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Lookup NU author(s): Dr Quoc Vuong
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 by the authors.Infant electrocardiograms (ECGs) and heart rates (HRs) are very useful biosignals for psychological research and clinical work, but can be hard to analyse properly, particularly longform (≥5 min) recordings taken in naturalistic environments. Infant HRs are typically much faster than adult HRs, and so some of the underlying frequency assumptions made about adult ECGs may not hold for infants. However, the bulk of publicly available ECG approaches focus on adult data. Here, existing open source ECG approaches are tested on infant datasets. The best-performing open source method is then modified to maximise its performance on infant data (e.g., including a 15 Hz high-pass filter, adding local peak correction). The HR signal is then subsequently analysed, developing an approach for cleaning data with separate sets of parameters for the analysis of cleaner and noisier HRs. A Signal Quality Index (SQI) for HR is also developed, providing insights into where a signal is recoverable and where it is not, allowing for more confidence in the analysis performed on naturalistic recordings. The tools developed and reported in this paper provide a base for the future analysis of infant ECGs and related biophysical characteristics. Of particular importance, the proposed solutions outlined here can be efficiently applied to real-world, large datasets.
Author(s): Mason HT, Martinez-Cedillo AP, Vuong QC, Garcia-de-Soria MC, Smith S, Geangu E, Knight MI
Publication type: Article
Publication status: Published
Journal: Signals
Year: 2024
Volume: 5
Issue: 1
Pages: 118-146
Online publication date: 13/03/2024
Acceptance date: 06/03/2024
Date deposited: 08/04/2024
ISSN (electronic): 2624-6120
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
URL: https://doi.org/10.3390/signals5010007
DOI: 10.3390/signals5010007
Data Access Statement: All data and software will be made available upon reasonable request sent to Elena Geangu (elena.geangu@york.ac.uk).
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