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Related Experiment Video

Updated: Aug 29, 2025

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates
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Aggregate Channel Features for newborn face detection in Neonatal Intensive Care Units.

Benedetta Olmi, Claudia Manfredi, Lorenzo Frassineti

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary
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    A new automated system accurately detects newborn faces, aiding in identifying neurological issues like seizures in Neonatal Intensive Care Units. This technology shows promise in distinguishing between seizure types based on facial movement analysis.

    Area of Science:

    • Computer Vision and Machine Learning
    • Neonatal Neurology and Clinical Neurophysiology

    Background:

    • Neurological dysfunctions, such as seizures, in Neonatal Intensive Care Units (NICUs) can be indicated by facial changes.
    • Developing automated face detection for newborns is challenging due to a lack of large public datasets.
    • Previous approaches have been semi-automatic, limiting widespread clinical application.

    Purpose of the Study:

    • To develop and evaluate a fully automated face detector for newborns in NICUs.
    • To assess the system's performance in detecting subtle facial movement differences related to seizure activity.
    • To explore the potential of automated facial analysis for differentiating seizure types in neonates.

    Main Methods:

    • Implementation of the Aggregate Channel Feature (ACF) algorithm for face detection.

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    Last Updated: Aug 29, 2025

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  • Testing the automated system on video recordings from 42 full-term newborns.
  • Analysis of performance metrics including log-Average Miss Rate and Average Precision Recall.

    • Main Results:

    • The developed face detector achieved promising results with a log-Average Miss Rate of 0.47 ± 0.05 and Average Precision Recall of 0.61 ± 0.05.
    • Statistically significant differences in performance metrics were observed between newborns with and without seizures.
    • The system highlighted distinct facial movement patterns differentiating newborns with electro-clinical seizures from those with electrographic-only or no seizures.

    Conclusions:

    • The proposed fully automated face detection system demonstrates clinical relevance for neonatal neurological assessment.
    • Quantitative analysis of facial movements can aid in the rapid assessment of pain or seizure events in NICU settings.
    • This technology has the potential to improve the timely diagnosis and management of seizures in newborns.