Giraffe: A Genetic Programming Algorithm To Build Deep Learning Ensembles For ECG Arrhythmia Classification

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@InProceedings{Kucharski:2024:ICIP,

• author = "Damian Kucharski and Agata M. Wijata and Lu Fu and Weidong Lin and Yumei Xue and Jacek Kawa and Yalin Zheng and Y. H. Gregory Lip and Jakub Nalepa",
• title = "Giraffe: A Genetic Programming Algorithm To Build Deep Learning Ensembles For {ECG} Arrhythmia Classification",
• booktitle = "2024 IEEE International Conference on Image Processing (ICIP)",
• year = "2024",
• pages = "3070--3076",
• month = oct,
• keywords = "genetic algorithms, genetic programming, Deep learning, ANN, Training, Analytical models, Arrhythmia, Manuals, Electrocardiography, Ensemble Learning, ECG Arrhythmia Classification, CVD",
• ISSN = "2381-8549",
• DOI = "doi:10.1109/ICIP51287.2024.10647780",
• abstract = "Cardiovascular diseases remain one of the leading causes of death worldwide. Therefore, developing and validating automated tools to help identify high-risk patients are of paramount clinical utility. In this article, we tackle this task and introduce a genetic programming algorithm (called GIRAFFE) to build (deep) machine learning classification ensembles for arrhythmia classification from two-dimensional images of 12-lead electrocardiogram (ECG) tracings. GIRAFFE evolves the architecture, content, and fusion scheme of the ensemble, to obtain an accurate yet lightweight classification system. The experimental study performed over a large-scale dataset of ECG images revealed that our approach outperforms other ensemble methods and carefully fine-tuned deep models, elaborates compact heterogeneous ensembles, and does not require any user intervention hence it is easy to apply to other classification tasks.",
• notes = "Also known as \cite{10647780}",

}

Genetic Programming entries for Damian Kucharski Agata M Wijata Lu Fu Weidong Lin Yumei Xue Jacek Kawa Yalin Zheng Gregory Lip Jakub Nalepa

Citations