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JAROŠ, O.; JANOUŠEK, O.
Originální název
Application of Neural Networks in Cardiovascular Load Analysis
Anglický název
Druh
Stať ve sborníku mimo WoS a Scopus
Originální abstrakt
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
Anglický abstrakt
Klíčová slova
Heart rate variability, Detrended Fluctuation Analysis, Aerobic, Anaerobic, Neural networks
Klíčová slova v angličtině
Autoři
Vydáno
29.04.2025
ISBN
978-80-214-6321-9
Kniha
Proceedings I of the 31st Conference STUDENT EEICT 2025
Edice
1
Strany od
95
Strany do
98
Strany počet
4
URL
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2025_sbornik_1.pdf
BibTex
@inproceedings{BUT198302, author="Oliver {Jaroš} and Oto {Janoušek}", title="Application of Neural Networks in Cardiovascular Load Analysis", booktitle="Proceedings I of the 31st Conference STUDENT EEICT 2025", year="2025", series="1", pages="95--98", isbn="978-80-214-6321-9", url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2025_sbornik_1.pdf" }