Anomaly detection in electrocardiogram signals using metaheuristic optimized time-series classification with attention incorporated models.

Efforts in cardiovascular disorder detection demand immediate attention as they hold the potential to revolutionize patient outcomes through early detection systems. The exploration of diseases and treatments, coupled with the potential of artifical intelligence to reshape healthcare, highlights a promising avenue for innovation. AI-driven early detection systems offer substantial benefits by improving quality of life and extending longevity through timely interventions for chronic diseases. The evolving landscape of healthcare algorithms presents vast possibilities, particularly in the application of metaheuristics to address complex challenges. An exemplary approach involves employing metaheuristic solutions such as PSO, FA, GA, WOA, and SCA to optimize an RNN for anomaly detection using ECG systems. Despite commendable outcomes in the best and median case scenarios, the study acknowledges limitations, focusing on a narrow comparison of optimization algorithms and exploring RNN capabilities for a specific problem. Computational constraints led to the use of smaller populations and limited rounds, emphasizing the need for future research to transcend these boundaries. Significantly, the introduction of attention layers emerges as a transformative element, enhancing neural network performance. The introduced optimizer proves robust across test scenarios, effectively navigating local minimum traps. Attention layers contribute to a substantial performance boost, reducing the error rate from 0.006837 to an impressive 0.002486, underscoring their role in focusing on pertinent information. This abstract advocates for further research to expand beyond these limitations, exploring novel algorithms and addressing broader medical challenges in the pursuit of refined and advanced solutions.