Data Fusion for Improved Respiration Rate Estimation
Name
1687-6180-2010-926305.pdf
Size
787.69 KB
Format
Adobe PDF
Checksum (MD5)
6b315b646a3b30bdc93614908c146f16
Author(s)
Nemati, Shamim
Malhotra, Atul
Clifford, Gari D.
Date Issued
June 2010
Journal
EURASIP Journal on Advances in Signal Processing
Publisher
Hindawi Pub. Corp/Springer
Citation
Nemati, Shamim, Atul Malhotra, and GariD Clifford. “Data Fusion for Improved Respiration Rate Estimation.” EURASIP Journal on Advances in Signal Processing 2010 Jun 08;(2010): 926305.
Version
Final published version
Abstract
We present an application of a modified Kalman-Filter (KF) framework for data fusion to the estimation of respiratory rate from multiple physiological sources which is robust to background noise. A novel index of the underlying signal quality of respiratory signals is presented and then used to modify the noise covariance matrix of the KF which discounts the effect of noisy data. The signal quality index, together with the KF innovation sequence, is also used to weight multiple independent estimates of the respiratory rate from independent KFs. The approach is evaluated both on a realistic artificial ECG model (with real additive noise) and on real data taken from 30 subjects with overnight polysomnograms, containing ECG, respiration, and peripheral tonometry waveforms from which respiration rates were estimated. Results indicate that our automated voting system can out-perform any individual respiration rate estimation technique at all levels of noise and respiration rates exhibited in our data. We also demonstrate that even the addition of a noisier extra signal leads to an improved estimate using our framework. Moreover, our simulations demonstrate that different ECG respiration extraction techniques have different error profiles with respect to the respiration rate, and therefore a respiration rate-related modification of any fusion algorithm may be appropriate.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1155/2010/926305
