An Autoregressive Model-Based Particle Filtering Algorithms for Extraction of Respiratory Rates as High as 90 Breaths Per Minute From Pulse Oximeter

• Published in: IEEE transactions on biomedical engineering Vol. 57; no. 9; pp. 2158 - 2167
• Main Authors: Lee, Jinseok, Chon, Ki H.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.09.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Applied sciences; Autoregressive (AR) model; Biological and medical sciences; Biomedical engineering; Computer Simulation; Continuous wavelet transforms; Data mining; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Female; Filtering algorithms; Heart rate; Humans; Information, signal and communications theory; Investigative techniques of respiratory function; Investigative techniques, diagnostic techniques (general aspects); Male; Medical equipment; Medical sciences; Monte Carlo Method; Monte Carlo simulation; Nearest neighbor searches; Neural networks; optimal parameter search (OPS); Oximetry - methods; particle filter; Particle filters; Pulse measurements; pulse oximeters; Regression Analysis; Respiratory Rate - physiology; respiratory rate extraction; Signal and communications theory; Signal Processing, Computer-Assisted; Signal, noise; Studies; Telecommunications and information theory; Time frequency analysis; Human; Blood gas; Autoregressive (AR) model; Pulse oxymetry; Optical telecommunication; pulse oximeters; Autoregressive model; respiratory rate extraction; Algorithm; Respiratory frequency; Signal processing; optimal parameter search (OPS); Respiration; Particle filter; Biomedical engineering
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2010.2051330

We present particle filtering (PF) algorithms for an accurate respiratory rate extraction from pulse oximeter recordings over a broad range: 12-90 breaths/min. These methods are based on an autoregressive (AR) model, where the aim is to find the pole angle with the highest magnitude as it corresponds to the respiratory rate. However, when SNR is low, the pole angle with the highest magnitude may not always lead to accurate estimation of the respiratory rate. To circumvent this limitation, we propose a probabilistic approach, using a sequential Monte Carlo method, named PF, which is combined with the optimal parameter search (OPS) criterion for an accurate AR model-based respiratory rate extraction. The PF technique has been widely adopted in many tracking applications, especially for nonlinear and/or non-Gaussian problems. We examine the performances of five different likelihood functions of the PF algorithm: the strongest neighbor, nearest neighbor (NN), weighted nearest neighbor (WNN), probability data association (PDA), and weighted probability data association (WPDA). The performance of these five combined OPS-PF algorithms was measured against a solely OPS-based AR algorithm for respiratory rate extraction from pulse oximeter recordings. The pulse oximeter data were collected from 33 healthy subjects with breathing rates ranging from 12 to 90 breaths/ min. It was found that significant improvement in accuracy can be achieved by employing particle filters, and that the combined OPS-PF employing either the NN or WNN likelihood function achieved the best results for all respiratory rates considered in this paper. The main advantage of the combined OPS-PF with either the NN or WNN likelihood function is that for the first time, respiratory rates as high as 90 breaths/min can be accurately extracted from pulse oximeter recordings.