Respiratory Rate Extraction Via an Autoregressive Model Using the Optimal Parameter Search Criterion

Lee, Jin-Seok; Chon, Ki H.

doi:10.1007/s10439-010-0080-9

Cited by 31 publications

(32 citation statements)

References 12 publications

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“…The pole with maximum magnitude inside the unit circle is considered as the prominent pole. 12,19,38 Figures 8c and 8d show the coherence analysis results of the subject in the fasting state. As seen in Fig.…”

Section: Resultsmentioning

confidence: 97%

On Non-Invasive Measurement of Gastric Motility from Finger Photoplethysmographic Signal

2010

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This article investigates the possibility of extracting gastric motility (GM) information from finger photoplethysmographic (PPG) signals non-invasively. Now-a-days measuring GM is a challenging task because of invasive and complicated clinical procedures involved. It is well-known that the PPG signal acquired from finger consists of information related to heart rate and respiratory rate. This thread is taken further and effort has been put here to find whether it is possible to extract GM information from finger PPG in an easier way and without discomfort to the patients. Finger PPG and GM (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the rate of 100 Hz from eight healthy subjects for 30 min duration in fasting and postprandial states. In this study, we process the finger PPG signal and extract a slow wave that is analogous to actual EGG signal. To this end, we chose two advanced signal processing approaches: first, we perform discrete wavelet transform (DWT) to separate the different components, since PPG and EGG signals are non-stationary in nature. Second, in the frequency domain, we perform cross-spectral and coherence analysis using autoregressive (AR) spectral estimation method in order to compare the spectral details of recorded PPG and EGG signals. In DWT, a lower frequency oscillation (≈0.05 Hz) called slow wave was extracted from PPG signal which looks similar to the slow wave of GM in both shape and frequency in the range (0-0.1953) Hz. Comparison of these two slow wave signals was done by normalized cross-correlation technique. Cross-correlation values are found to be high (range 0.68-0.82, SD 0.12, R = 1.0 indicates exact agreement, p < 0.05) for all subjects and there is no significant difference in cross-correlation between fasting and postprandial states. The coherence analysis results demonstrate that a moderate coherence (range 0.5-0.7, SD 0.13, p < 0.05) exists between EGG and PPG signal in the "slow wave" frequency band, without any significant change in the level of coherence in postprandial state. These results indicate that finger PPG signal contains GM-related information. The findings are sufficiently encouraging to motivate further exploration of finger PPG as a non-invasive source of GM-related information.

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Section: Resultsmentioning

confidence: 97%

On Non-Invasive Measurement of Gastric Motility from Finger Photoplethysmographic Signal

2010

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“…We used five Walsh functions for this example based on the use of the mean square criterion as detailed in our previous work. 15 Simulation Example 2…”

Section: Simulation Examplementioning

confidence: 99%

“…7 This compromise in accuracy with an AR model was largely due to its reliance on the Akaike information criterion for model order determination. 15 However, the motivation for using an AR model approach over non-parametric time-frequency analysis is mainly due to the well-known fact that the former method's accuracy is less dependent on the data length. 20 Thus, one of the motivations of our approach using sinusoidal basis functions in this work was to utilize an efficient AR model order criterion, known as the optimal parameter search (OPS), which has been shown to be accurate for time-invariant (TIV) and time-varying (TV) parametric models.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Monitor Nonstationary Dynamics in Physiological Signals: Application to Blood Pressure, Pulse Oximeter, and Respiratory Data

Yang

Chon

2010

Ann Biomed Eng

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We propose a parametric time-varying (TV) algorithm which utilizes sinusoids as the basis functions which are then projected onto sets of Legendre and Walsh functions for the purpose of monitoring nonstationary dynamics. The proposed algorithm is a general-purpose algorithm that has the potential to be widely applicable to various physiological signals, but is especially well-suited for tracking blood pressure (BP), pulse oximeter, and respiratory signals, as they all exhibit periodic oscillations with TV dynamics. The proposed algorithm's efficacy was verified using both simulation examples and application to experimental data from all of the above-mentioned sources. Our results show that the method can: (1) accurately monitor abrupt frequency changes even when the data are contaminated with significant noise, (2) accurately monitor the BP and pulse oximeter signals, and (3) provide accurate estimation of respiratory rates derived directly from pulse oximeter recordings.

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“…Recently, concomitant with increased interest in extracting BF directly from the PPG waveforms, several accurate nonparametric (e.g., time-frequency spectral methods) [6], [7] and parametric [e.g., autoregressive (AR) model-based approaches] [8] methods have been introduced.…”

Section: Introductionmentioning

confidence: 99%

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

Lee

Chon

2011

IEEE Trans. Biomed. Eng.

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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.

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Respiratory Rate Extraction Via an Autoregressive Model Using the Optimal Parameter Search Criterion

Cited by 31 publications

References 12 publications

On Non-Invasive Measurement of Gastric Motility from Finger Photoplethysmographic Signal

On Non-Invasive Measurement of Gastric Motility from Finger Photoplethysmographic Signal

A Novel Approach to Monitor Nonstationary Dynamics in Physiological Signals: Application to Blood Pressure, Pulse Oximeter, and Respiratory Data

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

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