Signal processing and classification for identification of clinically important parameters during neonatal resuscitation

Urdal, Jarle; Engan, Kjersti; Eftestøl, Trygve; Kidanto, Hussein; Yarrot, Ladislaus Blacy; Eilevstjønn, Joar; Ersdal, Hege

doi:10.1109/icsipa.2017.8120672

Cited by 3 publications

(1 citation statement)

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“…al. [19] signal processing and machine learning is utilized to find important parameters for survival after newborn resuscitation. Here it is shown that in addition to clinical parameters as birth weight and gestational age, the therapeutic strategies, i.e.…”

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confidence: 99%

Analysis and Knowledge Extraction of Newborn Resuscitation Activities from Annotation Files

Abukmeil,

Meinich-Bache,

Eftestøl

et al. 2024

Preprint

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Deprivation of oxygen in a newbornduring and after birth leads to birth asphyxia, which is considered one of the leading causes of death in the neonatal period. Adequate resuscitation activities are performed immediately after birth to save the majority of newborns. The primary resuscitation activities include ventilation, stimulation, drying, suction, and chest compression. While resuscitation guidelines exist, little research has been conducted on measured resuscitation episodes. Modeling the executed resuscitation activities to generate temporal data and extract knowledge can provide unique insights into dominant resuscitation activities. It also aids in constructing a resuscitation timeline to visually represent and describe the actions performed on a newborn.In this paper, we propose a method for generating and encoding temporal resuscitation data, enabling the description and visualization of the resuscitation timeline. We utilize neighborhood component analysis (NCA) to cluster the generated data based on the presence of ventilation and the outcome of the newborn. Additionally, we employ an autoencoder (AE) model to enhance clustering performance by visualizing its latent space.Our proposed method demonstrates high-quality visual clustering results on two different datasets. It provides insights into the intricate structure of the generated resuscitation data by grouping similar unlabeled resuscitation episodes into coherent clusters.

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confidence: 99%

Analysis and Knowledge Extraction of Newborn Resuscitation Activities from Annotation Files

Abukmeil,

Meinich-Bache,

Eftestøl

et al. 2024

Preprint

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Analysis and knowledge extraction of newborn resuscitation activities from annotation files

Abukmeil,

Meinich-Bache,

Eftestøl

et al. 2024

BMC Med Inform Decis Mak

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Deprivation of oxygen in an infant during and after birth leads to birth asphyxia, which is considered one of the leading causes of death in the neonatal period. Adequate resuscitation activities are performed immediately after birth to save the majority of newborns. The primary resuscitation activities include ventilation, stimulation, drying, suction, and chest compression. While resuscitation guidelines exist, little research has been conducted on measured resuscitation episodes. Objective data collected for measuring and registration of the executed resuscitation activities can be used to generate temporal timelines. This paper is primarily aimed to introduce methods for analyzing newborn resuscitation activity timelines, through visualization, aggregation, redundancy and dimensionality reduction. We are using two datasets: 1) from Stavanger University Hospital with 108 resuscitation episodes, and 2) from Haydom Lutheran Hospital with 76 episodes. The resuscitation activity timelines were manually annotated, but in future work we will use the proposed method on automatically generated timelines from video and sensor data. We propose an encoding generator with unique codes for combination of activities. A visualization of aggregated episodes is proposed using sparse nearest neighbor graph, shown to be useful to compare datasets and give insights. Finally, we propose a method consisting of an autoencoder trained for reducing redundancy in encoded resuscitation timeline descriptions, followed by a neighborhood component analysis for dimensionality reduction. Visualization of the resulting features shows very good class separability and potential for clustering the resuscitation files according to the outcome of the newborns as dead, admitted to NICU or normal. This shows great potential for extracting important resuscitation patterns when tested on larger datasets.

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Signal Processing for Newborn Survival from labour to resuscitation

Urdal¹

2020

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Signal processing and classification for identification of clinically important parameters during neonatal resuscitation

Cited by 3 publications

References 13 publications

Analysis and Knowledge Extraction of Newborn Resuscitation Activities from Annotation Files

Analysis and Knowledge Extraction of Newborn Resuscitation Activities from Annotation Files

Analysis and knowledge extraction of newborn resuscitation activities from annotation files

Signal Processing for Newborn Survival from labour to resuscitation

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