The healthcare system is undergoing a significant transformation, driven by advancements in digital health, including telecare, telehealth and telemedicine. These advances aim to address a range of challenges facing the healthcare system. Among emerging digital health technologies, portable devices play a crucial role by enabling continuous and personalized health monitoring, reducing the need for direct medical supervision. This study proposes the development of a portable device for the real-time monitoring of cardiovascular health through the acquisition of electrocardiogram (ECG) signals. The proposed system includes highly sensitive sensors such as the MAX30003 for ECG and the MAX30102 for PPG, both processed by an ESP32 microcontroller. The device is characterized by its compact, lightweight design and ease of use, requiring only simple finger placement on built-in electrodes to acquire signals. Data is acquired and processed using MATLAB for visualization and Neurokit2 for quantitative analysis. The system was validated through an experimental protocol involving 19 participants, comparing its performance to a standard medical-grade electrocardiograph. The results indicate promising accuracy in detecting PR intervals, QRS complex duration, and QT intervals, with percentage errors of 22.9%, 9%, and 27%, respectively, while the RR interval and BPM showed lower deviations of 11% and 10%. PR, QRS and RR intervals demonstrated good agreement with reference measurements but the QT interval exhibited greater variability, likely due to challenges in T-wave detection. Bland-Altman analysis confirmed these findings, highlighting the need for further refinement of T-wave identification algorithms. Future work will aim to enhance T-wave detection accuracy, refine signal processing, and validate the device on a larger population. Additionally, PPG signal evaluation will be explored to expand its applications in non-invasive cardiovascular monitoring.
Performance Validation of a Low-Cost Multisensor Device in the Healthy Aging Field / Bencivenga, Chiara; D'Alvia, Livio; Cangemi, Roberto; Basili, Stefania; Del Prete, Zaccaria. - (2025), pp. 1-6. (Intervento presentato al convegno 2025 IEEE Medical Measurements & Applications (MeMeA) tenutosi a Chania, Greece) [10.1109/memea65319.2025.11068066].
Performance Validation of a Low-Cost Multisensor Device in the Healthy Aging Field
Bencivenga, Chiara;D'Alvia, Livio;Cangemi, Roberto;Basili, Stefania;Del Prete, Zaccaria
2025
Abstract
The healthcare system is undergoing a significant transformation, driven by advancements in digital health, including telecare, telehealth and telemedicine. These advances aim to address a range of challenges facing the healthcare system. Among emerging digital health technologies, portable devices play a crucial role by enabling continuous and personalized health monitoring, reducing the need for direct medical supervision. This study proposes the development of a portable device for the real-time monitoring of cardiovascular health through the acquisition of electrocardiogram (ECG) signals. The proposed system includes highly sensitive sensors such as the MAX30003 for ECG and the MAX30102 for PPG, both processed by an ESP32 microcontroller. The device is characterized by its compact, lightweight design and ease of use, requiring only simple finger placement on built-in electrodes to acquire signals. Data is acquired and processed using MATLAB for visualization and Neurokit2 for quantitative analysis. The system was validated through an experimental protocol involving 19 participants, comparing its performance to a standard medical-grade electrocardiograph. The results indicate promising accuracy in detecting PR intervals, QRS complex duration, and QT intervals, with percentage errors of 22.9%, 9%, and 27%, respectively, while the RR interval and BPM showed lower deviations of 11% and 10%. PR, QRS and RR intervals demonstrated good agreement with reference measurements but the QT interval exhibited greater variability, likely due to challenges in T-wave detection. Bland-Altman analysis confirmed these findings, highlighting the need for further refinement of T-wave identification algorithms. Future work will aim to enhance T-wave detection accuracy, refine signal processing, and validate the device on a larger population. Additionally, PPG signal evaluation will be explored to expand its applications in non-invasive cardiovascular monitoring.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
