Diseño e implementación de un sistema biomédico para la clasificación de enfermedades cardiopulmonares mediante el análisis de señales acústicas usando Machine Learning

Abstract

This research work focuses on the design and implementation of a biomedical system for the classification of cardiopulmonary diseases through the analysis of acoustic signals using advanced machine learning techniques. This study arises from the need to improve the precision in cardiac and pulmonary auscultation, especially for health professionals in training and at the early stages of their careers. The proposed system seeks to overcome the limitations of traditional diagnostic methods, providing an accurate and reliable tool for the early detection of cardiopulmonary conditions. The development of the system includes the collection and analysis of cardiac and pulmonary acoustic signals, the extraction of relevant features, and the implementation of machine learning algorithms for effective diseases classification. Signal processing techniques were employed to improve the quality of the acoustic data and suitable machine learning models were selected fpalaor the classification task. The system was tested and validated against a labeled data set, demonstrating its ability to accurately identify various cardiopulmonary conditions. The results demonstrated that the developed biomedical system is a valuable tool to improve diagnostic accuracy in medical practice. Furthermore, this study contributes significantly to the field of biomedical engineering and medicine, opening new oportunities for research and development of advanced diagnostic technologies. The implementation of this system has the potential to improve the quality of medical care and support the training of health professionals in the accurate interpretation of heart and lung sounds.