Your browser doesn't support javascript.
loading
Automatic identification of stimulation activities during newborn resuscitation using ECG and accelerometer signals.
Urdal, Jarle; Engan, Kjersti; Eftestøl, Trygve; Naranjo, Valery; Haug, Ingunn Anda; Yeconia, Anita; Kidanto, Hussein; Ersdal, Hege.
Afiliação
  • Urdal J; Department of Electrical Engineering and Computer Science, University of Stavanger, Norway. Electronic address: jarle.urdal@uis.no.
  • Engan K; Department of Electrical Engineering and Computer Science, University of Stavanger, Norway. Electronic address: kjersti.engan@uis.no.
  • Eftestøl T; Department of Electrical Engineering and Computer Science, University of Stavanger, Norway.
  • Naranjo V; Instituto de Investigación e Innovación en Bioingeniera (I3B), Universitat Politécnica de Valéncia, Spain.
  • Haug IA; Strategic Research, Laerdal Medical AS, Stavanger, Norway.
  • Yeconia A; Haydom Lutheran Hospital, Haydom, Manyara, Tanzania.
  • Kidanto H; School of Medicine, Aga Khan University, Dar es Salaam, Tanzania.
  • Ersdal H; Department of Anesthesiology and Intensive Care, Stavanger University Hospital, Norway; Dep. of Health Sciences, University of Stavanger, Norway.
Comput Methods Programs Biomed ; 193: 105445, 2020 Sep.
Article em En | MEDLINE | ID: mdl-32283386
ABSTRACT
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 very well documented as it has been difficult to investigate through observations. In this paper the main objective is to identify stimulation activities, regardless if the state of the newborn is changed or not, and produce timelines of the resuscitation episode with the identified stimulations.

METHODS:

Data is collected by utilizing a new heart rate device, NeoBeat, with dry-electrode ECG and accelerometer sensors placed on the abdomen of the newborn. We propose a method, NBstim, based on time domain and frequency domain features from the accelerometer signals and ECG signals from NeoBeat, to detect time periods of stimulation. NBstim use causal features from a gliding window of the signals, thus it can potentially be used in future realtime systems. A high performing feature subset is found using feature selection. System performance is computed using a leave-one-out cross-validation and compared with manual annotations.

RESULTS:

The system achieves an overall accuracy of 90.3% when identifying regions with stimulation activities.

CONCLUSION:

The performance indicates that the proposed NBstim, used with signals from the NeoBeat can be used to determine when stimulation is performed. The provided activity timelines, in combination with the status of the newborn, for example the heart rate, at different time points, can be studied further to investigate both the time spent and the effect of different newborn resuscitation parameters.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Asfixia Neonatal Tipo de estudo: Diagnostic_studies Limite: Humans / Newborn Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Asfixia Neonatal Tipo de estudo: Diagnostic_studies Limite: Humans / Newborn Idioma: En Ano de publicação: 2020 Tipo de documento: Article