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

Content Provider	IEEE Xplore Digital Library
Author	Venu Madhav, K. Raghuram, M. Hari Krishna, E. Ashoka Reddy, K.
Copyright Year	2010
Abstract	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.
Starting Page	1
Ending Page	4
File Size	447864
Page Count	4
File Format	PDF
ISBN	9781424447121
ISSN	21517622
DOI	10.1109/ICBBE.2010.5516378
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Venus Nonlinear filters Electrocardiography White noise Predictive models Finite impulse response filter Frequency estimation Band pass filters Monitoring Testing
Content Type	Text
Resource Type	Article

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