Automated Pain Assessment using Electrodermal Activity Data and Machine Learning

Susam B. T., Akcakaya M., Nezamfar H., Diaz D., Xu X., De Sa V. R., ...Daha Fazla

40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Hawaii, Amerika Birleşik Devletleri, 18 - 21 Temmuz 2018, cilt.2018-July, ss.372-375 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 2018-July
  • Doi Numarası: 10.1109/embc.2018.8512389
  • Basıldığı Şehir: Hawaii
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.372-375
  • Hakkari Üniversitesi Adresli: Hayır

Özet

Objective pain assessment is required for appropriate pain management in the clinical setting. However, clinical gold standard pain assessment is based on subjective methods. Automated pain detection from physiological data may provide important objective information to better standardize pain assessment. Specifically, electrodermal activity (EDA) can identify features of stress and anxiety induced by varying pain levels. However, notable variability in EDA measurement exists and research to date has demonstrated sensitivity but lack of specificity in pain assessment. In this paper, we use timescale decomposition (TSD) to extract salient features from EDA signals to identify an accurate and automated EDA pain detection algorithm to sensitively and specifically distinguish pain from no-pain conditions.