Quadratic variation (QV) regularization is a recent method for BW removal but cannot be used for PLI cancellation. To overcome this limitation, an extension of QV regularization which can simultaneously deal with BW and PLI tracking and removal is presented. In the proposed method, PLI and BW are respectively modeled by a sinusoidal and a polynomial function. The difference equation of the sinusoidal function and the p-th order derivative of the polynomial function are then used as constrains in the optimization problem. The proposed approach is also implemented using Kalman filter and smoother which is an optimal estimator in mean square error (MSE) sense. We tested the method over data from the PhysioNet PTB database. Simulation results confirm the effectiveness of the approach and highlight its ability to simultaneously track and remove the PLI and BW.

An Extension of Quadratic Variation Regularization for Simultaneous Baseline Wander and Power Line Interference Removal from ECG / A. Kheirati Roonizi, R. Sassi - In: Computing in Cardiology 2022[s.l] : IEEE Computer Society, 2022. - ISBN 979-8-3503-0097-0. - pp. 1-4 (( Intervento presentato al 49. convegno Computing in Cardiology, CinC 2022 tenutosi a Tampere nel 2022 [10.22489/CinC.2022.158].

An Extension of Quadratic Variation Regularization for Simultaneous Baseline Wander and Power Line Interference Removal from ECG

A. Kheirati Roonizi
Primo
;
R. Sassi
Ultimo
2022

Abstract

Quadratic variation (QV) regularization is a recent method for BW removal but cannot be used for PLI cancellation. To overcome this limitation, an extension of QV regularization which can simultaneously deal with BW and PLI tracking and removal is presented. In the proposed method, PLI and BW are respectively modeled by a sinusoidal and a polynomial function. The difference equation of the sinusoidal function and the p-th order derivative of the polynomial function are then used as constrains in the optimization problem. The proposed approach is also implemented using Kalman filter and smoother which is an optimal estimator in mean square error (MSE) sense. We tested the method over data from the PhysioNet PTB database. Simulation results confirm the effectiveness of the approach and highlight its ability to simultaneously track and remove the PLI and BW.
Settore INF/01 - Informatica
Settore ING-INF/06 - Bioingegneria Elettronica e Informatica
2022
IEEE
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/969082
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