A model based method for deriving respiratory activity from photoplethysmographic signals

Clinical investigation of some sleep disorders requires simultaneous monitoring of heart and respiratory rates. There have been several efforts on ECG-Derived Respiration (EDR). The photoplethysmographic (PPG) signal includes both heart and respiratory components. In situations such as ambulatory monitoring, stress tests and sleep disorder investigations, where the respiration is not monitored by specialized equipment, it would be advantageous to derive a surrogate respiratory signal directly from the PPG. This paper presents an efficient technique, for the estimation of heart and respiratory rates from the PPG signals based on modified covariance auto regressive model. Test results reveal that the order reduced-modified covariance AR model (OR-MCAR) has efficiently estimated heart and respiratory rates. Accuracy rate, a quantitative evaluation measure, establishes the efficacy of the proposed algorithm.