Background: Many researchers have attempted to acquire respiratory rate (RR)\ninformation from a photoplet hysmogram (PPG) because respiration affects the\nwaveform of the PPG. However, most of these methods were difficult to operate in\nreal-time because of their complexity or computational requirements. From these\nneeds, we attempted to develop a method to estimate RR from a PPG with a light\ncomputational burden.\nMethods: To obtain RR information, we adopt a sequential filtering structure and\nfrequency estimation technique, which extracts a dominant frequency from a given\nsignal. In particular, we used an adaptive lattice notch filter (ALNF) to estimate RR\nfrom a PPG along with an additional heart rate that is utilized as an adaptation\nparameter of our method. Furthermore, we designed a sequential infinite impulse\nresponse (IIR) notch filtering system (i.e., harmonic IIR notch filter) to eliminate the\ncardiac component and its harmonics from the PPG. We compared the proposed\nmethod with Burg�s AR modeling method, which is widely used to estimate RR from\na PPG, using open-source data and measured data.\nResults: By using a statistical test, it was determined that our adaptive lattice-type\nrespiratory rate estimator (ALRE) was significantly more accurate than Burg�s AR\nmodel method (p <0.0001). Furthermore, the ALRE�s tracking performance was better\nthan that of Burg�s method, and the variances of its estimates were smaller than\nthose of Burg�s method.\nConclusions: In short, our method showed a better performance than Burg�s AR\nmodeling method for real-time applications.
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