A novel adaptive modelling and filtering method achieved high-fidelity signal reconstruction and pattern learning for quasi-periodic signals, including cardiac cycle measurements.
A novel signal processing technique effectively suppresses measurement noise in quasi-periodic signals, with potential applications in cardiac cycle monitoring using quantum sensors.
We present a novel approach to processing periodic signals with non-stationary fundamental frequency. These quasi-periodic signals feature a perpetually recurring underlying signal pattern and arise in various fields of science and engineering. The proposed method integrates a recursive extraction of the signal pattern with dynamic tracking of the instantaneous phase to effectively suppress measurement noise without prior knowledge of the signal characteristics or the frequency variation. The performance is showcased both in simulation and using experimental measurements of the cardiac cycle obtained by a nitrogen-vacancy diamond quantum sensor. Overall, high-fidelity signal reconstruction and convincing pattern learning is achieved, even in the presence of complex non-linear disturbances and non-Gaussian noise. Conclusively, the proposed technique constitutes a flexible and efficient solution, addressing limitations of existing methods and offering real-world applicability.
Corcione et al. (Mon,) conducted a other in Periodic signals with non-stationary fundamental frequency. Adaptive modelling and filtering method was evaluated on Signal reconstruction and pattern learning. A novel adaptive modelling and filtering method achieved high-fidelity signal reconstruction and pattern learning for quasi-periodic signals, including cardiac cycle measurements.
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