A1 Refereed original research article in a scientific journal
Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life
Authors: Feli, Mohammad; Azimi, Iman; Sarhaddi, Fatemeh; Sharifi-Heris, Zahra; Niela-Vilen, Hannakaisa; Liljeberg, Pasi; Axelin, Anna; Rahmani, Amir M.
Publisher: Nature Research
Publication year: 2024
Journal: Scientific Reports
Journal name in source: Scientific reports
Journal acronym: Sci Rep
Article number: 19896
Volume: 14
Issue: 1
ISSN: 2045-2322
eISSN: 2045-2322
DOI: https://doi.org/10.1038/s41598-024-70773-0
Web address : https://doi.org/10.1038/s41598-024-70773-0
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/457726124
Preterm birth (PTB) remains a global health concern, impacting neonatal mortality and lifelong health consequences. Traditional methods for estimating PTB rely on electronic health records or biomedical signals, limited to short-term assessments in clinical settings. Recent studies have leveraged wearable technologies for in-home maternal health monitoring, offering continuous assessment of maternal autonomic nervous system (ANS) activity and facilitating the exploration of PTB risk. In this paper, we conduct a longitudinal study to assess the risk of PTB by examining maternal ANS activity through heart rate (HR) and heart rate variability (HRV). To achieve this, we collect long-term raw photoplethysmogram (PPG) signals from 58 pregnant women (including seven preterm cases) from gestational weeks 12-15 to three months post-delivery using smartwatches in daily life settings. We employ a PPG processing pipeline to accurately extract HR and HRV, and an autoencoder machine learning model with SHAP analysis to generate explainable abnormality scores indicative of PTB risk. Our results reveal distinctive patterns in PTB abnormality scores during the second pregnancy trimester, indicating the potential for early PTB risk estimation. Moreover, we find that HR, average of interbeat intervals (AVNN), SD1SD2 ratio, and standard deviation of interbeat intervals (SDNN) emerge as significant PTB indicators.
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Funding information in the publication:
This research was supported by the Academy of Finland through the SLIM Project (grant numbers 316810 and 316811) and the U.S. National Science Foundation through the UNITE Project (grant number SCC CNS-1831918).
