Automatic identification of stimulation activities during newborn resuscitation using ECG and accelerometer signals

Jarle Urdal, Kjersti Engan, Trygve Eftestøl, Valery Naranjo, Ingunn Anda Haug, Anita Yeconia, Hussein Kidanto, Hege Ersdal

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Article number105445
JournalComputer Methods and Programs in Biomedicine
Publication statusPublished - Sept 2020


  • Activity recognition
  • Automatic annotation
  • Machine learning
  • Newborn resuscitation


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