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Updated: May 24, 2026
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Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
Published on: July 26, 2013
Artifact removal in physiological signals--practices and possibilities
Kevin T Sweeney1, Tomás E Ward, Seán F McLoone
1Department of Electronic Engineering, National University of Ireland, Maynooth, Ireland. ksweeney@eeng.nuim.ie
Summary
Aging populations increase healthcare burdens, driving demand for remote health technologies. This paper reviews in-home physiological data artifacts and removal techniques to improve remote patient monitoring.
Area of Science:
- Biomedical Engineering
- Health Informatics
- Signal Processing
Background:
- Global demographic shifts towards aging populations increase healthcare demands.
- The transition from hospital-centric to in-home health assessments is crucial for managing healthcare burdens and improving patient comfort.
- Advances in technology enable reliable in-home physiological data collection, but artifacts pose a significant challenge.
Purpose of the Study:
- To review physiological signals commonly recorded in home settings.
- To identify frequent and impactful artifacts in home-based physiological data.
- To analyze current artifact detection and removal techniques for personal healthcare applications.
Main Methods:
- Literature review of physiological signals in home healthcare.
- Documentation of common artifacts and their impact on signal quality.
- Detailed analysis and evaluation of existing artifact removal techniques.
Main Results:
- Identified key physiological signals for home monitoring.
- Characterized the types and frequency of artifacts encountered in home environments.
- Presented a comprehensive overview of current artifact removal strategies.
Conclusions:
- Effective artifact management is essential for the success of in-home health monitoring.
- Further research into advanced signal processing techniques is needed to enhance the reliability of remote physiological data.
- The findings provide a foundation for developing robust personal healthcare technologies.
