Automatic identification of stimulation activities during newborn resuscitation using ECG and accelerometer signals
作者: Jarle UrdalKjersti EnganTrygve EftestølValery NaranjoIngunn Anda HaugAnita YeconiaHussein KidantoHege Ersdal
作者单位: 1Department of Electrical Engineering and Computer Science, University of Stavanger, Norway
2Instituto de Investigación e Innovación en Bioingeniera (I3B), Universitat Politécnica de Valéncia, Spain
3Strategic Research, Laerdal Medical AS, Stavanger, Norway
4Haydom Lutheran Hospital, Haydom, Manyara, Tanzania
5School of Medicine, Aga Khan University, Dar es Salaam, Tanzania
6Department of Anesthesiology and Intensive Care, Stavanger University Hospital, Norway
7Dep. of Health Sciences, University of Stavanger, Norway
刊名: Computer Methods and Programs in Biomedicine, 2020, Vol.193
来源数据库: Elsevier Journal
DOI: 10.1016/j.cmpb.2020.105445
关键词: Newborn resuscitationActivity recognitionAutomatic annotationMachine learning
原始语种摘要: Abstract(#br)Background and Objective: Early neonatal death is a worldwide challenge with 1 million newborn deaths every year. The primary cause of these deaths are complications during labour and birth asphyxia. The majority of these newborns could have been saved with adequate resuscitation at birth. Newborn resuscitation guidelines recommend immediate drying, stimulation, suctioning if indicated, and ventilation of non-breathing newborns. A system that will automatically detect and extract time periods where different resuscitation activities are performed, would be highly beneficial to evaluate what resuscitation activities that are improving the state of the newborn, and if current guidelines are good and if they are followed. The potential effects of especially stimulation are not...
全文获取路径: Elsevier  (合作)
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影响因子:1.555 (2012)

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