Apnea Detection Method for Cheyne-Stokes Respiration Analysis on Newborn

Taiga Niimi, Yushi Itoh, Michiya Natori, Yoshimitsu Aoki

Research output: Contribution to journalArticle

Abstract

Cheyne-Stokes respiration is especially prevalent in preterm newborns, but its severity may not be recognized. It is characterized by apnea and cyclical weakening and strengthening of the breathing. We developed a method for detecting apnea and this abnormal respiration and for estimating its malignancy. Apnea was detected based on a "difference" feature (calculated from wavelet coefficients) and a modified maximum displacement feature (related to the respiratory waveform shape). The waveform is calculated from vertical motion of the thoracic and abdominal region during respiration using a vision sensor. Our proposed detection method effectively detects apnea (sensitivity 88.4%, specificity 99.7%).

Original languageEnglish
Pages (from-to)67-82
Number of pages16
JournalInternational Journal of Optomechatronics
Volume7
Issue number2
DOIs
Publication statusPublished - 2013 Apr 1

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Keywords

  • Cheyne-Stokes respiration
  • FG vision sensor
  • apnea
  • image processing
  • newborn
  • preterm
  • respiratory monitoring system
  • signal processing
  • wavelet

ASJC Scopus subject areas

  • Instrumentation
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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