Independence on the lead of the identification of the ventricular depolarization in the electrocardiogram in wearable devices

• The use of Q- and R-waves as time reference is prevented by non-standard leads in wearable devices • We propose an energy-based detection of the peak and onset of the ventricular depolarization • Energy-based detection decreases the lead-dependent error by up to 82% vs morphological-based detection • The achieved consistency across leads (< 6 ms) is compatible with clinical monitoring • The method can be implemented on microcontroller-based devices due to its low computational cost The home monitoring of cardiac time intervals reduces hospitalization and mortality of cardiovascular patients. However, a reliable time reference in the electrocardiogram is necessary. Nevertheless, the use of different single leads, typical of wearable devices, impacts the repeatability of the time reference and thus the accuracy of the time-dependent parameters. This work proposes a simple approach to detect the peak and onset of the ventricular depolarization, and demonstrates its lead independence, which makes it suitable for wearable devices even with non-standard leads. Our method grounds on an energy-based approach, which we applied on a) a publicly available dataset with standard 12-lead recordings; b) a proof-of-concept dataset including a custom precordial non-standard lead implemented on a wearable device. Compared against the Pan-Tompkins algorithm, our method reduced the absolute error between each lead and the first standard lead by 26% to 64% for the peak, and by 70% to 82% for the onset detection. The achieved consistency across leads is compatible with clinical monitoring. The computational time was also reduced by 65% to 96%, making the algorithm suitable for use on microcontroller-based wearable devices. The proposed method enables the identification of a stable reference of the ventricular depolarization regardless of the choice of the lead. The presented results open to the implementation on wearable devices for chronic disease monitoring purposes.