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Kuznetsova N.O.

Sechenov First Moscow State Medical University

Sagirova Zh.N.

Sechenov First Moscow State Medical University

Gubina A.Yu.

Sechenov First Moscow State Medical University

Zakharov I.P.

Sechenov First Moscow State Medical University

Fashafsha Z-Z.A.

Sechenov First Moscow State Medical University

Syrkina E.A.

The first Moscow State Medical University named after I.M. Sechenov (Sechenov University)

Chomakhidze P.Sh.

Sechenov First Moscow State Medical University

Kopylov F.Yu.

Sechenov First Moscow State Medical University

A new machine-learning algorithm for assessing the left ventricular diastolic dysfunction by a single-channel ECG monitor

Authors:

Kuznetsova N.O., Sagirova Zh.N., Gubina A.Yu., Zakharov I.P., Fashafsha Z-Z.A., Syrkina E.A., Chomakhidze P.Sh., Kopylov F.Yu.

More about the authors

Journal: Russian Journal of Cardiology and Cardiovascular Surgery. 2022;15(5): 496‑500

DOI: 10.17116/kardio202215051496

Read: 8348 times

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Table of contents

A NEW MACHINE-LEARNING ALGORITHM FOR ASSESSING THE LEFT VENTRICULAR DIASTOLIC DYSFUNCTION BY A SINGLE-CHANNEL ECG MONITOR
A NEW MACHINE-LEARNING ALGORITHM FOR ASSESSING THE LEFT VENTRICULAR DIASTOLIC DYSFUNCTION BY A SINGLE-CHANNEL ECG MONITOR

To cite this article:

Kuznetsova NO, Sagirova ZhN, Gubina AYu, et al. . A new machine-learning algorithm for assessing the left ventricular diastolic dysfunction by a single-channel ECG monitor. Russian Journal of Cardiology and Cardiovascular Surgery. 2022;15(5):496‑500. (In Russ.)
https://doi.org/10.17116/kardio202215051496

 
ЭндоКард
Подписка 2026 Кардиология и сердечно-сосудистая хирургия (Russian Journal of Cardiology and Cardiovascular Surgery)
 
МСФ на ЯМ
Использование инфографики авторами научных работ
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Abstract:

OBJECTIVE

Analysis of a new algorithm based on machine learning elements to determine significant (grade 2 or 3) left ventricular diastolic dysfunction using a single-channel electrocardiography (ECG).

MATERIAL AND METHODS

The study prospectively included 200 patients over 18 years old. Each one underwent transthoracic echocardiography with analysis of left ventricular diastolic dysfunction by high-speed and tissue Doppler ultrasound. ECG was recorded using a single-channel monitor. After spectral analysis of ECG, we calculated the time, amplitude and frequency parameters. The modernized algorithm for determining significant left ventricular diastolic dysfunction was evaluated considering the results obtained on the training sample of patients.

RESULTS

Mean age of patients was 53.2±18.1 years. Moderate-to-severe left ventricular diastolic dysfunction was found in 31% of patients. Sensitivity and specificity of the algorithm in determining moderate left ventricular diastolic dysfunction were 98.3% and 97.8%, severe dysfunction — 87.5% and 99.5%, moderate-to-severe diastolic dysfunction — 95.6% and 97.7%, respectively. Diagnostic accuracy was 97.5%, 99.0% and 96.5%, respectively.

CONCLUSION

The achieved results substantiate fundamental possibility of remote screening of left ventricular diastolic dysfunction based on ECG parameters.

Keywords:

diastolic function
portable ECG monitor
electrocardiogram
echocardiography
heart failure
machine learning

Authors:

Kuznetsova N.O.

Sechenov First Moscow State Medical University

Sagirova Zh.N.

Sechenov First Moscow State Medical University

Gubina A.Yu.

Sechenov First Moscow State Medical University

Zakharov I.P.

Sechenov First Moscow State Medical University

Fashafsha Z-Z.A.

Sechenov First Moscow State Medical University

Syrkina E.A.

The first Moscow State Medical University named after I.M. Sechenov (Sechenov University)

Chomakhidze P.Sh.

Sechenov First Moscow State Medical University

Kopylov F.Yu.

Sechenov First Moscow State Medical University

Received:

10.03.2022

Accepted:

22.04.2022

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