Fast fourier transform analysis of low frequency components in respiratory signals

• Autorzy: Chyliński, Mirosław
• Języki publikacji: EN

Abstrakty

EN

This paper presents a results of a Fast Fourier Transform (FFT) analysis of the electric activity of the respiratory muscles. The recorded respiratory signal measured by an author's measurement system was correlated with extracted breathing signal from the electrocardiogram (ECG). As the results of the analysis, the inhalations and exhalations are represented by low frequency components in ECG signal.

• Czasopismo: Zeszyty Naukowe. Elektryka / Politechnika Opolska
• Rocznik: 2019
• Tom: z. 77
• Strony: 29--32
• Opis fizyczny: Bibliogr. 10 poz. rys., tab., wykr.

Twórcy

autor

Chyliński, Mirosław

• Faculty of Electrical Engineering, Automatic Control and Informatics Institute of Control Engineering

Bibliografia

• [1] CHYLIŃSKI M., SZMAJDA M., SACHA J.: A Savitzky and Golay Filtration Method for Respiratory Rate Estimation from ECG Signals. VIII Metrology Congress, Augustów, 2019
• [2] DAIMIWAL N., SUNDHARARAJAN M., SHRIRAM R.: Respiratory rate, heart rate and continuous measurement of BP using PPG. 2014 International Conference on Communication and Signal Processing, Melmaruvathur, pp. 999–1002, doi: 10.1109/ICCSP.2014.6949996, 2014
• [3] HAQUE A.K.M., HANIF M., KIBER M. HASAN MD.: Automatic Feature Extraction of ECG Signal Using Fast Fourier Transform. Journal of Bangladesh Electronic Society, 2019.
• [4] JEYHANI V., VUORINEN T., MÄNTYSALO M., et al.: Comparison of simple algorithms for estimating respiration rate from electrical impedance pneumography signals in wearable devices. Health Technol., Vol. 7/21, doi: 10.1007/s12553-0160156-0, 2017
• [5] CAI J., LIU Y., YIN F.: Extracting Breathing Signal Using Fourier Transform from Cine Magnetic Resonance Imaging. Cancer Transl Med, Vol. 1, pp. 16–20, 2015
• [6] KEHTARNAVAZ N.: CHAPTER 7 – Frequency Domain Processing. Digital Signal Processing System Design (Second Edition). Academic Press, pp. 175–196, ISBN 9780123744906, doi.org/10.1016/B978-0-12-374490-6.00007-6, 2008
• [7] KIM H., KIM JY., IM CH.: Fast and Robust Real-Time Estimation of Respiratory Rate from Photoplethysmography. Sensors (Basel). Vol. 16(9), p. 1494, doi: 10.3390/s16091494, 2016
• [8] MOHAMED M., SREERAMAN R., MIODRAG B., MOHAMAD F., HILMI R. DAJANI, IZMAIL B.: Human Breathing Rate Estimation from Radar Returns Using Harmonically Related Filters. Journal of Sensors, Vol. 2016, Article ID 9891852, 7 pages, doi: 10.1155/2016/9891852, 2016
• [9] SCHRUMPF F., STURM M., BAUSCH G., FUCHS MI.: Derivation of the respiratory rate from directly and indirectly measured respiratory signals using autocorrelation. Current Directions in Biomedical Engineering. Vol. 2, pp. 241–245, doi: 10.1515/cdbme-2016-0054, 2016
• [10] ARUNACHALAM S., BROWN L.: Real-time estimation of the ECG-derived respiration (EDR) signal using a new algorithm for baseline wander noise removal. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, pp. 5681–5684, doi: 10.1109/ IEMBS.2009.5333113, 2009

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).