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Continuous Radar-based Heart Rate Monitoring using Autocorrelation-based Algorithm in Intensive Care Unit




TekijätSeifizarei, Sepehr; Elnaggar, Ismail; Anzanpour, Arman; Sandelin, Jonas; Lahdenoja, Olli; Glassee, Miguel; Castro, Ivan D.; Torfs, Tom; van de Poll, Marcel C. G.; Airola, Antti; Kaisti, Matti; Koivisto, Tero

KustantajaIEEE

Julkaisuvuosi2025

JournalIEEE Journal of Biomedical and Health Informatics

Tietokannassa oleva lehden nimiIEEE Journal of Biomedical and Health Informatics

ISSN2168-2208

eISSN2168-2208

DOIhttps://doi.org/10.1109/jbhi.2025.3527566

Verkko-osoitehttps://ieeexplore.ieee.org/document/10834566

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/477790260


Tiivistelmä

This study presents a radar-based algorithm for non-invasive heart rate monitoring in intensive care units (ICUs) using a 140 GHz Frequency-Modulated Continuous Wave (FMCW) radar, placed unobtrusively beneath hospital beds. Data were collected from 15 post-operative cardiac patients at Maastricht University Hospital, with an ECG device serving as the ground truth for validation. The proposed algorithm includes data preprocessing, channel selection, heart rate estimation, and post-processing, employing autocorrelation to detect rhythmic patterns and quality metrics to ensure reliable channel selection. The system achieved a mean absolute error (MAE) of 2.22 beats per minute (bpm) with 66% overall coverage, increasing to 98% during sinus rhythm periods. This approach demonstrates robust performance in challenging ICU environments by mitigating noise and motion artifacts and optimizing computational efficiency. These findings highlight the potential of radar-based systems to enhance patient care through continuous, non-invasive vital sign monitoring and validate the algorithm's effectiveness in real-world clinical scenarios.


Ladattava julkaisu

This is an electronic reprint of the original article.
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Julkaisussa olevat rahoitustiedot
This study was conducted as part of the Moore4Medical Project (M4M), which received funding from the Electronic Components and Systems for European Leadership Joint Undertaking (ECSEL JU) in collaboration with the European Union’s H2020 Framework Programme (H2020/2014-2020) and National Authorities, under grant agreement H2020- ECSEL-2019-IA-876190. We appreciate the support provided by this initiative in enabling our research.


Last updated on 2025-27-01 at 19:57