Monitoring Respiratory Activity Using PPG Signals by Order Reduced-Modified Covariance AR Technique

The clinical significance of certain cardiac arrhythmias can be understood only with reference to respiration. In normal healthy conditions, the respiratory rate is 10-20 breaths/minute. But, certain problems of illness, accidents or some other causes affect the regular sinus rhythm. A non-invasive, non-occlusive and non-intrusive respiration monitoring is desirable in a number of situations such as ambulatory monitoring, stress tests and sleep disorder investigations. Such methods are based on deriving the respiratory activity from the electrocardiogram (ECG). There have been several efforts on ECG-Derived Respiration (EDR). Presently research is being focused on PPG derived respiratory activity because of its simplicity in sensing the signal and proved a strong correlation between PPG and respiratory signals. In this paper, we present an efficient method for extraction of respiratory activity from photoplethysmographic (PPG) signals based on modified covariance method. The proposed method makes use of order reduced AR-model by restricting the pole locations in the frequency range of interest. Test results reveal that the order reduced-modified covariance AR model (OR-MCAR) has efficiently separated respiratory information from PPG than a normal AR model.