Artificial intelligence-based neonatal heart rate monitoring technologies: Systematic review

doi:10.5409/wjcp.119386

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World Journal of Clinical Pediatrics

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2219-2808

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Systematic Reviews

Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Clin Pediatr. Sep 9, 2026; 15(3): 119386
Published online Sep 9, 2026. doi: 10.5409/wjcp.119386

Artificial intelligence-based neonatal heart rate monitoring technologies: Systematic review

Pankaj Soni, Mukesh Kumar Yadav, Shailendra Kumar, Bal Krishan Bansal

Pankaj Soni, Shailendra Kumar, Department of Neonatology, Thumbay University Hospital, Ajman 4184, United Arab Emirates

Pankaj Soni, Shailendra Kumar, Department of Clinical Sciences (Pediatric Neonatology), College of Medicine, Gulf Medical University, Ajman 4184, United Arab Emirates

Mukesh Kumar Yadav, Department of Cardiology, National Institute of Medical Sciences and Research, NIMS University, Jaipur 303121, Rajasthan, India

Bal Krishan Bansal, Department of Pediatrics, Bansal Child Care Centre, Faridabad 121005, Haryana, India

Author contributions: Soni P designed the study and prepared the original draft; Kumar S performed validation and supervision; Yadav MK and Bansal BK reviewed and edited the manuscript. All authors read and approved the final version.

AI contribution statement: During manuscript preparation, limited use of AI-based tools (along with Grammarly) was made to assist with grammar correction. All AI-assisted text was carefully reviewed and revised.

Conflict-of-interest statement: The authors declare no conflicts of interest.

PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.

Corresponding author: Pankaj Soni, MRCPCH, FRCPCH, Specialist Pediatrician and Clinical Lecturer, Department of Neonatology, Thumbay University Hospital, Al Jurf, Ajman 4184, United Arab Emirates. ps3858@gmail.com

Received: January 26, 2026
Revised: February 7, 2026
Accepted: March 17, 2026
Published online: September 9, 2026
Processing time: 186 Days and 16.8 Hours

Abstract

BACKGROUND

Neonatal heart rate (HR) is an important parameter in the evaluation of newborn health and viability in the immediate postnatal period.

AIM

To evaluate the accuracy, reliability, and clinical applicability of emerging non-contact and artificial intelligence (AI)-assisted HR monitoring technologies in neonates compared to conventional electrocardiography (ECG)-based systems.

METHODS

A comprehensive literature search was conducted across PubMed, EMBASE, Google Scholar, and Cochrane databases from January 2013 through June 2025 following PRISMA guidelines.

RESULTS

The analysis revealed a progressive shift from contact-based ECG and pulse oximetry to camera-based photoplethysmography, thermal imaging, and AI-enhanced multimodal systems. These newer methods demonstrated a strong correlation with ECG readings, rapid signal acquisition, and improved robustness against motion and lighting variability.

CONCLUSION

Emerging non-contact, AI-assisted HR monitoring technologies offer accurate, safe, and efficient alternatives for neonatal care, supporting faster clinical decisions and improved outcomes. Future multicenter studies are required to validate accuracy and confirm clinical utility before routine clinical implementation.

Keywords: Artificial intelligence; Electrocardiography; Neonatal heart rate; Non-contact monitoring; Photoplethysmography

Core Tip: Electrocardiography (ECG) remains the reference standard for neonatal heart rate (HR) measurement, providing the fastest and most accurate detection during resuscitation. Limitations include electrode placement delays, skin fragility, and motion artifacts. Existing contact-based methods such as auscultation, palpation, and pulse oximetry are widely used but have limitations such as reduced reliability and dependency on perfusion, especially in preterm or unstable neonates. Emerging non-contact and artificial intelligence (AI)-enhanced technologies, including camera-based photoplethysmography, thermal imaging, Doppler, and multimodal AI systems, demonstrate promising accuracy compared with ECG,with faster signal acquisition and improved robustness against motion and lighting variability. This systematic review (2013 –2025) highlights the shift toward safe, contact-free, real-time HR monitoring while noting limited evidence during critical neonatal resuscitation scenarios.