Intelligent Monitoring and Analysis System for the Early Detection of Cardiovascular Anomalies Based on IoT and Deep Learning

Mendoza Ramos D.; Vidal Falcon C.; Rosales Caururu A. · Communications in Computer and Information Science (2026) · DOI: 10.1007/978-3-032-16758-3_39

Resumen

Cardiovascular diseases (CVDs) represent a leading factor in global mortality rates, posing substantial challenges for healthcare systems worldwide, presenting significant healthcare challenges in Metropolitan Lima. In Peru, delays and limited technology within the healthcare system hinder the early detection and timely treatment of CVDs, contributing to elevated mortality rates. This paper presents an “Intelligent Monitoring and Alert System” designed to address these gaps through a mobile alert application and an integrated web platform powered by deep learning and IoT-driven data collection. The system leverages a ResNet model to classify ECG data in real time, with Fog Computing ensuring rapid processing of metrics from sensors, such as the AD8232. Initial results demonstrate robust performance, achieving an AUC of 0.84 for ECG classification from a single-lead. The system has undergone validation according to GAMP 5 standards, along with usability testing conducted with healthcare professionals and received favorable usability ratings (4.53/5), with results demonstrating the system’s effectiveness and utility. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Instituciones: Universidad Peruana de Ciencias Aplicadas