Method for detecting myocardial infarction based on wavelet packet features of short-time HRV signal

A detection method and wavelet packet technology, applied in the field of pattern recognition, can solve problems such as being unable to be widely used, and achieve the effects of easy operation, high efficiency, and clear principles

Pending Publication Date: 2020-01-31
HANGZHOU DIANZI UNIV
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  • Claims
  • Application Information

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Problems solved by technology

Due to its own price and psychological factors of patients, imaging examination cannot be widely used in ...

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  • Method for detecting myocardial infarction based on wavelet packet features of short-time HRV signal
  • Method for detecting myocardial infarction based on wavelet packet features of short-time HRV signal
  • Method for detecting myocardial infarction based on wavelet packet features of short-time HRV signal

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Embodiment 1

[0072] In this example, a total of 90 samples were used, including 45 healthy samples and 45 abnormal samples. A conventional 12-lead electrocardiogram was used, with a total of 1080 samples of 12*90. The seven extracted feature values ​​were fused according to a ratio of 2:1. Divide the samples into 720 training samples and 360 test samples for training and testing, respectively use KNN for testing, the test accuracy rate is 74.4%; use ELM for testing, the test accuracy rate is 83.3%; use random forest for testing, the test accuracy The rate is 81.1%; using ensemble learning to test, the test accuracy rate is 75%. Among them, ELM and random forest have higher classification accuracy. Overall, the whole prediction method has a clear principle and high efficiency, and can identify the category of the sample more accurately in a shorter time. Figure 5 It is the classification recognition result of wavelet packet feature data in the embodiment of the present invention.

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Abstract

The invention discloses a method for detecting myocardial infarction based on wavelet packet features of a short-time HRV signal. The method comprises the steps that first data preprocessing is carried out on a conventional twelve-lead electrocardiogram, R wave locating is carried out, a RR interval is determined, and the HRV signal is obtained; and then wavelet packet decomposition is carried outon the HRV signal to obtain wavelet packet coefficients, then the wavelet packet coefficients are subjected to wavelet packet feature extraction, and finally, the extracted wavelet packet features are normalized for classification and recognition. The method can be applied to the detection of myocardial infarction, effective detection can be performed in a short period of time without adding newdetection equipment, and the method is simple, convenient, easy to operate and high in prediction efficiency.

Description

technical field [0001] The invention belongs to the field of pattern recognition, in particular to a method for detecting myocardial infarction based on short-term HRV signal wavelet packet characteristics. Background technique [0002] Myocardial infarction is myocardial necrosis caused by acute and persistent coronary ischemia and hypoxia. Clinically, the main manifestation is persistent severe retrosternal pain, which cannot be completely relieved by rest and nitrates, accompanied by elevated serum myocardial enzymes and progressive changes in the electrocardiogram, which may be complicated by arrhythmia, shock or heart failure, often life-threatening. Generally speaking, myocardial infarction is a serious and critical type of coronary heart disease. Due to its own price and psychological factors of patients, imaging examination cannot be widely used in general population screening, and imaging examination alone sometimes cannot maximize the diagnostic efficiency. In co...

Claims

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Application Information

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IPC IPC(8): A61B5/024A61B5/0456A61B5/00A61B5/352
CPCA61B5/02405A61B5/72A61B5/7203A61B5/7225A61B5/7267A61B5/352
Inventor 邓木清张壮黄晓渝宁丽萍
Owner HANGZHOU DIANZI UNIV
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