A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques.

Area of Science:

• Cardiology
• Medical Informatics
• Artificial Intelligence

Background:

• Diagnosing complex conditions like arrhythmia is challenging due to overlapping symptoms.
• Electrocardiogram (ECG) examinations are crucial for cardiac rhythm analysis.
• Artificial intelligence (AI) offers advanced tools for medical diagnosis and prognosis.

Purpose of the Study:

• To review and synthesize recent research (last 12 years) on AI-driven arrhythmia prediction.
• To analyze the application of machine learning and deep learning in classifying heartbeat rhythms.
• To identify trends and challenges in AI for arrhythmia diagnosis.

Main Methods:

• Systematic literature review of 40 studies from academic databases.
• Analysis of AI methodologies employed, categorizing them into deep learning, machine learning, and hybrid approaches.
• Focus on studies utilizing ECG data for automatic heartbeat rhythm classification.

Main Results:

• Deep learning methods were predominant, applied in 72.5% of the reviewed studies.
• Machine learning methods were used in 22.5% of studies, with 5% combining both.
• AI shows significant potential in improving the accuracy and efficiency of arrhythmia diagnosis.

Conclusions:

• AI, particularly deep learning, is a rapidly advancing field for arrhythmia diagnosis using ECG.
• Challenges remain in AI model interpretability and computational resource requirements.
• Future advancements in cloud computing and quantum AI promise further breakthroughs in cardiac rhythm analysis.