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Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring. Part 2: artificial intelligence

Sitaula, Chiranjibi; Grooby, Ethan; Kwok, T’ng Chang; Sharkey, Don; Marzbanrad, Faezeh; Malhotra, Atul

Authors

Chiranjibi Sitaula

Ethan Grooby

TNG KWOK Tng.Kwok@nottingham.ac.uk
Clinical Research Fellow in Neonatal Medicine

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DON SHARKEY don.sharkey@nottingham.ac.uk
Professor of Neonatal Medicine and Technologies

Faezeh Marzbanrad

Atul Malhotra



Abstract

Background: With the development of Artificial Intelligence (AI) techniques, smart health monitoring, particularly neonatal cardiorespiratory monitoring with wearable devices, is becoming more popular. To this end, it is crucial to investigate the trend of AI and wearable sensors being developed in this domain. Methods: We performed a review of papers published in IEEE Xplore, Scopus, and PubMed from the year 2000 onwards, to understand the use of AI for neonatal cardiorespiratory monitoring with wearable technologies. We reviewed the advances in AI development for this application and potential future directions. For this review, we assimilated machine learning (ML) algorithms developed for neonatal cardiorespiratory monitoring, designed a taxonomy, and categorised the methods based on their learning capabilities and performance. Results: For AI related to wearable technologies for neonatal cardio-respiratory monitoring, 63% of studies utilised traditional ML techniques and 35% utilised deep learning techniques, including 6% that applied transfer learning on pre-trained models. Conclusions: A detailed review of AI methods for neonatal cardiorespiratory wearable sensors is presented along with their advantages and disadvantages. Hierarchical models and suggestions for future developments are highlighted to translate these AI technologies into patient benefit. Impact: State-of-the-art review in artificial intelligence used for wearable neonatal cardiorespiratory monitoring.Taxonomy design for artificial intelligence methods.Comparative study of AI methods based on their advantages and disadvantages.

Citation

Sitaula, C., Grooby, E., Kwok, T. C., Sharkey, D., Marzbanrad, F., & Malhotra, A. (2023). Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring. Part 2: artificial intelligence. Pediatric Research, 93(2), 426-436. https://doi.org/10.1038/s41390-022-02417-w

Journal Article Type Article
Acceptance Date Nov 29, 2022
Online Publication Date Dec 13, 2022
Publication Date 2023-01
Deposit Date Feb 25, 2023
Publicly Available Date Jun 14, 2023
Print ISSN 0031-3998
Electronic ISSN 1530-0447
Publisher Nature Publishing Group
Peer Reviewed Peer Reviewed
Volume 93
Issue 2
Pages 426-436
DOI https://doi.org/10.1038/s41390-022-02417-w
Public URL https://nottingham-repository.worktribe.com/output/17778365
Publisher URL https://www.nature.com/articles/s41390-022-02417-w