Application of Neural Networks in Cardiovascular Load Analysis

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0198302" target="_blank" >RIV/00216305:26220/26:0198302 - isvavai.cz</a>
Result on the web
<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2025_sbornik_1.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2025_sbornik_1.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Application of Neural Networks in Cardiovascular Load Analysis
Original language description
This study investigates deep learning models for estimating aerobic (AeT) and anaerobic (AnT) thresholds using heart rate variability (HRV) analysis. Two CNN-LSTM architectures were developed: one predicting AeT and AnT values directly and another using signal delineation for enhanced threshold identification. The models were trained on HRV data from 119 subjects performing treadmill or cycle ergometer tests, with DFA alpha 1 used for threshold estimation. Performance evaluation showed an MAE of 4.67 bpm for AeT and 4.70 bpm for AnT in the first model, while the second model achieved 6.47 bpm for AeT and 3.15 bpm for AnT. Both models outperformed traditional DFA a1-based methods, with the second model demonstrating greater consistency in AnT detection. These results highlight the potential of deep learning for non-invasive en
Czech name
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Czech description
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Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings I of the 31st Conference STUDENT EEICT 2025
ISBN
978-80-214-6321-9
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
95-98
Publisher name
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Place of publication
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Event location
Brno
Event date
Apr 29, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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