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Kulagina T.Yu.

Petrovsky National Research Centre of Surgery

Yelantsev S.V.

Intellect Technologies LLC

Zakatov M.D.

Intellect Technologies LLC

Egorov A.V.

Intellect Technologies LLC

Sandrikov V.A.

Petrovsky National Research Centre of Surgery

Extraction and analysis of cardiorespiratory exercise test data for personalization and monitoring of rehabilitation process effectiveness

Authors:

Kulagina T.Yu., Yelantsev S.V., Zakatov M.D., Egorov A.V., Sandrikov V.A.

More about the authors

Journal: Problems of Balneology, Physiotherapy and Exercise Therapy. 2025;102(5‑2): 66‑71

DOI: 10.17116/kurort202510205266

Read: 363 times

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Table of contents

EXTRACTION AND ANALYSIS OF CARDIORESPIRATORY EXERCISE TEST DATA FOR PERSONALIZATION AND MONITORING OF REHABILITATION PROCESS EFFECTIVENESS
EXTRACTION AND ANALYSIS OF CARDIORESPIRATORY EXERCISE TEST DATA FOR PERSONALIZATION AND MONITORING OF REHABILITATION PROCESS EFFECTIVENESS

To cite this article:

Kulagina TYu, Yelantsev SV, Zakatov MD, Egorov AV, Sandrikov VA. Extraction and analysis of cardiorespiratory exercise test data for personalization and monitoring of rehabilitation process effectiveness. Problems of Balneology, Physiotherapy and Exercise Therapy. 2025;102(5‑2):66‑71. (In Russ.)
https://doi.org/10.17116/kurort202510205266

Подписка 2026 Вопросы курортологии, физиотерапии и лечебной физической культуры (Problems of Balneology, Physiotherapy and Exercise Therapy)
МСФ на ЯМ
Использование инфографики авторами научных работ
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Abstract:

All over the world, there is a high difficulty in extracting data related to cardiopulmonary exercise testing (CPX) for analysis for methodological purposes.

OBJECTIVE

Of the research is to create the foundations of a system for extracting, integrating and consolidating load testing data, including an information model.

MATERIAL AND METHODS

Automatic generation of multidimensional data samples for specific nosologies and their sets, groups of patients when performing stress tests with the generation of a larger amount of data in the pathology of the cardiovascular system, small circle of blood circulation.

RESULTS

The analysis of cardiorespiratory testing, stress echocardiography and electrocardiography (graphic, digital, text) was performed. The results of the patient’s medical record (text, digital, graphic) are analyzed.

Keywords:

multimodal data
rehabilitation
stress tests

Authors:

Kulagina T.Yu.

Petrovsky National Research Centre of Surgery

Yelantsev S.V.

Intellect Technologies LLC

Zakatov M.D.

Intellect Technologies LLC

Egorov A.V.

Intellect Technologies LLC

Sandrikov V.A.

Petrovsky National Research Centre of Surgery

Received:

22.10.2025

Accepted:

31.10.2025

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References:

  1. Panza L, Piamonti D, Palange P. Pulmonary gas exchange and ventilatory efficiency during exercise in health and diseases. Expert Rev Respir Med. 2024 Jun;18(6):355-367.  https://doi.org/10.1080/17476348.2024.2370447
  2. Landsteiner I, Ikoma T, Lewis GD. Cardiopulmonary Exercise Testing in Advanced Heart Failure Management. Heart Fail Clin. 2025 Jan;21(1):35-49.  https://doi.org/10.1016/j.hfc.2024.09.001
  3. Ye J, Hai J, Song J, Wang Z. Multimodal Data Hybrid Fusion and Natural Language Processing for Clinical Prediction Models. AMIA Jt Summits Transl Sci Proc. 2024 May;2024:191-200. 
  4. Amal S, Safarnejad L, Omiye JA, et al. Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care. Front Cardiovasc Med. 2022;9:840262. https://doi.org/10.3389/fcvm.2022.840262
  5. Hadida Barzilai D, Cohen-Shelly M, Sorin V, et al. Machine learning in cardiac stress test interpretation: a systematic review. Eur Heart J Digit Health. 2024 Apr;5(4):401-408.  https://doi.org/10.1093/ehjdh/ztae027
  6. Krol K, Morgan MA, Khurana S. Pulmonary Function Testing and Cardiopulmonary Exercise Testing: An Overview. Med Clin North Am. 2019 May;103(3):565-576.  https://doi.org/10.1016/j.mcna.2018.12.014
  7. Rasekh A, Heidari R, Hosein A, et al. Robust Fusion of Time Series and Image Data for Improved Multimodal Clinical Prediction. IEEE Access. 2024;12:174107-174121. https://doi.org/10.1109/ACCESS.2024.3497668
  8. Kotenko KV, Mikhailova AA, Badimova AV, et al. Definition of prognostically significant markers of pre-clinical detection of predictors of obesity and metabolic disorders. Problems of Balneology, Physiotherapy and Exercise Therapy. 2023;100(5-2):21. (In Russ.).
  9. Korchazhkina NB, Mikhailova AA, Reshetova IV, et al. Modern approaches to developing a system of valid methods for monitoring individual health and maintaining active longevity. Problems of Balneology, Physiotherapy and Exercise Therapy. 2023;100(6):6-13. (In Russ.).
  10. Korchazhkina NB, Mikhailova AA, Badimova AV, et al. Methods and markers of pre-clinical diagnostics and monitoring of individual health and maintenance of active longevity. Problems of Balneology, Physiotherapy and Exercise Therapy. 2023;100(5-2):20-21. (In Russ.).
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