Dispositivo portátil para la detección temprana de arritmias cardiacas en adultos mayores mediante técnicas de inteligencia artificial

Abstract

Cardiovascular diseases are one of the leading causes of death worldwide, with cardiac arrhythmias being a significant factor in many of these cases. Early detection of these irregularities is crucial to prevent serious complications and improve patients' quality of life. This research focuses on the development of a portable device for the early detection of cardiac arrhythmias, combining advanced biomedical module technologies, signal processing, and machine learning models. The device uses an LSTM autoencoder-decoder model to analyze electrocardiogram (ECG) signals in real time, identifying abnormal patterns in time series and distinguishing between normal and arrhythmic heart rhythms. The high-quality ECG5000 database was used for training and validating the model. The research includes key stages such as data acquisition, preprocessing to reduce noise and artifacts, model implementation, and validation testing in a controlled environment. Preliminary results show that the device is capable of detecting arrhythmias with high accuracy and speed, making it a valuable tool for remote monitoring and the prevention of cardiovascular events. This project not only contributes to technical advancements but also provides an accessible solution for cardiac health monitoring, improving the ability of patients and healthcare professionals to address cardiovascular conditions promptly and efficiently.