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Automatic motion and noise artifact detection in Holter ECG data using empirical mode decomposition and statistical
Jinseok Lee1, David D McManus, Sneh Merchant
1Department of Biomedical Engineering, Worcester Polytechnic Institute, MA 01609, USA. jinseok@wpi.edu
IEEE Transactions on Bio-Medical Engineering
|November 17, 2011
Summary
This study introduces a real-time method to detect motion and noise (MN) artifacts in electrocardiogram (ECG) signals from Holter monitors. The approach accurately identifies artifacts, improving rhythm assessment and atrial fibrillation detection.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Cardiology
Background:
- Motion and noise (MN) artifacts frequently compromise the accuracy of rhythm assessment in electrocardiogram (ECG) signals obtained from ambulatory Holter monitoring.
- Existing methods may struggle with real-time artifact detection, hindering immediate data quality assessment and subsequent analysis.
Purpose of the Study:
- To develop and validate a real-time method for detecting motion and noise (MN) artifacts in Holter monitor ECG signals.
- To improve the accuracy of cardiac rhythm assessment, specifically atrial fibrillation (AF) detection, by effectively removing artifact-corrupted segments.
Main Methods:
- A two-stage artifact detection approach utilizing empirical mode decomposition (EMD) and statistical analysis.
- Stage 1: Isolation of artifact dynamics using the first-order intrinsic mode function (F-IMF) derived from EMD.
- Stage 2: Application of Shannon entropy, mean, and variance on the F-IMF time series to identify artifactual randomness and variability.
Main Results:
- The developed algorithm achieved high detection performance for MN artifacts, with a sensitivity of 96.63% and specificity of 94.73% on independent datasets.
- Application of the artifact detection method significantly improved atrial fibrillation (AF) detection specificity from 73.66% to 85.04% without compromising sensitivity.
- The algorithm's computation time was less than 0.2 seconds, confirming its suitability for real-time Holter monitoring applications.
Conclusions:
- The proposed real-time MN artifact detection method is effective and accurate for Holter ECG data.
- This technique enhances the reliability of cardiac rhythm analysis, particularly for conditions like atrial fibrillation, by pre-processing data to remove artifacts.
- The computational efficiency supports seamless integration into real-time Holter monitoring systems.
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