Time-Varying Autoregressive Model-Based Multiple Modes Particle Filtering Algorithm for Respiratory Rate Extraction From Pulse Oximeter

• Published in: IEEE transactions on biomedical engineering Vol. 58; no. 3; pp. 790 - 794
• Main Authors: Lee, Jinseok, Chon, Ki H.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2011; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adult; Algorithms; Autoregressive (AR) model; Breathing; chronic heart failure (CHF); chronic obstructive pulmonary disease; Estimation; Extraction; Female; Filtering; Filtering algorithms; Filtration; Heart; Heuristic algorithms; Human subjects; Humans; Male; Monitoring, Physiologic - methods; optimal parameter search (OPS); Oximetry - methods; particle filter; Particle filters; Point-of-Care Systems; pulse oximeters; Regression Analysis; remote health monitoring; Respiratory rate; Respiratory Rate - physiology; respiratory rate extraction; Robustness; Signal Processing, Computer-Assisted; sleep apnea; Studies; sudden infant death syndrome; Television; vital signs
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2010.2085437

We present a particle filtering algorithm, which combines both time-invariant (TIV) and time-varying autoregressive (TVAR) models for accurate extraction of breathing frequencies (BFs) that vary either slowly or suddenly. The algorithm sustains its robustness for up to 90 breaths/min (b/m) as well. The proposed algorithm automatically detects stationary and nonstationary breathing dynamics in order to use the appropriate TIV or TVAR algorithm and then uses a particle filter to extract accurate respiratory rates from as low as 6 b/m to as high as 90 b/m. The results were verified on 18 healthy human subjects (16 for metronome and 2 for spontaneous measurements), and the algorithm remained accurate even when the respiratory rate suddenly changed by 24 b/m (either increased or decreased by this amount). Furthermore, simulation examples show that the proposed algorithm remains accurate for SNR ratios as low as -20 dB. We are not aware of any other algorithms that are able to provide accurate TV BF over a wide range of respiratory rates directly from pulse oximeters.