Title

Automatic motion and noise artifact detection in Holter ECG data using empirical mode decomposition and statistical approaches

UMMS Affiliation

Department of Medicine, Division of Cardiovascular Medicine; Meyers Primary Care Institute

Date

6-2012

Document Type

Article

Medical Subject Headings

*Algorithms; *Artifacts; Atrial Fibrillation; Computer Systems; Diagnosis, Computer-Assisted; Electrocardiography, Ambulatory; Humans; Motion; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal-To-Noise Ratio

Disciplines

Biomedical Engineering and Bioengineering | Cardiology

Abstract

We present a real-time method for the detection of motion and noise (MN) artifacts, which frequently interferes with accurate rhythm assessment when ECG signals are collected from Holter monitors. Our MN artifact detection approach involves two stages. The first stage involves the use of the first-order intrinsic mode function (F-IMF) from the empirical mode decomposition to isolate the artifacts' dynamics as they are largely concentrated in the higher frequencies. The second stage of our approach uses three statistical measures on the F-IMF time series to look for characteristics of randomness and variability, which are hallmark signatures of MN artifacts: the Shannon entropy, mean, and variance. We then use the receiver-operator characteristics curve on Holter data from 15 healthy subjects to derive threshold values associated with these statistical measures to separate between the clean and MN artifacts' data segments. With threshold values derived from 15 training data sets, we tested our algorithms on 30 additional healthy subjects. Our results show that our algorithms are able to detect the presence of MN artifacts with sensitivity and specificity of 96.63% and 94.73%, respectively. In addition, when we applied our previously developed algorithm for atrial fibrillation (AF) detection on those segments that have been labeled to be free from MN artifacts, the specificity increased from 73.66% to 85.04% without loss of sensitivity (74.48%-74.62%) on six subjects diagnosed with AF. Finally, the computation time was less than 0.2 s using a MATLAB code, indicating that real-time application of the algorithms is possible for Holter monitoring.

Comments

Citation: Jinseok Lee; McManus, D.D.; Merchant, S.; Chon, K.H.; , "Automatic Motion and Noise Artifact Detection in Holter ECG Data Using Empirical Mode Decomposition and Statistical Approaches," Biomedical Engineering, IEEE Transactions on , vol.59, no.6, pp.1499-1506, June 2012
doi: 10.1109/TBME.2011.2175729 Link to article on publisher's site

Related Resources

Link to Article in PubMed

PubMed ID

22086485