Sergio Olmos-Temois

Cardiovascular diseases are among the leading causes of mortality worldwide, making continuous electrocardiographic (ECG) monitoring essential for the early detection and follow-up of cardiac abnormalities. This study presents an Internet of Things (IoT) platform for continuous remote monitoring of three-lead ECG signals using LTE-M cellular connectivity. The proposed system integrates an ADS1293 analog front-end for ECG acquisition, a Walter IoT Module for MQTT-based data transmission, and a cloud infrastructure for secure data reception, storage, and visualization. A dual-core implementation on the ESP32 separates ECG acquisition from MQTT communication, preventing sample loss during continuous monitoring. Experimental results demonstrated reliable transmission of raw three-lead ECG signals, with no packet loss observed during a continuous 12 h LTE-M transmission test involving approximately 250,000 packets. The monitoring mechanism also automatically detected packet losses and device restarts. At the same time, server performance evaluation showed low CPU and memory utilization during simultaneous communication with five data sources under the evaluated conditions. These results validate the proposed IoT communication platform and establish a foundation for future work, including intelligent ECG analysis, improvements in signal acquisition and conditioning, and the continued development of the portable Holter device.

Coronary atherosclerosis is the most common form of cardiovascular diseases, which represent the leading global cause of mortality in the adult population. The amount of coronary artery calcium (CAC) is a robust predictor of this disease that can be measured using the medical workstations of computed tomography (CT) equipment or specialized tools included in commercial software for DICOM viewers, which is not available for all operating systems. This manuscript presents a web application that semiautomatically quantifies the amount of coronary artery calcium (CAC) on the basis of the coronary calcium score (CS) using the Agatston technique through digital image processing. To verify the correct functioning of this web application, 30 CTCSs were analyzed by a cardiologist and compared to those of commercial software (OsiriX DICOM Viewer).All the scans were correctly classified according to the cardiovascular event risk group, with an average error in the calculation of CS of 1.9% and a Pearson correlation coefficient r = 0.9997, with potential clinical application.