A ballistographic approach for continuous and non-obtrusive monitoring of movement in neonates

Rohan Joshi, Bart Bierling, Xi Long, Janna Weijers, Loe Feijs, Carola van Pul, Peter Andriessen

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Abstract

Continuously monitoring body movement in preterm infants can have important clinical applications since changes in movement-patterns can be a significant marker for clinical deteriorations including the onset of sepsis, seizures, and apneas. This paper proposes a system and method to monitor body movement of preterm infants in a clinical environment using ballistography. The ballistographic signal (BSG) is acquired using a thin and a film-like sensor that is placed underneath an infant. Manual annotations based on video-recordings served as a reference standard for identifying movement. We investigated the performance of multiple features, constructed from the BSG waveform, to discriminate movement from no movement based on data acquired from 10 preterm infants. Since routine cardiorespiratory monitoring is prone to movement artifacts, we also compared the application of these features on the simultaneously acquired cardiorespiratory waveforms, i.e., the electrocardiogram, the chest impedance, and the photoplethysmogram. The BSG-based-features consistently outperformed those based on the routinely acquired cardiorespiratory waveforms. The best performing BSG-based feature-the signal instability index-had a mean (standard deviation) effect size of 0.90 (0.06), as measured by the area under the receiver operating curve. The proposed system for monitoring body movement is robust to noise, non-obtrusive, and has high performance in clinical settings.

Original languageEnglish
Article number 2700809
Pages (from-to)10
JournalIEEE Journal of Translational Engineering in Health and Medicine
Volume6
DOIs
Publication statusPublished - 12 Oct 2018

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Keywords

  • Ballistography
  • Biomedical monitoring
  • body movement
  • Heart rate
  • Monitoring
  • neonates
  • patient monitoring
  • Pediatrics
  • Signal to noise ratio
  • Standards
  • statistical signal processing
  • Wearable sensors

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