Heart rate analysis by sparse representation for acute pain detection.

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...

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Publicado en:Medical & Biological Engineering & Computing Vol. 54; no. 4; pp. 595 - 607
Autores principales: Tejman-Yarden, Shai, Levi, Ofer, Beizerov, Alex, Parmet, Yisrael, Nguyen, Tu, Saunders, Michael, Rudich, Zvia, Perry, James, Baker, Dewleen, Moeller-Bertram, Tobias, Perry, James C, Baker, Dewleen G
Formato: research Journal Article
Publicado: Springer Nature Apr2016
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Heart rate analysis by sparse representation for acute pain detection.
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          Tejman-Yarden, Shai
          Levi, Ofer
          Beizerov, Alex
          Parmet, Yisrael
          Nguyen, Tu
          Saunders, Michael
          Rudich, Zvia
          Perry, James
          Baker, Dewleen
          Moeller-Bertram, Tobias
          Perry, James C
          Baker, Dewleen G
        affil: Department of Industrial Engineering and Management, Ben Gurion University of the Negev, Beersheba Israel
      sug:
        subj:
          Algorithms
          Pain Physiopathology
          Heart Rate Physiology
          Pain Diagnosis
          Signal Processing, Computer Assisted
          Young Adult
          Female
          Human
          Male
          Adult
          Validation Studies
          Comparative Studies
          Evaluation Research
          Multicenter Studies
          Adult: 19-44 years
          Female
          Male
      ab: 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.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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