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Heart rate analysis by sparse representation for acute pain detection

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Abstract

Objective pain assessment methods pose an advantage over the currently used subjective pain rating tools. Advanced signal processing methodologies, including the wavelet transform (WT) and the orthogonal matching pursuit algorithm (OMP), were developed in the past two decades. The aim of this study was to apply and compare these time-specific methods to heart rate samples of healthy subjects for acute pain detection. Fifteen adult volunteers participated in a study conducted in the pain clinic at a single center. Each subject’s heart rate was sampled for 5-min baseline, followed by a cold pressor test (CPT). Analysis was done by the WT and the OMP algorithm with a Fourier/Wavelet dictionary separately. Data from 11 subjects were analyzed. Compared to baseline, The WT analysis showed a significant coefficients’ density increase during the pain incline period (p < 0.01) and the entire CPT (p < 0.01), with significantly higher coefficient amplitudes. The OMP analysis showed a significant wavelet coefficients’ density increase during pain incline and decline periods (p < 0.01, p < 0.05) and the entire CPT (p < 0.001), with suggestive higher amplitudes. Comparison of both methods showed that during the baseline there was a significant reduction in wavelet coefficient density using the OMP algorithm (p < 0.001). Analysis by the two-way ANOVA with repeated measures showed a significant proportional increase in wavelet coefficients during the incline period and the entire CPT using the OMP algorithm (p < 0.01). Both methods provided accurate and non-delayed detection of pain events. Statistical analysis proved the OMP to be by far more specific allowing the Fourier coefficients to represent the signal’s basic harmonics and the wavelet coefficients to focus on the time-specific painful event. This is an initial study using OMP for pain detection; further studies need to prove the efficiency of this system in different settings.

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Authors and Affiliations

  1. Division of Pediatric Cardiology, Department of Pediatrics, University of California, San Diego, Rady Children’s Hospital, San Diego, CA, USA

    Shai Tejman-Yarden & James C. Perry

  2. Department of Industrial Engineering and Management, Ben Gurion University of the Negev, Beersheba, Israel

    Ofer Levi & Yisrael Parmet

  3. Department of Biomedical Engineering, Ben Gurion University of the Negev, Beersheba, Israel

    Shai Tejman-Yarden & Alex Beizerov

  4. Department of Electrical Engineering, University of California, San Diego, La Jolla, CA, USA

    Tu Nguyen

  5. Department of Management Science Engineering, Stanford University, Stanford, CA, USA

    Michael Saunders

  6. Department of Anesthesiology and Critical Care, Soroka University Medical Center, Beersheba, Israel

    Zvia Rudich

  7. Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA

    Dewleen G. Baker

  8. Veterans Affairs Center of Excellence for Stress and Mental Health, San Diego, CA, USA

    Dewleen G. Baker & Tobias Moeller-Bertram

  9. Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA

    Tobias Moeller-Bertram

  10. The Edmond J. Safra International Congenital Heart Center, Sheba Medical Center, Ramat Gan, Israel

    Shai Tejman-Yarden

Authors
  1. Shai Tejman-Yarden
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  2. Ofer Levi
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  3. Alex Beizerov
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  4. Yisrael Parmet
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  5. Tu Nguyen
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  6. Michael Saunders
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  7. Zvia Rudich
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  8. James C. Perry
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  9. Dewleen G. Baker
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  10. Tobias Moeller-Bertram
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Correspondence to Shai Tejman-Yarden.

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Tejman-Yarden, S., Levi, O., Beizerov, A. et al. Heart rate analysis by sparse representation for acute pain detection. Med Biol Eng Comput 54, 595–606 (2016). https://doi.org/10.1007/s11517-015-1350-3

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  • DOI: https://doi.org/10.1007/s11517-015-1350-3

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