A novel enhanced lemurs optimization-based hybrid attention network for classifying arrthymia from ECG signal using optimal weighted feature-Reference-Cited by-同舟云学术

A novel enhanced lemurs optimization-based hybrid attention network for classifying arrthymia from ECG signal using optimal weighted feature

Published:2024-03-25 Issue:3 Volume:21 Page:311-335
ISSN:1448-837X
Container-title:Australian Journal of Electrical and Electronics Engineering
language:en
Short-container-title:Australian Journal of Electrical and Electronics Engineering

Author:

Wajgi Rakhi¹,Brammya G.²,Ramesh Gopisetty³,Deotare Vilas Vasudeo⁴,Govindasamy Chinnaraj⁵,Preetha N.S. Ninu²

Affiliation:

1. Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, India

2. Department of Electronics and Communication Engineering, Resbee Info Technologies Private Limited, Chennai, India

3. Department of EIE, CVR College of Engineering, Mangalpalli, India

4. Professor in E&TC, Savitribai Phule Pune University, Pune, India

5. Department of Computer Science and Engineering, Saveetha School of Engineering, Kuthambakkam, India

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/1448837X.2024.2326272

Reference47 articles.

1. Lemurs Optimizer: A New Metaheuristic Algorithm for Global Optimization

2. Adib, E., F. Afghah, J. J. Prevost. 2023. “Arrhythmia Classification Using CGAN-Augmented ECG Signals.” 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

3. Detection and classification of arrhythmia type using hybrid model of LSTM with convolutional neural network

4. Classification of mechanisms underlying cardiac arrhythmias by deep learning

5. Constrained transformer network for ECG signal processing and arrhythmia classification

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