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Fusion enhancement for tracking of respiratory rate through intrinsic mode functions in photoplethysmography

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Fusion enhancement for tracking of respiratory rate through intrinsic mode functions in photoplethysmography. / Pirhonen, Mikko; Vehkaoja, Antti.

julkaisussa: Biomedical Signal Processing and Control, Vuosikerta 59, 101887, 2020.

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Pirhonen, Mikko ; Vehkaoja, Antti. / Fusion enhancement for tracking of respiratory rate through intrinsic mode functions in photoplethysmography. Julkaisussa: Biomedical Signal Processing and Control. 2020 ; Vuosikerta 59.

Bibtex - Lataa

@article{a82684a16ef54eb1b912f4fdffe08fd9,
title = "Fusion enhancement for tracking of respiratory rate through intrinsic mode functions in photoplethysmography",
abstract = "Decline in respiratory regulation demonstrates the primary forewarning for the onset of physiological aberrations. In clinical environment, the obtrusive nature and cost of instrumentation have retarded the integration of continuous respiration monitoring for standard practice. Photoplethysmography (PPG) presents a non-invasive, optical method of assessing blood flow dynamics in peripheral vasculature. Incidentally, respiration couples as a surrogate constituent in PPG signal, justifying respiratory rate (RR) estimation. The physiological processes of respiration emerge as distinctive oscillations that are fluctuations in various parameters extracted from PPG signal. We propose a novel algorithm designed to account for intermittent diminishment of the respiration induced variabilities (RIV) by a fusion-based enhancement of wavelet synchrosqueezed spectra. We have combined the information on intrinsic mode functions (IMF) of five RIVs to enhance mutually occurring, instantaneous frequencies of the spectra. The respiration rate estimate is obtained by tracking the spectral ridges with a particle filter. We have evaluated the method with a dataset recorded from 29 young adult subjects (mean: 24.17 y, SD: 4.19 y) containing diverse, voluntary, and periodically metronome-assisted respiratory patterns. Bayesian inference on fusion-enhanced Respiration Induced Frequency Variability (RIFV) indicated MAE and RMSE of 1.764 and 3.996 BPM, respectively. The fusion approach was deemed to improve MAE and RMSE of RIFV by 0.185 BPM (95{\%} HDI: 0.0285-0.3488, effect size: 0.548) and 0.250 BPM (95{\%} HDI: 0.0733-0.431, effect size: 0.653), respectively, with further pronounced improvements to other RIVs. We conclude that the fusion of variability signals proves important to IMF localization in the spectral estimation of RR.",
keywords = "Particle filtering, Photoplethysmography, Respiration rate, Spectral fusion, Synchrosqueezing",
author = "Mikko Pirhonen and Antti Vehkaoja",
note = "INT=bmte,{"}Pirhonen, Mikko{"}",
year = "2020",
doi = "10.1016/j.bspc.2020.101887",
language = "English",
volume = "59",
journal = "Biomedical Signal Processing and Control",
issn = "1746-8094",
publisher = "Elsevier",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Fusion enhancement for tracking of respiratory rate through intrinsic mode functions in photoplethysmography

AU - Pirhonen, Mikko

AU - Vehkaoja, Antti

N1 - INT=bmte,"Pirhonen, Mikko"

PY - 2020

Y1 - 2020

N2 - Decline in respiratory regulation demonstrates the primary forewarning for the onset of physiological aberrations. In clinical environment, the obtrusive nature and cost of instrumentation have retarded the integration of continuous respiration monitoring for standard practice. Photoplethysmography (PPG) presents a non-invasive, optical method of assessing blood flow dynamics in peripheral vasculature. Incidentally, respiration couples as a surrogate constituent in PPG signal, justifying respiratory rate (RR) estimation. The physiological processes of respiration emerge as distinctive oscillations that are fluctuations in various parameters extracted from PPG signal. We propose a novel algorithm designed to account for intermittent diminishment of the respiration induced variabilities (RIV) by a fusion-based enhancement of wavelet synchrosqueezed spectra. We have combined the information on intrinsic mode functions (IMF) of five RIVs to enhance mutually occurring, instantaneous frequencies of the spectra. The respiration rate estimate is obtained by tracking the spectral ridges with a particle filter. We have evaluated the method with a dataset recorded from 29 young adult subjects (mean: 24.17 y, SD: 4.19 y) containing diverse, voluntary, and periodically metronome-assisted respiratory patterns. Bayesian inference on fusion-enhanced Respiration Induced Frequency Variability (RIFV) indicated MAE and RMSE of 1.764 and 3.996 BPM, respectively. The fusion approach was deemed to improve MAE and RMSE of RIFV by 0.185 BPM (95% HDI: 0.0285-0.3488, effect size: 0.548) and 0.250 BPM (95% HDI: 0.0733-0.431, effect size: 0.653), respectively, with further pronounced improvements to other RIVs. We conclude that the fusion of variability signals proves important to IMF localization in the spectral estimation of RR.

AB - Decline in respiratory regulation demonstrates the primary forewarning for the onset of physiological aberrations. In clinical environment, the obtrusive nature and cost of instrumentation have retarded the integration of continuous respiration monitoring for standard practice. Photoplethysmography (PPG) presents a non-invasive, optical method of assessing blood flow dynamics in peripheral vasculature. Incidentally, respiration couples as a surrogate constituent in PPG signal, justifying respiratory rate (RR) estimation. The physiological processes of respiration emerge as distinctive oscillations that are fluctuations in various parameters extracted from PPG signal. We propose a novel algorithm designed to account for intermittent diminishment of the respiration induced variabilities (RIV) by a fusion-based enhancement of wavelet synchrosqueezed spectra. We have combined the information on intrinsic mode functions (IMF) of five RIVs to enhance mutually occurring, instantaneous frequencies of the spectra. The respiration rate estimate is obtained by tracking the spectral ridges with a particle filter. We have evaluated the method with a dataset recorded from 29 young adult subjects (mean: 24.17 y, SD: 4.19 y) containing diverse, voluntary, and periodically metronome-assisted respiratory patterns. Bayesian inference on fusion-enhanced Respiration Induced Frequency Variability (RIFV) indicated MAE and RMSE of 1.764 and 3.996 BPM, respectively. The fusion approach was deemed to improve MAE and RMSE of RIFV by 0.185 BPM (95% HDI: 0.0285-0.3488, effect size: 0.548) and 0.250 BPM (95% HDI: 0.0733-0.431, effect size: 0.653), respectively, with further pronounced improvements to other RIVs. We conclude that the fusion of variability signals proves important to IMF localization in the spectral estimation of RR.

KW - Particle filtering

KW - Photoplethysmography

KW - Respiration rate

KW - Spectral fusion

KW - Synchrosqueezing

U2 - 10.1016/j.bspc.2020.101887

DO - 10.1016/j.bspc.2020.101887

M3 - Article

VL - 59

JO - Biomedical Signal Processing and Control

JF - Biomedical Signal Processing and Control

SN - 1746-8094

M1 - 101887

ER -