APPLICATION OF ARTIFICIAL INTELLIGENCE IN BIOPHYSICAL SIGNAL ANALYSIS - Yorqin Sattarov; Zuhra Shonazarova (Web of Journals Publishing, 2025)
This study explores the role and potential of artificial intelligence (AI) in the analysis of biophysical signals. Biophysical signals-such as heart rate, brain activity, and muscle electrical signals-are complex data derived from various physiological systems that require advanced methods for accurate and efficient interpretation. AI technologies, particularly machine learning and deep learning algorithms, demonstrate high effectiveness in the automatic detection, classification, and prediction of these signals. The application of AI enhances noise reduction, feature extraction, and early detection of pathological conditions in biophysical signals, thereby improving clinical diagnosis and treatment processes. Additionally, AI enables the processing of large-scale datasets, facilitating the development of personalized medicine and real-time monitoring systems. This research reviews the key AI algorithms used in biophysical signal analysis, their practical applications, and discusses current challenges and promising future directions in the field.
Keywords: artificial intelligence, biophysical signal analysis, machine learning, deep learning, physiological signals, signal processing, clinical diagnosis, pattern recognition, noise reduction, personalized medicine, real-time monitoring, biomedical engineering.