Wyznaczanie częstości oddychania na podstawie analizy widmowej zmienności okresu sygnału fotopletyzmograficznego

• Autorzy: Wilk B. , Pazowski M.

Warianty tytułu

EN

Determination of the respiratory rate based on spectral analysis of the PPG period variability

• Języki publikacji: PL

Abstrakty

PL

W artykule opisano algorytm do wyznaczania częstości oddychania na podstawie analizy widmowej sygnału reprezentującego zmienność okresu fali tętna. Falę tętna zarejestrowano za pomocą czujnika fotopletyzmograficznego (tzw. PPG) umieszczonego na placu ręki. Do przetwarzania sygnału PPG zaproponowano zastosowanie analizy falkowej. Przeprowadzono także ocenę dokładności opracowanej metody wykorzystując sygnał referencyjny, który reprezentuje przepływ powietrza w czasie wydechu.

EN

The arterial pressure waveform contains valuable information regarding the respiratory rate. This paper describes the algorithm developed for estimating the respiratory rate by analyzing the period variability of the peripheral pulse wave. To record a pulse wave at the finger, a transmissiontype photoplethysmographic sensor was used. PPG signals were collected from 10 healthy subjects during free breathing, and breath holding over a period of 3-min using a data acquisition system (Fig. 1). The reference breathing rate was determined from the airflow signal recorded simultaneously with the PPG signal (Figs. 7 and 8). Firstly, the PPG signal was detrended and denoised using the wavelet transform (Fig. 2 and 3). Based on the locations of the maximum points, all periods were detected and the tachogram was constructed. The signal representing the period variability (PPV) was obtained by interpolating the envelope of the tachogram with a cubic polynomial function (Fig. 5). Then, fluctuations extracted by the DWT from the PPV signal were segmented into 10 s intervals. Using Burg’s method, the AR model based PSD was computed for each segment. Finally, the respiratory component was detected as the maximum in the frequency band of 0.150.4 Hz (Fig. 6). The obtained results show (Fig. 9) that the proposed method allows us to monitor the respiratory rate and to detect the induced apnea with the acceptable accuracy.

Słowa kluczowe

PL

częstość oddychania; sygnał PPG; analiza falkowa; metoda Burga

EN

respiratory rate; PPG signal; wavelet analysis; Burg's method

• Wydawca: Wydawnictwo PAK
• Czasopismo: Pomiary Automatyka Kontrola
• Rocznik: 2014
• Tom: R. 60, nr 8
• Strony: 610--613
• Opis fizyczny: Bibliogr. 9 poz., rys., wykr.

Twórcy

autor

Wilk B.

• Politechnika Rzeszowska, Katedra Metrologii i Systemów Diagnostycznych, ul. W. Pola 2, 35-959 Rzeszów

autor

Pazowski M.

• Politechnika Rzeszowska, Katedra Metrologii i Systemów Diagnostycznych, ul. W. Pola 2, 35-959 Rzeszów

Bibliografia

• [1] Meredith D. J. et al.: Photoplethysmographic derivation of respiratory rate: a review of relevant physiology. Journal of Medical Engineering & Technology, vol. 36, pp. 1-7, 2012.
• [2] Jih-Sen Wong J-S., et al.: A comparative study of pulse rate variability and heart rate variability in healthy subjects. J Clin Monit Comput, vol. 26, pp. 107-114, 2012.
• [3] Karlen W. et al.: Multiparameter respiratory rate estimation from photoplethysmogram. IEEE Tran. Biomed. Eng., vol. 60, pp.1946-1953, 2013.
• [4] Lázaro J. et al.: Deriving respiration from the pulse photoplethysmographic signal. Computing in Cardiology, vol. 38, pp. 713-716, 2011.
• [5] Seo H. et al.: Performance improvement of pulse oximetry - based respiration detection by selective mode bandpass filtering. Ergonomics and Health Aspects, LNCS 4566, pp. 300-308, 2007.
• [6] Lee J., Chon K. H.: Respiratory rate extraction via an autoregressive model using the optimal parameter search criterion. Annals of Biomedical Engineering, vol. 38, pp. 3218-3225, 2010.
• [7] Chon Ki. H., Dash S., Ju K.: Estimation of respiratory rate from photoplethysmogram data using time-frequency spectral estimation. IEEE Tran. Biomed. Eng., vol. 56, pp. 2054-2063, 2009.
• [8] Mallat S.: A Wavelet Tour of Signal Processing. Academic Press, San Diego-London, 1998.
• [9] Broersen P. M. T., de Waele S.: The Burg algorithm for segments. IEEE Trans. on Signal Processing, vol. 48, pp. 2876-2880, 2000.