Intelligent Recognition Algorithm of Multiple Myocardial Infarction Based on Morphological Feature Extraction

Myocardial infarction is a type of heart disease marked by rapid progression and high mortality. In this paper, a novel intelligent recognition algorithm of multiple myocardial infarctions using a bidirectional long short-term memory (BiLSTM) neural network classification was proposed. This algorith...

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Bibliographic Details
Published in:Processes Vol. 10; no. 11; p. 2348
Main Authors: Xu, Wenchang, Wang, Lei, Wang, Biao, Cheng, Wenbo
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2022
Subjects:
Accuracy
Algorithms
Cardiovascular diseases
Computational linguistics
Development and progression
Diagnosis
Disease
EKG
Electrocardiogram
Electrocardiography
Feature extraction
Health aspects
Heart attacks
Heart diseases
Language processing
Localization
Long short-term memory
Michigan
Morphology
Mortality
Myocardial infarction
Natural language interfaces
Neural networks
Noise control
Noise reduction
Parameter sensitivity
Segmentation
Waveforms
Wavelet transforms
Michigan
ISSN:2227-9717, 2227-9717
Online Access:Get full text
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Summary:Myocardial infarction is a type of heart disease marked by rapid progression and high mortality. In this paper, a novel intelligent recognition algorithm of multiple myocardial infarctions using a bidirectional long short-term memory (BiLSTM) neural network classification was proposed. This algorithm was based on morphological feature extraction, which can greatly improve the diagnostic efficiency of doctors for different kinds of myocardial infarction diseases. The algorithm includes noise reduction and beat segmentation of electrocardiogram (ECG) signals from the Physikalisch-Technische Bundesanstalt (PTB) database. According to the medical diagnosis guide, the distance feature of the whole waveform and the amplitude feature of the branch lead waveform are extracted. According to the extracted features, the long short-term memory network (LSTM) and the BiLSTM neural networks are built to classify and recognize heartbeats. The experimental results show that the accuracy of the morphological feature + BiLSTM algorithm in MI detection is 99.4%. At the same time, among the six common myocardial infarction diseases, the location and recognition rate of the culprit vessel is high. The sensitivity, specificity, PPV, NPV, and F1 score parameters all reach more than 98.4%, and the kappa coefficient also reaches 0.983, while the overall accuracy reaches 98.6%. The accuracy of this algorithm is improved by at least 1% compared with that of other existing algorithms. Thus, this study exhibits a very important clinical application value.
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr10112348