An Evaluation of Machine Learning Classifiers for Detection of Myocardial Infarction Using Wavelet Entropy and Eigenspace Features

Choudhary, Pharvesh Salman; Dandapat, Samarendra

doi:10.1109/aspcon49795.2020.9276680

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…Feature-based classification of myocardial infarction [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] requires a comprehensive understanding of the data, so that the algorithm is easy to understand and interpret; and its classification accuracy usually depends on the designed features. erefore, in order to obtain better recognition performance, some studies not only extract original physiological features but also use various techniques to extract advanced features [18][19][20][21]. Specifically, the RR interval [4], amplitude [22][23][24]26], area [22,27,28], and other original features are extracted from the electrocardiogram.…”

Section: Myocardial Infarction Classification Based On Featurementioning

confidence: 99%

Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features

Wen-zhi

Shen

et al. 2021

Journal of Healthcare Engineering

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Myocardial infarction (MI) is one of the most common cardiovascular diseases threatening human life. In order to accurately distinguish myocardial infarction and have a good interpretability, the classification method that combines rule features and ventricular activity features is proposed in this paper. Specifically, according to the clinical diagnosis rule and the pathological changes of myocardial infarction on the electrocardiogram, the local information extracted from the Q wave, ST segment, and T wave is computed as the rule feature. All samples of the QT segment are extracted as ventricular activity features. Then, in order to reduce the computational complexity of the ventricular activity features, the effects of Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), and Locality Preserving Projections (LPP) on the extracted ventricular activity features are compared. Combining rule features and ventricular activity features, all the 12 leads features are fused as the ultimate feature vector. Finally, eXtreme Gradient Boosting (XGBoost) is used to identify myocardial infarction, and the overall accuracy rate of 99.86% is obtained on the Physikalisch-Technische Bundesanstalt (PTB) database. This method has a good medical diagnosis basis while improving the accuracy, which is very important for clinical decision-making.

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Section: Myocardial Infarction Classification Based On Featurementioning

confidence: 99%

Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features

Wen-zhi

Shen

et al. 2021

Journal of Healthcare Engineering

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A Low Resource Machine Learning Approach for Prediction of Dressler Syndrome

Sengupta¹,

Mondal²,

Chatterjee³

et al. 2023

Lecture Notes in Electrical Engineering

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Community-Acquired Pneumonia Recognition by Wavelet Entropy and Cat Swarm Optimization

2022

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Aim) Community-acquired pneumonia (CAP) is a type of pneumonia acquired outside the hospital.(Methods) To recognize CAP more efficiently and more precisely, we propose a novel method-wavelet entropy (WE) is used as the feature extractor, and cat swarm optimization (shortened as CSO) is used to train an artificial neural network (ANN). Our method is abbreviated as WE-ANN-CSO. (Results) This proposed WE-ANN-CSO algorithm yields a sensitivity of 91.64±0.99%, a specificity of 90.64±2.11%, a precision of 90.96±1.81%, an accuracy of 91.14±1.12%, an F1 score of 91.29±1.04%, an MCC of 82.31±2.22%, an FMI of 91.29±1.03%, and an AUC of 0.9527.(Conclusions) This proposed WE-ANN-CSO algorithm provides better performances than four state-of-the-art approaches.

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An Evaluation of Machine Learning Classifiers for Detection of Myocardial Infarction Using Wavelet Entropy and Eigenspace Features

Cited by 4 publications

References 24 publications

Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features

Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features

A Low Resource Machine Learning Approach for Prediction of Dressler Syndrome

Community-Acquired Pneumonia Recognition by Wavelet Entropy and Cat Swarm Optimization

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