Cheyne-Stokes respiration detection method for newborns with apnea

Taiga Niimi, Yushi Itoh, Michiya Natori, Yoshimitsu Aoki

Research output: Contribution to journalArticlepeer-review

Abstract

Cheyne-Stokes respiration (CSR) has a high prevalence among newborns, especially among preterm babies. Although doctors generally recognize the phenomenon, they are not able to assess the severity of CSR in individual infants. CSR is characterized by cyclical weakening and strengthening of respirations with apnea. In this study, we developed an apnea detection method, and a CSR detection method using detected apneic events. We detected apnea using two features of respiratory waveforms. The first feature is frequency information calculated from wavelet coefficients. The second is information based on the shape of the waveform. In our CSR detection method, we used a spurious periodicity feature to determine CSR sections. The waveform is calculated by a respiratory monitoring system that uses a fiber-grating vision sensor to measure the vertical motion of the infant's thoracic and abdominal regions during respiration. Our method is effective at detecting apnea (sensitivity: 94.3 %, specificity: 99.7 %).

Original languageEnglish
Pages (from-to)278-291
Number of pages14
JournalITE Transactions on Media Technology and Applications
Volume1
Issue number4
DOIs
Publication statusPublished - 2013

Keywords

  • Apnea
  • Cheyne-Stokes respiration
  • FG vision sensor.
  • Newborn
  • Preterm
  • Wavelet

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Graphics and Computer-Aided Design

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