FORECASTING BASED ON ARTIFICIAL INTELLIGENCE IN BIOSIGNAL PROCESSING
Keywords:
Artificial intelligence, biomedical signal processing, predictive analytics, deep learning, health monitoring, disease detection, biosignal forecasting, machine learning, neural networks, electrocardiogram, electroencephalogram, electromyogram, real-time analysis, personalized medicine, feature extraction, classification models.Abstract
Artificial intelligence (AI) has significantly advanced the field of biomedical signal processing, offering innovative approaches to diagnosing, monitoring, and predicting health conditions. The ability of AI to analyze complex patterns within biosignals, such as electrocardiograms (ECG), electroencephalograms (EEG), and electromyograms (EMG), enables more accurate and efficient medical assessments. This study explores the role of AI-driven forecasting models in biosignal processing, emphasizing their potential in early disease detection, personalized medicine, and real-time health monitoring. The increasing availability of large biomedical datasets and the development of deep learning techniques have contributed to substantial improvements in predictive analytics. However, challenges such as data quality, model interpretability, and regulatory compliance remain significant barriers to widespread adoption. This paper provides an overview of AI applications in biosignal prediction, reviews current methodologies, and discusses future directions in integrating AI-based forecasting into medical practice.
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