IMPROVING THE EFFICIENCY OF NUMERICAL PROCESSING ALGORITHMS FOR BIOMEDICAL SIGNALS IN ARTIFICIAL INTELLIGENCE

Abstract

The article explores methods for enhancing the efficiency of numerical processing algorithms used in analyzing biomedical signals within the framework of artificial intelligence (AI). Emphasis is placed on optimizing signal preprocessing, reducing computational complexity, and leveraging machine learning models for more accurate and real-time analysis. The study evaluates several algorithmic strategies and presents comparative results that demonstrate significant improvements in processing speed and diagnostic accuracy. This research is relevant to AI-based diagnostics, wearable health devices, and clinical decision-support systems.