Deep Learning-Based Detection of Arrhythmia Using ECG Signals - A Comprehensive Review.

Area of Science:

• Cardiology and Artificial Intelligence
• Medical Signal Processing
• Machine Learning in Healthcare

Background:

• Cardiac arrhythmias pose a significant global health risk, necessitating accurate and timely diagnosis.
• Traditional arrhythmia detection methods face challenges in accuracy and efficiency.
• Deep learning offers advanced capabilities for analyzing complex biomedical signals like ECG.

Purpose of the Study:

• To review and evaluate the application of deep learning techniques for arrhythmia detection using ECG.
• To analyze current trends, methodologies, and performance of deep learning models in this field.
• To identify limitations and future research directions for AI-driven cardiac arrhythmia diagnosis.

Main Methods:

• Systematic review of 30 research papers on deep learning for ECG-based arrhythmia detection.
• Analysis of various deep learning architectures, including Convolutional Neural Networks (CNNs) and hybrid models (CNN-RNN).
• Evaluation of model performance metrics such as accuracy and F1 scores.

Main Results:

• Deep learning models demonstrate exceptional performance, achieving accuracy up to 99.93% and F1 scores up to 99.57%.
• Convolutional Neural Networks (CNNs) and hybrid CNN-RNN architectures are prominent.
• Key challenges include dataset variability, model interpretability, and real-time implementation.

Conclusions:

• Deep learning is a highly effective tool for accurate cardiac arrhythmia detection from ECG.
• Further research is needed to address limitations for widespread clinical adoption.
• This technology holds significant potential to enhance cardiac healthcare and patient outcomes.