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September 10, 2025Engineering Technology & Applied Science ResearchOpen Access

A Comparative Analysis of Advanced Deep Learning Techniques for Accurate Cardiac Arrhythmia Classification

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The proposed model achieved 99.53% accuracy in classifying cardiac arrhythmias, demonstrating high reliability in clinical settings.
Utilizing advanced deep learning techniques, the study addresses challenges posed by ECG signal noise and patient-specific variations.
A novel framework incorporating various methods, including filtering and clustering, improves feature selection and model performance.
Testing on the St. Petersburg INCART dataset indicates the model's effectiveness in real-time arrhythmia detection, suggesting practical applications.

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Implication

This study demonstrates a deep learning framework for improved cardiac arrhythmia classification accuracy, highlighting noise reduction and feature selection.

Cite This Study

Pratima et al. (2025) studied this question.

synapsesocial.com/papers/68c1ac0954b1d3bfb60e4a39 https://doi.org/10.48084/etasr.11333

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Implication

This study demonstrates a deep learning framework for improved cardiac arrhythmia classification accuracy, highlighting noise reduction and feature selection.

A Comparative Analysis of Advanced Deep Learning Techniques for Accurate Cardiac Arrhythmia Classification

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Implication

Cite This Study

A Comparative Analysis of Advanced Deep Learning Techniques for Accurate Cardiac Arrhythmia Classification

Key Points

Discussion

Implication

Cite This Study

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