TY - GEN
T1 - Signal processing and classification for identification of clinically important parameters during neonatal resuscitation
AU - Urdal, Jarle
AU - Engan, Kjersti
AU - Eftestol, Trygve
AU - Kidanto, Hussein
AU - Yarrot, Ladislaus Blacy
AU - Eilevstjonn, Joar
AU - Ersdal, Hege
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017
Y1 - 2017
N2 - Neonatal mortality is a global challenge. One million newborns die each year within their first 24 hours as a result of complications during labour and birth asphyxia. Most of these deaths happen in low resource settings. However, basic resuscitation at birth can increase newborn survival. Identification of initial factors and simple therapeutic strategies determinant for neonatal outcome can aid health care workers provide the best follow-up during resuscitation. In this work, the initial condition of the newborn, the treatment given, and early heart rate response from manual bag mask ventilation are parameterized. The features are investigated in a machine learning framework to identify which features are determinant for the different outcomes. Using a selection of the defined features, an identification rate of 89% for newborns in the normal group, and an identification rate of 74% for episodes ending in death was found. This points to the direction of identifying the important factors of newborn survival.
AB - Neonatal mortality is a global challenge. One million newborns die each year within their first 24 hours as a result of complications during labour and birth asphyxia. Most of these deaths happen in low resource settings. However, basic resuscitation at birth can increase newborn survival. Identification of initial factors and simple therapeutic strategies determinant for neonatal outcome can aid health care workers provide the best follow-up during resuscitation. In this work, the initial condition of the newborn, the treatment given, and early heart rate response from manual bag mask ventilation are parameterized. The features are investigated in a machine learning framework to identify which features are determinant for the different outcomes. Using a selection of the defined features, an identification rate of 89% for newborns in the normal group, and an identification rate of 74% for episodes ending in death was found. This points to the direction of identifying the important factors of newborn survival.
UR - http://www.scopus.com/inward/record.url?scp=85041416191&partnerID=8YFLogxK
U2 - 10.1109/ICSIPA.2017.8120672
DO - 10.1109/ICSIPA.2017.8120672
M3 - Conference contribution
AN - SCOPUS:85041416191
T3 - Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017
SP - 547
EP - 552
BT - Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017
Y2 - 12 September 2017 through 14 September 2017
ER -
