Name
Title | ||
|---|---|---|
A closed-loop system for artifact mitigation in ambulatory electrocardiogram monitoring |
Abstract | ||
|---|---|---|
Motion artifacts interfere with electrocardiogram (ECG) detection and information processing. In this paper, we present an independent component analysis based technique to mitigate these signal artifacts. We propose a new statistical measure to enable an automatic identification and removal of independent components, which correspond to the sources of noise. For the first time, we also present a signal-dependent closed-loop system for the quality assessment of the denoised ECG. In one experiment, noisy data is obtained by the addition of calibrated amounts of noise from the MIT-BIH NST database to the AHA ECG database. Arrhythmia classification based on a state-of-the-art algorithm with the direct use of noisy data thus obtained shows sensitivity and positive predictivity values of 87.7% and 90.0%, respectively, at an input signal SNR of -9 dB. Detection with the use of ECG data denoised by the proposed approach exhibits significant improvement in the performance of the classifier with the corresponding results being 96.5% and 99.1%, respectively. In a related lab trial, we demonstrate a reduction in RMS error of instantaneous heart rate estimates from 47.2% to 7.0% with the use of 56 minutes of denoised ECG from four physically active subjects. To validate our experiments, we develop a closed-loop, ambulatory ECG monitoring platform, which consumes 2.17 mW of power and delivers a data rate of 33 kbps over a dedicated UWB link. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/DATE.2012.6176510 | Design, Automation & Test in Europe Conference & Exhibition |
Keywords | Field | DocType |
electrocardiography,feature extraction,independent component analysis,medical disorders,medical signal detection,patient monitoring,signal classification,AHA ECG database,MIT-BIH NST database,ambulatory electrocardiogram monitoring platforms,arrhythmia classification,dedicated UWB link,denoised ECG signal detection,feature extraction,independent component analysis,information processing,motion artifact mitigation,power 2.17 mW,signal-dependent closed-loop system,state-of-the-art algorithm | Noise reduction,Noise measurement,Remote patient monitoring,Computer science,Signal-to-noise ratio,Feature extraction,Real-time computing,Root-mean-square deviation,Independent component analysis,Classifier (linguistics) | Conference |
ISSN | ISBN | Citations |
1530-1591 | 978-1-4577-2145-8 | 1 |
PageRank | References | Authors |
0.41 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammed Shoaib | 1 | 20 | 4.34 |
| Gene Marsh | 2 | 1 | 0.41 |
| Harinath Garudadri | 3 | 171 | 23.32 |
| Somdeb Majumdar | 4 | 4 | 1.17 |