Automated detection of myocardial infarction using binary Harry Hawks feature selection and ensemble KNN classifier-Reference-Cited by-同舟云学术

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Automated detection of myocardial infarction using binary Harry Hawks feature selection and ensemble KNN classifier

Published:2023-10-20 Issue: Volume: Page:1-17
ISSN:1025-5842
Container-title:Computer Methods in Biomechanics and Biomedical Engineering
language:en
Short-container-title:Computer Methods in Biomechanics and Biomedical Engineering

Author:

Chaitanya M. Krishna¹,Sharma Lakhan Dev¹

Affiliation:

1. School of Electronics Engineering, VIT-AP University, Amaravati, India

Publisher

Informa UK Limited

Subject

Computer Science Applications,Human-Computer Interaction,Biomedical Engineering,General Medicine,Bioengineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/10255842.2023.2270101

Reference73 articles.

1. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals

2. Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads

3. Al-Ani A, Khushaba RN. 2012. A population based feature subset selection algorithm guided by fuzzy feature dependency. In: Advanced Machine Learning Technologies and Applications: first International Conference, AMLTA 2012, Cairo, Egypt; December 8–10. Springer. p. 430–438.

4. Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities

5. Al-Kindi SG, Ali F, Farghaly A, Nathani M, Tafreshi R. 2011. Towards real-time detection of myocardial infarction by digital analysis of electrocardiograms. In: 2011 1st Middle East Conference on Biomedical Engineering. IEEE. p. 454–457.

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