research-article

A novel automated CNN arrhythmia classifier with memory-enhanced artificial hummingbird algorithm

Authors:

Evren Kıymaç,

Yasin KayaAuthors Info & Claims

Volume 213, Issue PC

https://doi.org/10.1016/j.eswa.2022.119162

Published: 01 March 2023 Publication History

Abstract

Cardiac arrhythmias indicate cardiovascular disease which is the leading cause of mortality worldwide, and can be detected by an electrocardiogram (ECG). Automated deep learning methods have been developed to overcome the disadvantages of manual interpretation by medical experts. The performance of the networks strongly depends on hyperparameter optimization (HPO), and this NP-hard problem is suitable for metaheuristic (MH) methods. In this study, a novel method is proposed for the HPO of a convolutional neural network (CNN) arrhythmia classifier using an MH algorithm. The approach utilizes our variant of an MH method, named the memory-enhanced artificial hummingbird algorithm, which has an additional memory unit that stores the evaluations of the solutions and reduces the computation time significantly. The study also proposes a novel fitness function that considers both the accuracy rate and the total number of parameters of each candidate network. Experiments were conducted on raw ECG samples from the MIT-BIH arrhythmia database. The proposed method was compared with five other MH methods and achieved equal or outperforming results, with classification accuracy reaching 98.87%. The proposed method yielded promising results in finding a high-performing solution with relatively lower complexity.

Highlights

•

A novel memory-enhanced AHA-based automated CNN arrhythmia classifier is proposed.

•

A new fitness function is developed to evaluate hyperparameters of candidate CNNs.

•

The proposed method is utilized to classify cardiac arrhythmia on raw ECG samples.

•

The obtained results are promising compared to other state-of-the-art methods.

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Cited By

Gürsoy EKaya Y(2025)Multi-source deep feature fusion for medical image analysisMultidimensional Systems and Signal Processing10.1007/s11045-024-00897-z36:1Online publication date: 1-Dec-2025
https://dl.acm.org/doi/10.1007/s11045-024-00897-z
Gürsoy EKaya Y(2024)Brain-GCN-NetComputers in Biology and Medicine10.1016/j.compbiomed.2024.108971180:COnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108971
Kaya YTopuz E(2024)Human activity recognition from multiple sensors data using deep CNNsMultimedia Tools and Applications10.1007/s11042-023-15830-y83:4(10815-10838)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15830-y

Index Terms

A novel automated CNN arrhythmia classifier with memory-enhanced artificial hummingbird algorithm
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Information

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 213, Issue PC

Mar 2023

1402 pages

ISSN:0957-4174

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Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 March 2023

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Cited By

Gürsoy EKaya Y(2025)Multi-source deep feature fusion for medical image analysisMultidimensional Systems and Signal Processing10.1007/s11045-024-00897-z36:1Online publication date: 1-Dec-2025
https://dl.acm.org/doi/10.1007/s11045-024-00897-z
Gürsoy EKaya Y(2024)Brain-GCN-NetComputers in Biology and Medicine10.1016/j.compbiomed.2024.108971180:COnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108971
Kaya YTopuz E(2024)Human activity recognition from multiple sensors data using deep CNNsMultimedia Tools and Applications10.1007/s11042-023-15830-y83:4(10815-10838)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15830-y
Kaya YGürsoy E(2023)RETRACTED ARTICLE: A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detectionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07798-y27:9(5521-5535)Online publication date: 4-Jan-2023
https://dl.acm.org/doi/10.1007/s00500-022-07798-y

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