Abstract
Purpose
Excessive intensity exercises can bring irreversible damage to the heart. We explore whether heart sounds can evaluate cardiac function after high-intensity exercise and hope to prevent overtraining through the changes of heart sound in future training.
Methods
The study population consisted of 25 male athletes and 24 female athletes. All subjects were healthy and had no history of cardiovascular disease or family history of cardiovascular disease. The subjects were required to do high-intensity exercise for 3 days, with their blood sample and heart sound (HS) signals being collected and analysed before and after exercise. We then developed a Kernel extreme learning machine (KELM) model that can distinguish the state of heart by using the pre- and post-exercise data.
Results
There was no significant change in serum cardiac troponin I after 3 days of load cross-country running, which indicates that there was no myocardial injury after the race. The statistical analysis of time-domain characteristics and multi-fractal characteristic parameters of HS showed that the cardiac reserve capacity of the subjects was enhanced after the cross-country running, and the KELM is an effective classifier to recognize HS and the state of the heart after exercise.
Conclusion
Through the results, we can draw the conclusion that this intensity of exercise will not cause profound damage to the athlete’s heart. The findings of this study are of great significance for evaluating the condition of the heart with the proposed index of heart sound and prevention of excessive training that causes damage to the heart.
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Data availability
Data are available from the corresponding authors on reasonable request.
Abbreviations
- D:
-
Diastole
- ELM:
-
Extreme learning machine
- HS:
-
Heart sound
- cTnI:
-
Cardiac troponin I
- KELM:
-
Kernel extreme learning machine
- MF-DFA:
-
Multifractal detrended fluctuation analysis
- NT-proBNP:
-
N-terminal forebrain natriuretic peptide
- HR:
-
Heart rate
- S1:
-
The first heart sound
- S2:
-
The second heart sound
- S:
-
Systole
- SVM:
-
Support vector machine
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Acknowledgements
The author is particularly grateful for the financial support of the National Natural Science Foundation of China.
Funding
This work received the support of the National Natural Science Foundation of China, grant No.31870980, 31570003 and 31800823.
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GX conceived and designed the study. LK provided experimental needs. WM, LC, Z, YX and LC performed the experiments, WM, ZY wrote the paper. ZY, GX reviewed the manuscript. All authors read and approved the manuscript.
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The authors have no relevant financial or non-financial interests to disclose.
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The study was carried out according to the latest revision of the declaration of Helsinki. This study was approved by the ethics committee of the Third Military Medical University. All volunteers signed a written informed consent before the study intervention and were informed of their rights to withdraw from the study at any time without having to provide a reason.
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Communicated by Ellen Adele Dawson.
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Wang, M., Lv, C., Zhang, Y. et al. Analysis and recognition of post-exercise cardiac state based on heart sound features and cardiac troponin I. Eur J Appl Physiol 123, 2461–2471 (2023). https://doi.org/10.1007/s00421-023-05245-w
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DOI: https://doi.org/10.1007/s00421-023-05245-w