Data Fusion for Improved Respiration Rate Estimation

Abstract--We present an application of a modified KalmanFilter (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 on both 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 ex...

• Type: Journal
• Year: 2010
• Where: EJASP
• Authors: Shamim Nemati, Atul Malhotra, Gari D. Clifford
• Keywords: EJASP 2010, Respiration Rate, Respiration Rate Estimation, Respiratory Rate