Data Fusion for Improved Respiration Rate Estimation

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 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.