Publication result detail

Application of Neural Networks in Cardiovascular Load Analysis

JAROŠ, O.; JANOUŠEK, O.

Original Title

Application of Neural Networks in Cardiovascular Load Analysis

English Title

Application of Neural Networks in Cardiovascular Load Analysis

Type

Paper in proceedings outside WoS and Scopus

Original Abstract

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

English abstract

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

Keywords

Heart rate variability, Detrended Fluctuation Analysis, Aerobic, Anaerobic, Neural networks

Key words in English

Heart rate variability, Detrended Fluctuation Analysis, Aerobic, Anaerobic, Neural networks

Authors

JAROŠ, O.; JANOUŠEK, O.

Released

29.04.2025

ISBN

978-80-214-6321-9

Book

Proceedings I of the 31st Conference STUDENT EEICT 2025

Edition

1

Pages from

95

Pages to

98

Pages count

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"
}