Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques

Abstract

This review offers a detailed examination of modern ECG signal processing techniques employed in the prediction and detection of ventricular fibrillation (VF). It contains a thorough analysis of recent advancements in the field, exploring the strengths, limitations, and real-world applications of these techniques. By evaluating the current state of research, the review seeks to identify the most effective approaches and highlight key areas where further investigation is needed, ultimately guiding future research efforts toward improving VF prediction and detection. Overall, AI has shown significant potential in a range of VF-related tasks. However, real-world implementation encounters several challenges, including difficulties in accurately interpreting ECG signals, the variability in individual physiological responses, and the infrequency of ventricular fibrillation events. Additionally, there are issues related to the critical timing required for detecting VF, the presence of similar arrhythmias, the need for adaptation to new ECG devices, energy consumption concerns, and the complex process of obtaining regulatory and legislative approvals for integrating software components into medical equipment. We consider that the present work might be useful in approaching the above challenges.

Keywords

• ventricular fibrillation
• features extraction
• ensemble classifiers
• machine learning
• detection
• prediction