文尹义龙
Owner SHANDONG UNIV