Screening for Cardiac and Cardiac-associated Pathology Using Single-channel Electrocardiogram

NCT07396792 | Not Yet Recruiting

• 1 other identifier: 7747-211021
• Study Type: observational
• Target: 4,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

It is a prospective, controlled, single-center, observational, non-randomized study. The study is planned to include at least 4000 patients 18 years old and older in the training sample and 1000 patients over 18 years old in the test sample (the total number of patients is at least 5000 people). Patients will be included in the study if they have undergone a full examination (laboratory, clinical and instrumental), allowing for the verification or exclusion of cardiac and cardiac-associated pathology in accordance with current recommendations. During the course of the study, the authors of the work do not interfere with the above-mentioned scope of the examination, which is carried out on patients in accordance with clinical guidelines. All patients included in the study will undergo ECG recording in standard lead I for 1 minute twice, followed by spectral analysis of the obtained data, which will be stored at the remote monitoring center of Sechenov University without being linked to the personal data of patients. A spectral analysis of the electrocardiogram will be performed using a continuous wavelet transform. The result of this study will be the identification of ECG parameters that will correlate with cardiac and cardiac-associated pathology

Conditions

Valvular Heart Diseases; Heart Failure; Diabetes Mellitus; Chronic Kidney Disease (Stage 3-4); Metabolic Syndrome; Coronary Artery Disease; Arterial Hypertension; Heart Rhythm Disorders

Keywords

Screening; Electrocardiogramm; single-channel electrocardiogram; machine learning models; Coronary artery disease; diabetes mellitus; metabolic syndrome; anemia; hypercholesterolemia; arterial hypertension

Outcome Measures

Primary Outcomes (4)

• Parameters of single-channel ECG that significantly correlate with the presence of various cardiac and cardiac-associated pathologies

  comparison of the presence of cardiac and cardiac-associated pathology by the results of full examination (laboratory, clinical and instrumental) with the results of the presence of valvular heart defects obtained using the mathematical model of a single-channel ECG monitor

  through study completion, an average of 2 years

• Determination of sensitivity of various cardiac and cardiac-associated pathologies of multivariate models for analyzing single-channel electrocardiogram data

  comparison of the presence of cardiac and cardiac-associated pathology by the results of full examination (laboratory, clinical and instrumental) with the results of the presence of valvular heart defects obtained using the mathematical model of a single-channel ECG monitor

  through study completion, an average of 2 years

• Determination of specificity of various cardiac and cardiac-associated pathologies of multivariate models for analyzing single-channel electrocardiogram data

  comparison of the presence of cardiac and cardiac-associated pathology by the results of full examination (laboratory, clinical and instrumental) with the results of the presence of valvular heart defects obtained using the mathematical model of a single-channel ECG monitor

  through study completion, an average of 2 years

• Determination of diagnostic accuracy of various cardiac and cardiac-associated pathologies of multivariate models for analyzing single-channel electrocardiogram data

  comparison of the presence of cardiac and cardiac-associated pathology by the results of full examination (laboratory, clinical and instrumental) with the results of the presence of valvular heart defects obtained using the mathematical model of a single-channel ECG monitor

  through study completion, an average of 2 years

Study Arms (2)

Training sample

2500 of patients 18 years old and older with and without cardiac and cardiac-associated pathology confirmed by the results of full examination (laboratory, clinical and instrumental) and by results of the spectral analysis of electrocardiogram (the parameters listed below will be calculated as the median of the tact-cycle: TpTe, VAT, QTc, QT / TQ, QRS\_E, T\_E, TP\_E, BETA, BETA\_S, BAD\_T, QRS\_D1\_ons, QRS\_D1\_offs, QRS\_D2, QRS\_Ei (i = 1,2,3,4), T\_Ei (i= 1,2,3,4), HFQRS, QRSw, RA, SA, TA and others).

Test sample

1500 of patients 18 years old and older with and without cardiac and cardiac-associated pathology confirmed by the results of full examination (laboratory, clinical and instrumental) and by results of the spectral analysis of electrocardiogram (the parameters listed below will be calculated as the median of the tact-cycle: TpTe, VAT, QTc, QT / TQ, QRS\_E, T\_E, TP\_E, BETA, BETA\_S, BAD\_T, QRS\_D1\_ons, QRS\_D1\_offs, QRS\_D2, QRS\_Ei (i = 1,2,3,4), T\_Ei (i= 1,2,3,4), HFQRS, QRSw, RA, SA, TA and others).

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All patients with or without cardiac and cardiac-associated pathologies over 18 years old

You may qualify if:

• The presence of written informed consent of the patient to participate in the study
• Age 18 years old and older
• Patients with an implanted permanent pacemaker;
• ECG changes that prevent spectral analysis;
• Conditions that may impair the quality of the ECG recording (Parkinson's disease, essential tremor, etc.);
• Conditions that make ECG recording in lead I impossible (congenital anomalies of the upper limbs, traumatic amputation of the upper limbs).
• Lack of written informed consent from the patient to participate in the study.

You may not qualify if:

• Poor quality of the ECG recording on a single-channel ECG monitor
• Insufficient examination data to verify or exclude cardiac or cardiac-associated pathology;
• Patient's unwillingness to continue participating in the study for any reason.

Sponsors & Collaborators

• I.M. Sechenov First Moscow State Medical University: lead

Related Publications (6)

• A screening method for predicting left ventricular dysfunction based on spectral analysis of a single-channel electrocardiogram using machine learning algorithms / N. Kuznetsova, Zh. Sagirova, A. Suvorov [et al.] // Biomedical Signal Processing and Control. - 2023. - Vol. 86. - P. 105219. - DOI 10.1016/j.bspc.2023.105219. - EDN APQSQF.

  BACKGROUND

• Complex automated remote system for assessing hemodynamic parameters when analyzing the native signal of a single-channel ECG and pulse wave using machine learning techniques / N. O. Kuznetsova, Zh. N. Sagirova, E. A. Sultygova [et al.] // Russian Journal of Cardiology. - 2023. - T. 28, No. S7. - pp. 41-42. - EDN LZGDKG.

  BACKGROUND

• A Systematic Review on the Effectiveness of Machine Learning in the Detection of Atrial Fibrillation / A. L. Wuraola, B. Al-Dwa, D. Shchekochikhin [et al.] // Current Cardiology Reviews. - 2024. - Vol. 20. - DOI 10.2174/011573403x293703240715104503. - EDN XQZPAY.

  BACKGROUND

• A single-lead ECG based cardiotoxicity detection in patients on polychemotherapy / D. F. Mesitskaya, Z. Z. A. Fashafsha, M. G. Poltavskaya [et al.] // IJC Heart and Vasculature. - 2024. - Vol. 50. - P. 101336. - DOI 10.1016/j.ijcha.2024.101336. - EDN XMKKZY.

  BACKGROUND

• Kuznetsova N.O., Alekseeva A.M., Mamedzade F.E., Sedov V.P., Kopylov F.Yu., Syrkin A.L., Chomakhidze P.Sh. Screening for heart defects when analyzing an electrocardiogram using machine learning methods (literature review) // Bulletin of new medical technologies. Electronic edition. 2025. No. 1. Publication 1-6. DOI: 10.24412/2075-4094-2025-1-1-6. EDN KNFSNS

  BACKGROUND

• Kuznetsova N.O., Nartova A.A., Kurbanalieva N.K., Adueva D.Sh., Chursina E.Yu., Zhvania R.E., Ustinova D.I., Kostikova A.S., Kazakova M.V., Tarnaeva L.A., Chomakhidze P.Sh., Kopylov F.Yu. Results of screening for heart rhythm disturbances using a single-channel electrocardiogram without the participation of medical personnel // Bulletin of new medical technologies. 2025. No. 1. P. 56-60. DOI: 10.24412/1609-2163-2025-1-56-60. EDN YWJLFH.

  BACKGROUND

Related Links

• A single-channel ECG using machine learning techniques presentation

MeSH Terms

Conditions

Hypertension; Arrhythmias, Cardiac; Heart Valve Diseases; Heart Failure; Diabetes Mellitus; Renal Insufficiency, Chronic; Metabolic Syndrome; Coronary Artery Disease; Anemia; Hypercholesterolemia

Condition Hierarchy (Ancestors)

Vascular Diseases; Cardiovascular Diseases; Heart Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Chronic Disease; Disease Attributes; Insulin Resistance; Hyperinsulinism; Coronary Disease; Myocardial Ischemia; Arteriosclerosis; Arterial Occlusive Diseases; Hematologic Diseases; Hemic and Lymphatic Diseases; Hyperlipidemias; Dyslipidemias; Lipid Metabolism Disorders

Central Study Contacts

Natalia Kuznetsova, Dr.

CONTACT

+79164778724; kuznetsova_n_o@staff.sechenov.ru

Petr Chomakhidze, Professor

CONTACT

+79166740369; chomakhidze_p_sh@staff.sechenov.ru

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 2, 2026
• First Posted: February 9, 2026
• Study Start: April 1, 2026
• Primary Completion (Estimated): March 1, 2028
• Study Completion (Estimated): May 13, 2028
• Last Updated: April 16, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share