TAILIEUCHUNG - Báo cáo sinh học: " Research Article Data Fusion for Improved Respiration Rate Estimation"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Data Fusion for Improved Respiration Rate Estimation | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 926305 10 pages doi 2010 926305 Research Article Data Fusion for Improved Respiration Rate Estimation Shamim Nemati 1 2 Atul Malhotra 2 and Gari D. Clifford1 2 department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford OX1 3PJ UK 2Division of Sleep Medicine Harvard Medical School Brigham and Women s Hospital 221 Longwood Avenue Boston MA 02115 UsA Correspondence should be addressed to Gari D. Clifford Received 7 January 2010 Revised 11 March 2010 Accepted 8 May 2010 Academic Editor Igor Djurovic Copyright 2010 Shamim Nemati et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 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 outperform 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 .

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