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Clustering Analysis of Long-Term Cardiovascular Complications in COVID-19 Patients
Research Article  ·  Published: 16 May 2025
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Frontiers in Biomedical Signal Processing
Volume 1, Issue 1, 2026: 1-23
Research Article Open Access

Clustering Analysis of Long-Term Cardiovascular Complications in COVID-19 Patients

by
Seyed-Ali Sadegh-Zadeh Seyed-Ali Sadegh-Zadeh 1 ORCID
Alireza Soleimani Mamalo Alireza Soleimani Mamalo 2 *
Shayan Saadat Shayan Saadat 3 ORCID
Mahsa Behnemoon Mahsa Behnemoon 4
Masoud Ojarudi Masoud Ojarudi 5
Naser Gharebaghi Naser Gharebaghi 6
Mohammad Reza Pashaei Mohammad Reza Pashaei 7
Reza Hajizadeh Reza Hajizadeh 8
1 Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, United Kingdom
2 Student Research Committee, Urmia university of medical sciences, Urmia‚ Iran
3 Hull York Medical School, University of York, York, United Kingdom
4 Department of cardiology, school of medicine, Urmia University of medical sciences, Urmia, Iran
5 Department of Biochemistry, Faculty of Medicine, Urmia University of Medical Sciences, Urmia, Iran
6 Department of Infectious Diseases and Dermatology, School of Medicine, Taleghani Hospital, Urmia University of Medical Sciences, Urmia, Iran
7 Department of Internal Medicine, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran
8 Departement of cardiology, Urmia University of medical Sciences, Urmia, Iran
* Corresponding Author: Alireza Soleimani Mamalo, [email protected]
Volume 1, Issue 1
Volume 1, Issue 1 2026
Received: 31 March 2025 Accepted: 05 April 2025 Published: 16 May 2025 CrossMark
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DOI: 10.62762/FBSP.2025.731159
ARK: ark:/57805/fbsp.2025.731159
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Article Information

Published in Frontiers in Biomedical Signal Processing
Volume/Issue Volume 1, Issue 1, 2026
Pages 1-23

Abstract

This study employs K-means clustering to analyze long-term cardiovascular complications in COVID-19 patients through ECG parameters, demographics, comorbidities, and hospitalization data. Three distinct clusters emerged: Cluster 0 (moderate heart rate variability/ICU admissions), Cluster 1 (lower variability/admissions), and Cluster 2 (higher variability/admissions, indicating elevated risk). Bootstrap validation confirmed model robustness, supported by high silhouette scores and consistent cluster labels. The novel integration of multimodal data with machine learning revealed hidden cardiovascular outcome patterns, demonstrating clinical utility for risk stratification. Findings underscore the value of clustering techniques in personalizing post-COVID care and optimizing resource allocation for high-risk survivors.

Graphical Abstract

Clustering Analysis of Long-Term Cardiovascular Complications in COVID-19 Patients

Keywords

COVID-19 cardiovascular complications clustering analysis K-means ECG parameters

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, Alireza Soleimani Mamalo, upon reasonable request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

This study was conducted in accordance with the ethical principles and national norms and standards for conducting medical research in Iran, as approved by the Research Ethics Committee of Urmia University of Medical Sciences (Approval ID: IR.UMSU.REC.1403.234, Approval Date: 2024-10-30). Written informed consent was obtained from all participants. The researchers ensured compliance with all professional and legal requirements, maintaining the confidentiality and anonymity of participant data.

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Cite This Article

APA Style
Sadegh-Zadeh, S. A., Mamalo, A. S., Saadat, S., Behnemoon, M., Ojarudi, M., Gharebaghi, N., Pashaei, M. R., & Hajizadeh, R. (2025). Clustering Analysis of Long-term Cardiovascular Complications in COVID-19 Patients. Frontiers in Biomedical Signal Processing, 1(1), 37–59. https://doi.org/10.62762/FBSP.2025.731159
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TY  - JOUR
AU  - Sadegh-Zadeh, Seyed-Ali
AU  - Mamalo, Alireza Soleimani
AU  - Saadat, Shayan
AU  - Behnemoon, Mahsa
AU  - Ojarudi, Masoud
AU  - Gharebaghi, Naser
AU  - Pashaei, Mohammad Reza
AU  - Hajizadeh, Reza
PY  - 2025
DA  - 2025/05/16
TI  - Clustering Analysis of Long-Term Cardiovascular Complications in COVID-19 Patients
JO  - Frontiers in Biomedical Signal Processing
T2  - Frontiers in Biomedical Signal Processing
JF  - Frontiers in Biomedical Signal Processing
VL  - 1
IS  - 1
SP  - 1
EP  - 23
DO  - 10.62762/FBSP.2025.731159
UR  - https://www.icck.org/article/abs/FBSP.2025.731159
KW  - COVID-19
KW  - cardiovascular complications
KW  - clustering analysis
KW  - K-means
KW  - ECG parameters
AB  - This study employs K-means clustering to analyze long-term cardiovascular complications in COVID-19 patients through ECG parameters, demographics, comorbidities, and hospitalization data. Three distinct clusters emerged: Cluster 0 (moderate heart rate variability/ICU admissions), Cluster 1 (lower variability/admissions), and Cluster 2 (higher variability/admissions, indicating elevated risk). Bootstrap validation confirmed model robustness, supported by high silhouette scores and consistent cluster labels. The novel integration of multimodal data with machine learning revealed hidden cardiovascular outcome patterns, demonstrating clinical utility for risk stratification. Findings underscore the value of clustering techniques in personalizing post-COVID care and optimizing resource allocation for high-risk survivors.
SN  - 3071-2912
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
BibTeX Format
Compatible with LaTeX, BibTeX, and other reference managers
@article{SadeghZadeh2025Clustering,
  author = {Seyed-Ali Sadegh-Zadeh and Alireza Soleimani Mamalo and Shayan Saadat and Mahsa Behnemoon and Masoud Ojarudi and Naser Gharebaghi and Mohammad Reza Pashaei and Reza Hajizadeh},
  title = {Clustering Analysis of Long-Term Cardiovascular Complications in COVID-19 Patients},
  journal = {Frontiers in Biomedical Signal Processing},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {1-23},
  doi = {10.62762/FBSP.2025.731159},
  url = {https://www.icck.org/article/abs/FBSP.2025.731159},
  abstract = {This study employs K-means clustering to analyze long-term cardiovascular complications in COVID-19 patients through ECG parameters, demographics, comorbidities, and hospitalization data. Three distinct clusters emerged: Cluster 0 (moderate heart rate variability/ICU admissions), Cluster 1 (lower variability/admissions), and Cluster 2 (higher variability/admissions, indicating elevated risk). Bootstrap validation confirmed model robustness, supported by high silhouette scores and consistent cluster labels. The novel integration of multimodal data with machine learning revealed hidden cardiovascular outcome patterns, demonstrating clinical utility for risk stratification. Findings underscore the value of clustering techniques in personalizing post-COVID care and optimizing resource allocation for high-risk survivors.},
  keywords = {COVID-19, cardiovascular complications, clustering analysis, K-means, ECG parameters},
  issn = {3071-2912},
  publisher = {Institute of Central Computation and Knowledge}
}

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