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Related Experiment Video

Updated: May 25, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Baseline wander estimation and removal by quadratic variation reduction.

A Fasano¹, V Villani, L Vollero

¹Faculty of Engineering, Università Campus Bio-Medico di Roma, via Álvaro del Portillo 21, 00128 Rome, Italy. a.fasano@unicampus.it

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 19, 2012

Summary

This study introduces a new method for removing baseline wander, a common noise in electrocardiogram (ECG) signals. The algorithm effectively estimates and removes this interference, improving signal quality for real-time applications.

Related Experiment Videos

Last Updated: May 25, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

Biomedical Engineering
Signal Processing
Medical Informatics

Background:

Baseline wander is low-frequency noise that interferes with electrocardiogram (ECG) signals.
Removing baseline wander without distorting the ECG signal is challenging.

Purpose of the Study:

To develop a novel algorithm for estimating and removing baseline wander from ECG signals.
To create an efficient method suitable for real-time processing.

Main Methods:

Utilized the concept of quadratic variation as an index of variability.
Developed a constrained convex optimization problem for baseline estimation.
Algorithm complexity is linear, enabling real-time application.

Main Results:

Simulation results demonstrate the effectiveness of the proposed approach.
The algorithm successfully removes baseline wander from ECG signals.
The method is applicable to other biosignals like electroencephalogram (EEG).

Conclusions:

The proposed quadratic variation-based method offers an effective solution for baseline wander removal.
The algorithm's efficiency makes it suitable for real-time ECG and EEG analysis.
This technique provides a robust tool for biosignal processing where baseline estimation is critical.