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Peer-reviewedCC BY 4.0Frontiers in Cardiovascular Medicine

A screening method for aortic valve disease based on spectral analysis of a single-channel electrocardiogram using machine learning algorithms

  • Natalia Kuznetsova1
  • Daniil Zelenov1
  • Aleksandra Bogdanova2
  • Ines Dhif2
  • Ekaterina Shobonova3
  • Anna Alekseeva2
  • Dinara Mesitskaya2
  • Abram Syrkin2
  • Roman Komarov4
  • Philippe Kopylov1,2
  • Petr Chomakhidze1,2
4 affiliations
  1. 1Institute of Personalized Cardiology of Biomedical Science and Technology Park, Sechenov First Moscow State Medical University (Sechenov University)
  2. 2Department of Cardiology, Functional and Ultrasound Diagnostics of N.V. Sklifosovsky Institute for Clinical Medicine, Sechenov First Moscow State Medical University (Sechenov University)
  3. 3Russian Medical Academy of Continuous Professional Education
  4. 4Department of Cardiovascular Surgery and Invasive Cardiology, Sechenov First Moscow State Medical University (Sechenov University)
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Abstract

Background: Given the high prevalence of aortic heart disease and the importance of early diagnosis, there is a need to develop a simple screening method to identify significant aortic heart disease based on available biosignals, one of which is a single-channel electrocardiogram (ECG).

Aim: We aimed to develop a machine learning algorithm for analyzing single-channel electrocardiograms to identify patients with moderate to severe aortic stenosis and regurgitation, as well as significant aortic valve disease.

Methods: Patients ( n = 2,503) underwent echocardiographic examination and single-channel I-lead ECG recording using a portable electrocardiograph. Moderate to severe aortic stenosis on echocardiography was present in 62 (2.4%) patients, and moderate to severe aortic regurgitation was present in 157 (6.3%). Single-channel ECG analysis was performed using advanced signal processing and machine learning techniques.

Results: Both models were evaluated under an identical 10-fold group cross-validation framework. For detecting moderate or severe aortic stenosis, diagnostic accuracy was 0.650 (Se 57.9%, Sp 65.0%) for Balanced Random Forest and 0.662 (Se 46.4%, Sp 66.5%) for XGBoost. For moderate or severe aortic regurgitation, accuracy was 0.726 (Se 60.5%, Sp 73.3%) for Balanced Random Forest and 0.712 (Se 61.7%, Sp 71.9%) for XGBoost. For significant aortic valve disease, accuracy was 0.724 (Se 61.1%, Sp 73.4%) for Balanced Random Forest and 0.691 (Se 67.8%, Sp 69.2%) for XGBoost.

Conclusions: The results indicate good diagnostic accuracy and potential applicability for screening for significant aortic heart disease using single-channel ECG parameters analyzed with modern signal processing and machine learning techniques.

Clinical Trial Registration : https://clinicaltrial.gov/ , identifier NCT07099417.

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Cite

BibTeXRIS
BibTeX
@article{kuznetsova2026screening,
  title = {{A screening method for aortic valve disease based on spectral analysis of a single-channel electrocardiogram using machine learning algorithms}},
  author = {Natalia Kuznetsova and Daniil Zelenov and Aleksandra Bogdanova and Ines Dhif and Ekaterina Shobonova and Anna Alekseeva and Dinara Mesitskaya and Abram Syrkin and Roman Komarov and Philippe Kopylov and Petr Chomakhidze},
  journal = {Frontiers in Cardiovascular Medicine},
  year = {2026},
  month = oct,
  doi = {10.3389/fcvm.2026.1969401},
  url = {https://www.frontiersin.org/articles/10.3389/fcvm.2026.1969401/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - A screening method for aortic valve disease based on spectral analysis of a single-channel electrocardiogram using machine learning algorithms
AU  - Natalia Kuznetsova
AU  - Daniil Zelenov
AU  - Aleksandra Bogdanova
AU  - Ines Dhif
AU  - Ekaterina Shobonova
AU  - Anna Alekseeva
AU  - Dinara Mesitskaya
AU  - Abram Syrkin
AU  - Roman Komarov
AU  - Philippe Kopylov
AU  - Petr Chomakhidze
PY  - 2026
DA  - 2026/10/08
JO  - Frontiers in Cardiovascular Medicine
DO  - 10.3389/fcvm.2026.1969401
UR  - https://www.frontiersin.org/articles/10.3389/fcvm.2026.1969401/full
AB  - Background: Given the high prevalence of aortic heart disease and the importance of early diagnosis, there is a need to develop a simple screening method to identify significant aortic heart disease based on available biosignals, one of which is a single-channel electrocardiogram (ECG).  Aim: We aimed to develop a machine learning algorithm for analyzing single-channel electrocardiograms to identify patients with moderate to severe aortic stenosis and regurgitation, as well as significant aortic valve disease.  Methods: Patients ( n = 2,503) underwent echocardiographic examination and single-channel I-lead ECG recording using a portable electrocardiograph. Moderate to severe aortic stenosis on echocardiography was present in 62 (2.4%) patients, and moderate to severe aortic regurgitation was present in 157 (6.3%). Single-channel ECG analysis was performed using advanced signal processing and machine learning techniques.  Results: Both models were evaluated under an identical 10-fold group cross-validation framework. For detecting moderate or severe aortic stenosis, diagnostic accuracy was 0.650 (Se 57.9%, Sp 65.0%) for Balanced Random Forest and 0.662 (Se 46.4%, Sp 66.5%) for XGBoost. For moderate or severe aortic regurgitation, accuracy was 0.726 (Se 60.5%, Sp 73.3%) for Balanced Random Forest and 0.712 (Se 61.7%, Sp 71.9%) for XGBoost. For significant aortic valve disease, accuracy was 0.724 (Se 61.1%, Sp 73.4%) for Balanced Random Forest and 0.691 (Se 67.8%, Sp 69.2%) for XGBoost.  Conclusions: The results indicate good diagnostic accuracy and potential applicability for screening for significant aortic heart disease using single-channel ECG parameters analyzed with modern signal processing and machine learning techniques.  Clinical Trial Registration : https://clinicaltrial.gov/ , identifier NCT07099417.
SN  - 2297-055X
ER  -