Systematic Review and Primary Outcomes in Arrhythmia Detection via Evolutionary Optimization

The accurate detection and classification of arrhythmias from electrocardiogram (ECG) signals is a critical challenge in clinical cardiology, with current models having limitations in sensitivity, computational efficiency, and generalizability. This study introduces the Human Evolutionary Liquid-Neural Network (HEL-Net), an advanced deep learning model that addresses these issues by combining the Human Evolutionary Optimization Algorithm (HEOA) and Liquid Neural Networks (LNN). HEL-Net uses HEOA to optimize feature selection, ensuring that only the most relevant features are used, thereby increasing the model’s efficiency and accuracy. Meanwhile, LNN processes dynamic temporal sequences, capturing the subtle variations found in arrhythmia signals. Initial results show that the model has the potential to provide high sensitivity and specificity in arrhythmia detection, and this is supported by a comprehensive methodological approach to feature optimization and classification. The paper reviews recent advancements, identifies gaps in current technologies, and presents preliminary findings that demonstrate HEL-Net’s effectiveness in clinical settings.