Auto-regression-driven, reallocative particle filtering approaches in PPG-based respiration rate estimation

Pirhonen, Mikko; Suominen, Olli; Vehkaoja, Antti

doi:10.1007/978-981-10-5122-7_255

Cited by 1 publication

(3 citation statements)

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“…One of the major advantages of the synchrosqueezing spectral estimates is the consistency they often provide. We have obtained mean absolute and median error of 2.699 bpm and 1.494 bpm, respectively, with the same dataset by using AR-modeling of low-pass filtered PPG signal and particle filtering [ 12 ]. A large number of algorithms with various approaches have been assessed with this dataset in [ 3 , 4 ], where comparison for percent number of results falling at or below the error of 2 breaths (CP 2 ) has been given.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, the PPG RIAV was interpolated to the sampling frequency of 1.5 Hz. For a frequency component analysis, the ROI was set in range of 0.1–0.75 Hz in accordance with Nyquist sampling theorem, corresponding to 6 bpm–45 bpm as has been proposed earlier in [ 12 , 16 ]. This frequency band corresponds to the range of both healthy and disordered RRs in adult subjects.…”

Section: Methodsmentioning

confidence: 99%

“…Synchrosqueezing has also been applied for the estimation of instantaneous heart rate from PPG signal [ 10 ]. Particle filtering in PPG-derived RR extraction has been proposed previously in combination with autoregressive (AR) signal modelling in [ 11 , 12 ]. Besides synchrosqueezing, also other novel and efficient TF analysis methods, such as variable frequency complex demodulation have been proposed for RR estimation [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Acquiring Respiration Rate from Photoplethysmographic Signal by Recursive Bayesian Tracking of Intrinsic Modes in Time-Frequency Spectra

Pirhonen

Peltokangas

Vehkaoja

2018

Sensors

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Respiration rate (RR) provides useful information for assessing the status of a patient. We propose RR estimation based on photoplethysmography (PPG) because the blood perfusion dynamics are known to carry information on breathing, as respiration-induced modulations in the PPG signal. We studied the use of amplitude variability of transmittance mode finger PPG signal in RR estimation by comparing four time-frequency (TF) representation methods of the signal cascaded with a particle filter. The TF methods compared were short-time Fourier transform (STFT) and three types of synchrosqueezing methods. The public VORTAL database was used in this study. The results indicate that the advanced frequency reallocation methods based on synchrosqueezing approach may present improvement over linear methods, such as STFT. The best results were achieved using wavelet synchrosqueezing transform, having a mean absolute error and median error of 2.33 and 1.15 breaths per minute, respectively. Synchrosqueezing methods were generally more accurate than STFT on most of the subjects when particle filtering was applied. While TF analysis combined with particle filtering is a promising alternative for real-time estimation of RR, artefacts and non-respiration-related frequency components remain problematic and impose requirements for further studies in the areas of signal processing algorithms an PPG instrumentation.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%