In the paper, authors discuss the development and performance evaluation of an e-health Android App, which allows to monitor and classify ECG-related features. The mobile platform is composed of a wearable and non-obtrusive sensor and a smartphone, which collects and process data during the daily life. The main goal of this work has been the development of a real-time, wireless monitoring tool that is not related to an indoor environment. Five physiological quantities are continuously measured: Heart Rate (HR) and 4 ECG time intervals (i.e., QT, ST, PR, QR); together with the complete ECG waveform, these parameters are displayed in real-time, while the user is performing different activities, indoor or outside. A visual feedback (i.e., colour bars, according to the level of the measured quantities) is provided to the user, together with the possibility to save data for further analysis. A pilot study has been conducted to evaluate the accuracy and the uncertainty of the data computed in real-time by the mobile application, to those derived from a reference system (i.e., ECG signal coming from a standard electrocardiograph), in post-processing. Results have shown a good agreement with gold standard instrumentation. The Bland-Altman test has identified a high correlation between the data computed by the App with respect to the ones from the gold standard (i.e., R-2 equal or higher than 90%), with a slight underestimation of the time intervals (3.6 +/- 1.5 ms for QT, 4.1 +/- 1.7 ms for ST, 3.0 +/- 1.2 ms for QR and 11.9 +/- 1.9 ms for PR interval).
Cardiac Activity Classification using an E-Health App for a Wearable Device / Pirozzi, M.; Pietroni, F.; Casaccia, S.; Scalise, L.; Revel, G. M.. - ELETTRONICO. - (2018), pp. 1-6. (Intervento presentato al convegno 13th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2018 tenutosi a Universita La Sapienza, ita nel 2018) [10.1109/MeMeA.2018.8438674].
Cardiac Activity Classification using an E-Health App for a Wearable Device
Pirozzi, M.;Pietroni, F.;Casaccia, S.;Scalise, L.;Revel, G. M.
2018-01-01
Abstract
In the paper, authors discuss the development and performance evaluation of an e-health Android App, which allows to monitor and classify ECG-related features. The mobile platform is composed of a wearable and non-obtrusive sensor and a smartphone, which collects and process data during the daily life. The main goal of this work has been the development of a real-time, wireless monitoring tool that is not related to an indoor environment. Five physiological quantities are continuously measured: Heart Rate (HR) and 4 ECG time intervals (i.e., QT, ST, PR, QR); together with the complete ECG waveform, these parameters are displayed in real-time, while the user is performing different activities, indoor or outside. A visual feedback (i.e., colour bars, according to the level of the measured quantities) is provided to the user, together with the possibility to save data for further analysis. A pilot study has been conducted to evaluate the accuracy and the uncertainty of the data computed in real-time by the mobile application, to those derived from a reference system (i.e., ECG signal coming from a standard electrocardiograph), in post-processing. Results have shown a good agreement with gold standard instrumentation. The Bland-Altman test has identified a high correlation between the data computed by the App with respect to the ones from the gold standard (i.e., R-2 equal or higher than 90%), with a slight underestimation of the time intervals (3.6 +/- 1.5 ms for QT, 4.1 +/- 1.7 ms for ST, 3.0 +/- 1.2 ms for QR and 11.9 +/- 1.9 ms for PR interval).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
