A Transfer-Learning Based Ensemble Architecture for ECG Signal Classification

Ovi, Tareque Bashar; Naba, Sauda Suara; Chanda, Dibaloke; Onim, Md. Saif Hassan

doi:10.48550/arxiv.2207.00002

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This strategy, typically deployed within the deep learning paradigm, leverages existing knowledge from pre-trained models on extensive datasets, transferring this knowledge to the target task and significantly aiding the learning process [46]. Transfer learning has shown potential in improving model performance and reducing computational costs [47,48]. However, the incorporation of transfer learning within the ECG classification framework is an active research area with numerous untapped opportunities and challenges.…”

Section: Related Workmentioning

confidence: 99%

Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach

Benchaira,

Bitam

2024

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

Rapid and accurate electrocardiogram (ECG) signal classification is crucial in high-stakes healthcare settings. However, existing computational models often struggle to balance high performance with computational efficiency. This study introduces an innovative computational framework that combines transfer learning with traditional machine learning to optimize ECG classification. We use a pre-trained Stacked Convolutional Neural Network (SCNN) to generate high-dimensional feature embeddings, which are then evaluated by an array of machine learning classifiers. Our models demonstrate exceptional performance, particularly when utilizing embeddings from SCNNs trained on diverse datasets. This underscores the importance of data diversity in improving classifier discrimination. Notably, Multilayer Perceptrons (MLPs) stand out for their ability to balance computational efficiency with strong performance, achieving test F1-scores of 0.94 and 1.00 in multi-class and binary tasks on the CinC2017 dataset, and 0.85 and 0.99 on the CPSC2018 dataset. Our approach consistently outperforms existing methods, setting new benchmarks in ECG classification. The synergy between deep learning-based feature extraction and traditional machine learning through transfer learning offers a robust, efficient, and adaptable strategy for ECG classification, addressing a critical research gap and laying the groundwork for future advancements in this crucial healthcare field.

show abstract

Section: Related Workmentioning

confidence: 99%