Audio Feature Analysis for Acoustic Pain Detection in Term Newborns

Vito Giordano, Alexandra Luister, Christoph Reuter, Isabella Czedik-Eysenberg, Dominique Singer, David Steyrl, Eik Vettorazzi, Philipp Deindl

Publications: Contribution to journalArticlePeer Reviewed

Abstract

INTRODUCTION: Crying newborns signal a need or discomfort as part of the innate communication system. Exposure to pain is related to infants' unfavorable neurodevelopmental outcomes. There is a tremendous need for more objective methods to assess neonatal pain. An audio analysis of acoustic utterances could provide specific information on the patient's pain level.

METHODS: We analyzed 67 videos of 33 term-born newborns recorded during a planned capillary blood sample, including the stimuli, non-noxious thermal stimulus, short noxious stimulus, and prolonged unpleasant stimulus, between December 2020 and March 2021. Two expert raters evaluated the infants' pain responses using the Neonatal Facial Coding System (NFCS). The mean values of 123 timbre features of the recorded audio data were analyzed by using specific toolboxes and libraries from the following programming environments: MIRtoolbox (MATLAB), MiningSuite (MATLAB), Essentia (Python), AudioCommons timbral models (Python), and Librosa (Python).

RESULTS: The NFCS values were significantly higher during the short noxious stimulus (p < 0.001) and prolonged unpleasant stimulus (p < 0.001) than during the non-noxious thermal stimulus, whereas NFCS values during the short noxious stimulus and prolonged unpleasant stimulus were similar (p = 0.79). Brightness, roughness, percussive energy, and attack times were identified as the features having the highest impact on the NFCS.

CONCLUSION: This hypothesis-generating study identified several salient acoustic features highly associated with pain responses in term newborns. Our analysis is an encouraging starting point for the targeted analysis of pain-specific acoustic features of neonatal cries and vocalizations from the perspective of real-time acoustic processing.

Original languageEnglish
Pages (from-to)760-768
Number of pages9
JournalNeonatology
Volume119
Issue number6
Early online date16 Sep 2022
DOIs
Publication statusPublished - 1 Dec 2022

Austrian Fields of Science 2012

  • 302049 Neonatology
  • 102019 Machine learning

Keywords

  • Crying
  • Neonatal Facial Coding System
  • Noxious stimulation
  • Timbre features
  • Vocalization

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