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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Veröffentlicht in:	Processes Jg. 10; H. 11; S. 2348
Hauptverfasser:	Xu, Wenchang, Wang, Lei, Wang, Biao, Cheng, Wenbo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.11.2022
Schlagworte:	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
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Abstract	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.
AbstractList	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.
Audience	Academic
Author	Cheng, Wenbo Wang, Lei Wang, Biao Xu, Wenchang
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Snippet	Myocardial infarction is a type of heart disease marked by rapid progression and high mortality. In this paper, a novel intelligent recognition algorithm of...
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SubjectTerms	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
Title	Intelligent Recognition Algorithm of Multiple Myocardial Infarction Based on Morphological Feature Extraction
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