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Clustering Analysis of Long-Term Cardiovascular Complications in COVID-19 Patients
Research Article | Published: 16 May 2025
Frontiers in Biomedical Signal Processing Volume 1, Issue 1, 2025: 1-23
Volume 1, Issue 1, Frontiers in Biomedical Signal Processing
Volume 1, Issue 1, 2025
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Kiran Kumar Patro
Kiran Kumar Patro
Aditya Institute of Technology and Management, India
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Frontiers in Biomedical Signal Processing, Volume 1, Issue 1, 2025: 1-23

Open Access | Research Article | 16 May 2025
Clustering Analysis of Long-Term Cardiovascular Complications in COVID-19 Patients
by
Seyed-Ali Sadegh-Zadeh Seyed-Ali Sadegh-Zadeh 1
Alireza Soleimani Mamalo Alireza Soleimani Mamalo 2 *
Shayan Saadat Shayan Saadat 3
Mahsa Behnemoon Mahsa Behnemoon 4
Masoud Ojarudi Masoud Ojarudi 5
Naser Gharebaghi Naser Gharebaghi 6
Mohammad Reza Pashaei Mohammad Reza Pashaei 7
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
* Corresponding Author: Alireza Soleimani Mamalo, [email protected]
DOI: 10.62762/FBSP.2025.731159
Received: 31 March 2025, Accepted: 05 April 2025, Published: 16 May 2025  
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Abstract
This study investigates long-term cardiovascular sequelae in COVID-19 survivors using advanced clustering methodologies. By analyzing ECG parameters, demographic information, comorbidities, and hospitalization data, three distinct clusters were identified based on heart rate variability (HRV) and ICU admissions. Cluster 0 exhibited moderate HRV with ICU admissions, Cluster 1 showed lower HRV alongside ICU admissions, and Cluster 2 displayed higher HRV with ICU admissions, all suggesting varying levels of cardiovascular risk. The robustness and stability of the clusters were validated through bootstrapping, confirming the reliability of the model. The findings underscore significant heterogeneity in post-COVID-19 cardiovascular outcomes, highlighting the need for tailored post-recovery care strategies. The clustering model demonstrates potential as a clinical decision support tool for early identification of high-risk patients and optimization of healthcare resources, ultimately improving patient outcomes.
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
Data will be made available on 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
Not applicable.
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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. (2025). Clustering Analysis of Long-Term Cardiovascular Complications in COVID-19 Patients. Frontiers in Biomedical Signal Processing, 1(1), 1–23. https://doi.org/10.62762/FBSP.2025.731159
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