A robust detection algorithm to identify breathing peaks in respiration signals from spontaneously breathing subjects

Chathuri Daluwatte, Christopher G. Scully, George Kramer, David G. Strauss

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    Assessing respiratory and cardiovascular system coupling can provide new insights into disease progression, but requires accurate analysis of each signal. Respiratory waveform data collected during spontaneous breathing are noisy and respiration rates from long term physiological experiments can vary over a wide range across time. There is a need for automatic and robust algorithms to detect breathing peaks in respiration signals for assessment of the coupling between the respiratory and cardiovascular systems. We developed an automatic algorithm to detect breathing peaks from a respiration signal. The algorithm was tested on respiration signals collected during hemorrhage in a conscious ovine model (N=9, total length = 11.0h). The breathing rate varied from 15 to as high as 160 breaths/min for some animals during the hemorrhage protocol. The sensitivity of the algorithm to detect respiration peaks was 93.7% with a precision of 94.5%. The developed algorithm presents a promising approach to detect breathing peaks in respiration signals from spontaneously breathing subjects. The algorithm was able to consistently identify breathing peaks while the breathing rate varied from 15 to 160 breaths/min.

    Original languageEnglish (US)
    Title of host publicationComputing in Cardiology
    PublisherIEEE Computer Society
    Pages297-300
    Number of pages4
    Volume42
    ISBN (Print)9781509006854
    DOIs
    StatePublished - Feb 16 2016
    Event42nd Computing in Cardiology Conference, CinC 2015 - Nice, France
    Duration: Sep 6 2015Sep 9 2015

    Other

    Other42nd Computing in Cardiology Conference, CinC 2015
    CountryFrance
    CityNice
    Period9/6/159/9/15

    Fingerprint

    Respiration
    Respiratory system
    Cardiovascular system
    Cardiovascular System
    Respiratory System
    Animals
    Hemorrhage
    Respiratory Rate
    Disease Progression
    Sheep
    Experiments

    ASJC Scopus subject areas

    • Cardiology and Cardiovascular Medicine
    • Computer Science(all)

    Cite this

    Daluwatte, C., Scully, C. G., Kramer, G., & Strauss, D. G. (2016). A robust detection algorithm to identify breathing peaks in respiration signals from spontaneously breathing subjects. In Computing in Cardiology (Vol. 42, pp. 297-300). [7408645] IEEE Computer Society. https://doi.org/10.1109/CIC.2015.7408645

    A robust detection algorithm to identify breathing peaks in respiration signals from spontaneously breathing subjects. / Daluwatte, Chathuri; Scully, Christopher G.; Kramer, George; Strauss, David G.

    Computing in Cardiology. Vol. 42 IEEE Computer Society, 2016. p. 297-300 7408645.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Daluwatte, C, Scully, CG, Kramer, G & Strauss, DG 2016, A robust detection algorithm to identify breathing peaks in respiration signals from spontaneously breathing subjects. in Computing in Cardiology. vol. 42, 7408645, IEEE Computer Society, pp. 297-300, 42nd Computing in Cardiology Conference, CinC 2015, Nice, France, 9/6/15. https://doi.org/10.1109/CIC.2015.7408645
    Daluwatte C, Scully CG, Kramer G, Strauss DG. A robust detection algorithm to identify breathing peaks in respiration signals from spontaneously breathing subjects. In Computing in Cardiology. Vol. 42. IEEE Computer Society. 2016. p. 297-300. 7408645 https://doi.org/10.1109/CIC.2015.7408645
    Daluwatte, Chathuri ; Scully, Christopher G. ; Kramer, George ; Strauss, David G. / A robust detection algorithm to identify breathing peaks in respiration signals from spontaneously breathing subjects. Computing in Cardiology. Vol. 42 IEEE Computer Society, 2016. pp. 297-300
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