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

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

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dc.contributor.author Urdal, Jarle es_ES
dc.contributor.author Engan, Kjersti es_ES
dc.contributor.author Eftestøl, Trygve es_ES
dc.contributor.author Naranjo Ornedo, Valeriana es_ES
dc.contributor.author Haud, Ingunn Anda es_ES
dc.contributor.author Yeconia, Anita es_ES
dc.contributor.author Kidanto, Hussein es_ES
dc.contributor.author Ersdal, Hege es_ES
dc.date.accessioned 2021-04-23T03:31:28Z
dc.date.available 2021-04-23T03:31:28Z
dc.date.issued 2020-09 es_ES
dc.identifier.issn 0169-2607 es_ES
dc.identifier.uri http://hdl.handle.net/10251/165517
dc.description.abstract [EN] 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. es_ES
dc.description.sponsorship This work is part of the Safer Births project which has received funding from Laerdal Foundation, Laerdal Global Health, Skattefunn, Norwegian Ministry of Education and USAID. The work was partly supported by the Research Council of Norway through the Global Health and Vaccination Programme (GLOBVAC) project number 228203. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Computer Methods and Programs in Biomedicine es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Newborn resuscitation es_ES
dc.subject Activity recognition es_ES
dc.subject Automatic annotation es_ES
dc.subject Machine learning es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Automatic identification of stimulation activities during newborn resuscitation using ECG and accelerometer signals es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.cmpb.2020.105445 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/RCN//228203/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation Urdal, J.; Engan, K.; Eftestøl, T.; Naranjo Ornedo, V.; Haud, IA.; Yeconia, A.; Kidanto, H.... (2020). Automatic identification of stimulation activities during newborn resuscitation using ECG and accelerometer signals. Computer Methods and Programs in Biomedicine. 193:1-9. https://doi.org/10.1016/j.cmpb.2020.105445 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.cmpb.2020.105445 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 9 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 193 es_ES
dc.identifier.pmid 32283386 es_ES
dc.relation.pasarela S\408332 es_ES
dc.contributor.funder Research Council of Norway es_ES
dc.contributor.funder Laerdal Foundation for Acute Medicine es_ES
dc.contributor.funder United States Agency for International Development es_ES
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