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Updated: Jul 5, 2026
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Published on: August 30, 2013
Enhanced automatic artifact detection based on independent component analysis and Renyi's entropy
Nadia Mammone1, Francesco Carlo Morabito
1DIMET, University of Reggio Calabria, Italy. nadia.mammone@unirc.it
Summary
This study introduces a new method using Independent Component Analysis (ICA) and Renyi
Area of Science:
- Neuroscience
- Signal Processing
- Biomedical Engineering
Background:
- Electroencephalographic (EEG) recordings are susceptible to artifacts, which are disturbances during signal acquisition.
- These artifacts can significantly impact the accuracy of EEG signal processing and interpretation.
- Accurate artifact detection is crucial for reliable EEG analysis in various research and clinical applications.
Purpose of the Study:
- To propose and evaluate a novel technique for the automatic detection of artifacts in EEG recordings.
- To compare the performance of the new technique against established artifact detection methods.
- To assess the capability of Renyi's entropy in identifying and discriminating different types of artifacts.
Main Methods:
- A new artifact detection technique combining Independent Component Analysis (ICA) for artifactual signal extraction and Renyi's entropy for automatic detection.
- Comparison with a conventional approach utilizing ICA along with kurtosis and Shannon's entropy.
- Evaluation performed on a specific database of EEG recordings.
Main Results:
- The novel technique demonstrated an average detection rate of 92.6% for artifactual signals.
- This significantly outperforms the previous technique, which achieved an average detection rate of 68.7%.
- Renyi's entropy effectively identified muscle artifacts and very low-frequency activity, distinguishing them from other artifact types.
Conclusions:
- The proposed method significantly improves automatic artifact detection in EEG compared to existing approaches.
- Renyi's entropy offers a robust measure for identifying and classifying diverse EEG artifacts.
- Future work will focus on enhancing blind artifact separation to minimize information loss and improve artifact rejection efficiency.
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