{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T06:08:15Z","timestamp":1788761295223,"version":"build-2803163510"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Netw. Physiol."],"abstract":"\n Hypoxic-ischemic encephalopathy (HIE) remains a critical challenge in neonatal intensive care, requiring precise bedside monitoring to mitigate long-term neurological morbidity. While individual assessment of cerebral and cardiovascular activity is standard, the dynamic interaction between these systems provides untapped diagnostic value. This pilot study investigates neuro-cardiac coupling using synchronized long-term EEG and ECG recordings, focusing primarily on a core subset of 61 neonates classified by clinical HIE severity (Sarnat Stage 1 vs. Stage 2). We employed cross-modal analysis to evaluate the relationship between the amplitude-integrated EEG (aEEG) envelope and heart rate variability (HRV) metrics. Our results demonstrate that neuro-cardiac interaction is significantly modulated by HIE severity and electrocortical background patterns. Neonates with healthy continuous normal voltage (CNV) patterns exhibited robust neuro-cardiac synchronization (median Spearman\u2019s\n \n \n \n r<\/mml:mi>\n =<\/mml:mo>\n 0.22<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ), which was significantly reduced by approximately 60% in patients with abnormal EEG backgrounds (median Spearman\u2019s\n \n \n \n r<\/mml:mi>\n =<\/mml:mo>\n 0.09<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ,\n \n \n \n p<\/mml:mi>\n =<\/mml:mo>\n 0.015<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ). To ensure robust translation, machine learning models were evaluated across diverse parameter grids. While isolated hyperparameter configurations yielded peak performance metrics\u2013such as an overall maximum accuracy of 90.22% using combined cardiocycle and spectral ECG features with XGBoost\u2013the feature space demonstrated strong, un-biased baseline stability, yielding reliable average accuracies across window lengths (e.g., 77.27% mean\n \n \n \n \n \n F<\/mml:mi>\n <\/mml:mrow>\n \n 1<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n -score for EEG-based Logistic Regression and 72.73% mean\n \n \n \n \n \n F<\/mml:mi>\n <\/mml:mrow>\n \n 1<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n for ECG-based XGBoost). These findings suggest that multi-modal coupling metrics offer enhanced physiological insight for automated HIE grading in a pilot setting.\n <\/jats:p>","DOI":"10.3389\/fnetp.2026.1889331","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T05:33:40Z","timestamp":1788759220000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantitative assessment of heart-brain coupling in neonates with hypoxic-ischemic encephalopathy"],"prefix":"10.3389","volume":"6","author":[{"given":"Danylo","family":"Reznichenko","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Igor Sikorsky Kyiv Polytechnic Institute","place":["Kyiv, Ukraine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alina","family":"Frunza","sequence":"additional","affiliation":[{"name":"Scientific Department with an Innovative Development Sector, Bukovinian State Medical University, Central Municipal Clinical Hospital","place":["Chernivtsi, Ukraine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anastasiya","family":"Babintseva","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Neonatology and Perinatal Medicine, Bukovinian State Medical University","place":["Chernivtsi, Ukraine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yevhenii","family":"Karpliuk","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Igor Sikorsky Kyiv Polytechnic Institute","place":["Kyiv, Ukraine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oleksandr","family":"Romanchuk","sequence":"additional","affiliation":[{"name":"Department of Internal and Family Medicine, Lesya Ukrainka Volyn National University","place":["Lutsk, Ukraine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anton","family":"Popov","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Igor Sikorsky Kyiv Polytechnic Institute","place":["Kyiv, Ukraine"]},{"name":"Faculty of Applied Sciences, Ukrainian Catholic University","place":["Lviv, Ukraine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kateryna","family":"Ivanko","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Igor Sikorsky Kyiv Polytechnic Institute","place":["Kyiv, Ukraine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Illya","family":"Chaikovsky","sequence":"additional","affiliation":[{"name":"V. M. Glushkov Institute of Cybernetics of the NAS of Ukraine","place":["Kyiv, Ukraine"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1242505","DOI":"10.3389\/fnetp.2023.1242505","article-title":"Time-varying information measures: an adaptive estimation of information storage with application to brain-heart interactions","volume":"3","author":"Antonacci","year":"2023","journal-title":"Front. Netw. 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